Podcast episode

How Ben Bernanke can bring Superforecasting to the Bank of England w/ Nicholas Gruen – EP207

Host Gene Tunny chats with Dr. Nicholas Gruen about economic forecasting and what recommendations former US Fed Chair Ben Bernanke could make in his current review of forecasting at the Bank of England. Nicholas, the CEO of Lateral Economics, discusses the shortcomings of economic forecasting and shares his insights into how it can be improved. The conversation was inspired by Nicholas’s article in the Financial Times titled “How to Improve Economic Forecasting.” The episode is split into two parts, with the second part focusing on the feedback Nicholas received on his article. 

Please get in touch with any questions, comments and suggestions by emailing us at or sending a voice message via

You can listen to the episode via the embedded player below or via podcasting apps including Google PodcastsApple Podcasts and Spotify.

About this episode’s guest: Nicholas Gruen

Described by the Financial Times’ Chief Economic Writer Martin Wolf as “a brilliant man who deserves to be better known”, and by former Finance Minister Lindsay Tanner as “Australia’s foremost public intellectual”, Dr Nicholas Gruen is a policy economist, entrepreneur and commentator on our economy, society and innovation.

What’s covered in EP207

  • [00:02:13] Ben Bernanke’s review of economic forecasting at the Bank of England.
  • [00:05:23] Hedgehogs and foxes. 
  • [00:09:36] Long-term issues with economic forecasting. 
  • [00:13:18] Improving economic forecasting techniques. 
  • [00:19:29] Forecasting accuracy. 
  • [00:24:30] Open sourcing economic forecasting. 
  • [00:26:29] Developing a forecasting market. 
  • [00:34:21] Tetlockian forecasting tournaments. 
  • [00:48:37] Wind in the Willows author Kenneth Grahame at the Bank of England.

Links relevant to the conversation

Video versions of the conversations featured in this episode on Nicholas’s YouTube channel:

Information on the Bank of England’s Citizens’ Panels/Forums:

Mandarin column in which Nicholas declares former Bank of England Chief Economist Andy Haldane was “my favourite public servant in all the world”:

Transcript: How Ben Bernanke can bring Superforecasting to the Bank of England w/ Nicholas Gruen – EP207

N.B. This is a lightly edited version of a transcript originally created using the AI application It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.

Gene Tunny  00:06

Welcome to the Economics Explored podcast, a frank and fearless exploration of important economic issues. I’m your host Gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode, please check out the show notes for relevant information. Now on to the show. Hello, thanks for tuning in to the show. In this episode, I chat with Dr. Nicholas Gruen about economic forecasting. Nicholas is CEO of lateral economics. He’s been described by the Financial Times as Chief Economic writer Martin Wolf as a brilliant man who deserves to be better known, and by former Australian finance minister Lindsay Tanner, as Australia’s foremost public intellectual. This conversation was inspired by an article that Nicholas had published in late August in the Financial Times How to improve economic forecasting. The FTS one line summary of the article was myopia and groupthink mean this science is not as evolved as it could be. This episode is in two parts. The first was recorded prior to Nicholas’s article coming out, and in the second part, we reconvened to go over some of the feedback that he received on the article. The video version of the first part is available on Nicholas’s YouTube channel. I’ll include links in the show notes to the YouTube channel, and to material mentioned in the episode. Okay, let’s get into the conversation. I hope you enjoy my conversation with Nicholas Gruen. Nicolas, good to be catching up with you again on economic forecasting. Likewise, so Nicholas, last month, the Bank of England announced that Ben Bernanke, so the former chair of the Federal Reserve in the US, he is to lead a review into forecasting at the Bank of England. So the the court of the Bank of England’s pleased to announce Dr. Ben Bernanke has agreed to lead a review of the bank’s forecasting and related processes during times of significant uncertainty, or we’ve had plenty of those. And he’ll be supported by the bank’s Independent Evaluation Office. Now, Nicholas, you’ve had some thoughts on what Ben Bernanke could offer to the Bank of England regarding forecasting, haven’t you? So would you be able to give us an overview of what those thoughts are, please?

Nicholas Gruen  02:44

Sure. So their thoughts? I’m not terribly hopeful. And that’s an amazing thing to say about Ben Bernanke. I regard Ben Bernanke happens to have a Nobel Prize on his shelf. Ah, you’ll notice that I don’t. And I also think he’s a great guy. You know, he’s a very sensible, practical economist with a lot of understanding of empirical economics and happened to be a one of the world’s experts on the Great Depression at the time when boy, did we need an expert on the Great Depression in the Fed. So that’s all great. I fear that Ben Bernanke, like a really scandalously large proportion of economists are so caught up in their own discipline that they haven’t noticed what has happened in adjacent areas. And this is a little bit like, what’s been going on is something quite like what Daniel Kahneman and Danny and a must for Seversky. If I got that, right, we’re cooking up with behavioural economics. It’s happened a little since then. But a guy that many people will have heard of Philip Tetlock, he got tenure in about 1982 Or three. And he decided that he would now engage in a long term project that he always wanted to engage in, but you can’t if you don’t have tenure, because you get sacked before you could get a publication if there’s so long range thing. And what he wanted to measure was do geo political experts. You can call Tom Friedman. He certainly poses as a geopolitical expert, The New York Times columnist, but also intelligence analysts, academics, international relations academics. If you ask them to forecast events, do they add value? Do they the fact that it’s quite clear they know more than your average bear? Does that translate into actually having actionable better capacity to say what’s going to happen? And the answer was on average and barely. And then he divided that up into experts that did add something. And they didn’t add that much, and experts that actually were worse than ranked, or worse than a naive prediction, and he divided them up into hedgehogs and foxes, hedgehogs no one big thing. And that means that their forecasts are worse than yours or mine, Gene, because we’re just trying to doing our best, whereas the hedgehog will have one big thing, you’ll be anti communist or pro communist, or this or that. And that banks, their forecasts worse than a fox, I think of someone like the economist, John Maynard Keynes, or Paul Krugman, as a fox, someone who knows many things and is trying to balance all those things, and to work out how much this matters and how much that matters, and how much do I know and so on. Now, that’s pretty striking. But it doesn’t tell us exactly what to do. But there is one thing that the study showed us. And it didn’t, we didn’t actually need the study to show us. But it gives us a very concrete illustration of a problem, which is, and this goes on in economics, which is that if you don’t issue your forecasts in a form, that can be back tested, that we can revisit and say did was that a good forecast or a bad forecast? And how did it compare with your peers? You’re basically, you know, it’s a bit like fortune telling. And to do that. What Tetlock did was he forced analysts to say precisely what they were predicting would happen, or in fact, he would specify something like, my Mikhail Gorbachev will continue to be the secretary of the general Committee of the Communist Party, whatever it was called, then, by the end of 1988. What are the chances and then you would have to say, I think the chances are 88% or 23%? Not probably, which means somewhere between 51% and 100%. And not unlikely. And not you can’t rule this out the sort of things you read in a newspaper column. Now we need to do that with economic forecasts.

Gene Tunny  07:31

Yeah, yeah. So just for background, so Philip Tetlock is a Canadian American Political science professor at University of Pennsylvania. And yeah, he wrote that book, super forecasting, or super forecasters. I’m

Nicholas Gruen  07:46

just gonna get on to the talk about that’s the book for the people who can watch not the people who are listening. I’m holding it up to the microphone. Thank you.

Gene Tunny  07:54

Yeah, absolutely. And so he was looking at, you mentioned geopolitical forecasts. But we’re interested in economic forecasts. Now, we know and I guess the general public knows that economic forecasts have been had. there been some notable failures and Amin in Australia that they go way back. I mean, always remember the I mean, I guess I was young at the time was in high school with the Treasury. And was forecasting the soft landing during was it the 9091 recession? Yeah. And it was the worst recession since

Nicholas Gruen  08:30

then, you know, the problems. Yeah. And there are other notable examples. More recently, we’ve been expecting wages to pick up and abroad for about, well over a decade, it just goes on. And, and to their credit, the Treasury and the reserve, published these graphs, I might see if I can put one in the show notes or on screen, or the editor can put one on screen, where you see wage growth gradually trending down with every year, the forecast is to come back to the long term at what was the long term trend average, it’s no longer the long term trend average.

Gene Tunny  09:08

Yeah. And there are some charts like that in the latest intergenerational report that the Treasury has put out, Jim Chalmers launched today, which showed just how bad those long run projections have been. So you know, it’s a it’s a problem, both in the short term and the long term. With economics. Yes. So I suppose yeah, be good to sort of to diagnose I mean, what are the what’s the actual issue and the problem is that the the economy is fundamentally difficult to forecast but

Nicholas Gruen  09:41

no, but I mean, we’re not even trying so to try, we would nail economic forecast down to something that can be properly back tested so I we have a forecast. You may know what the Treasury’s forecast is for wages or growth. Next year, I don’t you just give us a number. Even if you don’t know, the sort of thing you think it should be around what for wages, wages or for growth it all for economic growth,

Gene Tunny  10:12

it’s probably around 2%, or two and a half percent or so and a

Nicholas Gruen  10:16

half, okay, two and a half percent. So first problem is that if the forecast is 2.5%, and it comes in at 2.62%, is that a success? Or is that a failure? So because 2.5%, we call it a point forecast, and the chances that it comes in exactly at that number are infinitesimally small, I just have to add decimal points. And eventually, it won’t won’t be 2.500000. It will be it will fall on one side or the other of 2.5. So we need if, if we’re going to back test, a forecast, we need a forecast that we can declare a success or a failure. And the next thing we need is we need the forecast to tell us how confident they are that it’s got that that event will happen. And that happens to be exactly how weather forecasters forecast. They give us an event it will rain which I’m sure has a media or logical definition of you know more than this amount of precipitation in 24 hours or in in an hour. It will rain and it will rain with this degree of probability. Now what’s beautiful about that is Daniel Kahneman says that there are places where he said this I think he’s a no doubt he’s been more circumspect in other places, but I’ve heard him say, all professions are overconfident? Well, weather forecasters are not overconfident. Because the confidence with which they express themselves becomes part of the metric by which we judge them. And so they make a point of being exactly the right degree of confidence. So I think of weather forecasting as one of the few Socratic areas of domain expertise, because it knows what it knows. And it knows the limits of that knowledge. So that’s what we need to start to try to do with economists. And I think it was you who sent me this thing in the last six months where some of the techniques that Philip Tetlock has perfected has developed, have started to show dividends in economic forecasting. Now, one thing we haven’t explained yet is that that in that book, super forecasting, Philip Tetlock took the ideas with which he demonstrated how little value was added, and how some types of people added more value than others. And he asked himself the question, could we identify the very best to the people who consistently add the most value? Can we understand more about how they do that? Could we get them together and get them to help each other? And the answer is that using these simple and common sensical techniques, you can actually start to get a lot better. Certainly, geopolitical forecasting. And now there’s some evidence that we may be able to get better at economic forecasting.

Gene Tunny  13:32

Right? So with weather forecasting, so in your you’ve been working on a, an article on this, and you’ve identified that weather forecasts are much better than they were 30 years ago. Yeah. Now, that’s because of an infant. My understanding is that’s because of the ingestion of so much new data. And I mean, we’ve seen with that integrated marine observing system, for example, the imass organisation that we’ve done some work for that there’s a whole bunch of data that comes from the ocean, and that helps with weather forecasts. They’ve got huge numerical models and their physical processes involved that they can actually model with economics is a lot, a lot more challenging. So yeah, weather I guess, it is embarrassing. How economic forecasting hasn’t hasn’t improved. And I suppose that does suggest we need to, we need to adopt a different approach is not necessarily going to be we’re not necessarily going to improve our forecast by building more complicated models or bringing in more data. Perhaps we do need to adopt a new approach along the lines of this super forecasting methodology. And you mentioned, yep, there was that evidence about how they’re forecasting the Fed rate decisions much more accurately than others their super forecasting approach. So I guess you are starting to unpack it. What do you see as the main elements of This super forecasting approach, Nicolas.

Nicholas Gruen  15:02

So one of the things that that I think is quite interesting and useful is that like Daniel Kahneman, who was the last person who really, I won’t say revolutionise because it’s not true, but he really he started a whole new way of thinking about things within economics and managed to get himself a Nobel Prize for his trouble. And he’s a psychologist. And so it was Philip Tetlock and Philip Tetlock is drawing our attention to something that’s incredibly important. But because it lies outside of economics, economists just ignore it. And what he’s saying is that if you want to be a good forecaster, you must forecast in a particular way, I’ll say you must have a certain kind of psychology. Now. In fact, in philosophy, there is a term for this, I don’t much fancy it, but the term is Virtue Epistemology. That is if you want to, if you want to be good at knowing if you want to be a good scientist, if you want to be good at mastering a domain and being useful to other people by not being overconfident. By actually knowing how much you know and making it count. Then you have to exhibit virtues, you have to exhibit actual virtues, you have to have the courage of your convictions, you have to have the humility to know when other people or events might be, make it time for you to revise your opinion. Is this reminding you of lots of economists? You’ve talked to Jim? And perhaps not so so the list that I put in this op ed that I’ve written for the Financial Times and may have been published by the time you people get to listen to this conversation? What qualities does he see in Super forecasters, as well as mastering the mesh necessary formal techniques, which we economists are very strong on. They’re open minded, careful, curious, and so critical. away like Socrates, of how little they know, they’re constantly seeking to learn from unfolding events, and from respected colleagues. So that’s how you forecast I would argue, that is how you do anything that is expert. And there’s a really important thing here. Because even if we can’t improve forecasting much, and one thing I do want to throw in, parenthetically on that question, is that when economists make for when a central bank or a treasury makes forecasts, this is a forecast of how certain economic aggregates are going to move that they plan to try to manipulate on on the way through. So it’s a very, it’s a very different kind of forecast, the, the forecasters of the weather don’t say, well, it’s going to be a 30% chance of rain on Tuesday, and we’re going to be trying to make it a 30% chance of rain on or we’re going to be making trying to make it a 20% chance of rain. So so it’s it’s a lot more complicated. But one of the things that are super forecaster might do person have that kind of temperament might do is they might say, well, our point forecasts much used to us. And the answer is I don’t think they I mean, quite apart from the fact that we can’t back test them. I think the most important thing I want to know as a business person doing planning of for something or as an employee, and I’m thinking should I buy a house or buy an investment property or whatever? Seen, I think the most important metric I want the most important thing I want forecast is what is the chance of a recession in the next six months or 12 months or two years? So I think we should be trying to forecast a lot more along those lines. Now there’s a problem and that is that well, firstly, let’s talk about the problem of forecasting at the moment. Because economists forecasts are not probabilistic because we don’t test an economist according to they don’t issue those forecasts like there is a 40% chance of recession or whatever. Almost all the time, even when a recession is more likely than most other times, it’s still unlikely that there will be a recession. And so now what we’ve got is we’ve got all the forecasters in the same situation as 40 tippers, which is I might want to say that the backmarker What do you call it the last of the non favourite in a horse race or a football am, I might want to say that I think the favourite has got an unusually large chance of losing. But I still think it’s more than 50%. So if people are just saying, How many times did you tip the right answer, then we’re not going hunting for who knows that this is the who’s got some extra information, which is that for some reason or other some some particular players not inform or something rather, that there’s a lower chance of the favourite winning than usual, no one has an incentive to do that if we’re going to give a prize out to the person at the end of the year, who tipped more winners than anyone else. And that’s real. And that’s what happens in economics. So of the last 18 recessions, economists pick, tipped about one or two of them. And if you’re competing with other economists, with how often you got it right or wrong, that’s actually quite a rational strategy. So what we need is, we need to find a way for economists to put their hand up and say, I think the chance of recession have gone from, let’s say, 10% per year or something like that, maybe a bit more, I think to the next year, it’s 35%, or whatever, and then at least you get an effective, you know, a number.

Gene Tunny  21:24

Right. So is this what Ben Bernanke should be recommending he should be recommending that the Bank of England provides percentage estimates of regarding its forecast, so how confident it is? I mean, to an extent it does that, I think, doesn’t it? It has Fein charts. It has fan

Nicholas Gruen  21:41

charts, it has fan charts. And I think, yeah, once you try to operationalize this in economics, you end up with a lot of fan charts. Now fan charts, we may or may be able to show those on the screen. And in the show notes, fan charts show you the point forecast through time, and then they say this, the 70% confidence interval is this fat. And the 90% confidence interval is this fat. In other words, if you want to know what were the the range within which we’re 90% Sure, that’s the range. Now the problem is that range isn’t helpful doing because the 90% range usually takes you from somewhat one of the most savage recessions you can possibly imagine through to boom conditions. So we do need to think about that. But what really, I think that there’s a few things here. One of the things is that we need to get, this is a good way to get different teams and different forecasters to compete with each other. It’s a good way to compare forecasters, so that you’re constantly getting feedback on who’s good and who’s not. The other thing that I think it does, well, it also enables us to surface you can have a different series, which is not in any central bank or Treasury that I know of, which is the chances of recession, you can have that series and you can have people trying to forecast that. Now there’s a further problem. And the problem is that we get feedback on what growth was every time we forecast it, because we can’t we get a growth number. We don’t get feedback on what the question was there a session will accept that the answer is no. It only varies once a decade or so. That’s a really big problem. Because if you want to ask who’s the best person at forecasting recessions, then you’ve got to wait 20 or 30 years to even start to short sort the sheep from the goats. Yeah. So Philip Tetlock has actually been working on this on a problem. It’s not in economics. It’s in his his the area that he manages to get the most funding from, which is in intelligence organisations and so on. But what he’s trying to ask is, can we leverage the credibility of forecasters of things we do get a lot of feedback from for these other areas where we get less feedback? And I think the answer is yes, we should be able to do that. And we must be able to do that in some areas, and maybe not in others. And then we don’t know about this area, but that’s the sort of thing that we should

Gene Tunny  24:28

be exploring. Okay, so for economics, so just to summarise, are you arguing for open sourcing for coal, that’s

Nicholas Gruen  24:36

a separate thing. That was what I was going to get to, which is that so what I want to see is that this is one area that given that we’ve outsourced all kinds of things in government that we shouldn’t have outsourced. Maybe we could outsource some of the things we should and we this is the sort of thing that we can outsource on I don’t even mean outsource we can’t what we should do the best Bank of England, the Reserve Bank of Australia can get with the programme and the programme is the smartest person is always outside the room. And in some areas, you can, in some sense, bring them in. And in other areas you can’t. But in the area of forecasting, you can and you can hold a Tetlock like forecasting competition, you can say, we’re trying to get forecast for this, and this and this and chances of recession in six months, one year and two years, and then everyone can participate. Now, the world or certainly the markets and the people in the different national countries, they want to know, what’s the reserve, what’s the central bank forecast, so that central bank has its own, I think that central bank should have its own teams, team or teams in these forecasts. But they should separate out the teams from the bank itself, and the bank should observe the forecast should observe the forecasting competition. And from that forecasting competition, say what it thinks is its best forecasts and those become signed with the imprimatur of the central bank. They might be produced by the central bank team, or one of them, they might be produced by somebody completely outside, they might be produced by some kind of hybrid. And all of this is visible to everyone. And so we’re starting to develop a market in which we can start to see who’s really good at this. And some people are going to surprise us on both the upside and the downside, by the way. So that’s what I’m suggesting.

Gene Tunny  26:46

Yeah, I mean, what, what I’d like to understand is, to what extent will it be teams, interdisciplinary teams of economists, and then some other non economists, may be busy people who are expert in business or maybe not even expert in business people who are just good forecasters. And when I was chatting with Warren hatch from good judgement, this is a organisation he set up with Philip Tetlock, he was telling me that it’s people with good pattern recognition skills, and then be in any discipline and people who are cognitively flexible, or they’re there. As you were saying before they actually they’re not caught up with their particular theory. They’re actually yeah, they’re evaluating everything. Yeah, that’s right. That’s

Nicholas Gruen  27:33

right. So the answer is, we don’t have to know the answer to that. But we Yes, you would expect that the teams that are going to perform best will be hybrid teams will have economists Well, technically excellent economists in them. They’ll have people who look at other kinds of things. And there will certainly be some surprises. And some people who’ve always had a fascination with, you know, certain kinds of things which turn out to be relevant to how you forecast. So that’s where I would expect it to, to end up. But maybe it’ll just be economic experts. If they win the if they win the competitions. All this Tetlock stuff will have proven itself to be relevant for economics, but both common sense and the evidence suggests that that that’s not the way it will turn out. And there aren’t that many areas where at the centre of government, you can improve performance and improve. And through that improve economic performance someone. This is this is one of those billion dollar bills on the pavement that we find ourselves talking about from time to time, Gene,

Gene Tunny  28:46

absolutely. Yeah. And I misremembered. What Treasury’s forecast is 2023 24 GDP forecasts for Australia at one and a half percent. So not Oh, is there any

Nicholas Gruen  29:01

memorable number or perhaps it is memorable, but not in a good

Gene Tunny  29:04

way? Just so many numbers out there? Harada? Yeah, exactly. Exactly. I feel sorry for these politicians, they get put on the spot about these different numbers from toe to toe? Oh, absolutely. Yeah, absolutely. fully on board with that suggestion. At the very least it’d be a good trial, a good pilot. Exactly that out, see how it will works?

Nicholas Gruen  29:23

Well, I’ll just say one other thing, which is that this is again, what we’re talking about here is convening power, not executive power. So anyone can run this. The Business Council could run this. It’s not it won’t be cheap, but it’s not very expensive. Having worked at the Business Council, I can tell you, their budget easily would easily accommodate this. You could do it for a few $100,000. Anyone can do this. So it’s it’s kind of extraordinary and pretty outrageous that we’ve really known this, that there are benefits here. We can do this better. And it just gets ignored again. And again, it got ignored in the review of the RBA that we had here. It’s pretty terrible that we’re not looking around and trying to grab hold of things that are in the ether, that it’s starting to work, and that we can benefit from.

Gene Tunny  30:21

Yeah, I suppose there’s a public benefit to it. It’s not necessarily in the interest of the people in the Treasury or the Reserve Bank or the Bank of England or their ministers. I think that’s one of the the issues.

Nicholas Gruen  30:32

Yes, but economists are pretty impatient with policy makers who don’t do the right thing, but that the economists have to figure this out themselves. And I would, I would have thought that it’s Well, time for this to be standard economic advice, and it’s very, very left field and economic advice at this stage.

Gene Tunny  30:55

Okay, we’ll see how your Financial Times I bet is received?


Well, let’s see. Let’s see what Ben says. Very good, he might be giving you a call. Let’s hope.

Gene Tunny  31:09

Okay, we’ll take a short break here for a word from our sponsor.

Female speaker  31:15

If you need to crunch the numbers, then get in touch with Adept Economics. We offer you Frank and fearless economic analysis and advice. We can help you with funding submissions, cost benefit analysis, studies, and economic modelling of all sorts. Our head office is in Brisbane, Australia, but we work all over the world. You can get in touch via our website, We’d love to hear from you.

Gene Tunny  31:44

Now back to the show. So Nicholas, it’s been a few weeks now since your article was published in the Financial Times. So your article, how do we improve economic forecasting? And we chatted about that, in the previous conversation, the in the lead up to that coming out. So your ideas about how the Bank of England and other central banks or treasuries or finance ministers can improve economic forecasting? So it’s been a few weeks and says come out, you’ve had a bit of feedback. Yeah. How would you describe the reaction to your article?

Nicholas Gruen  32:22

I think it’s been the best reaction. I’ve published three pieces in the Financial Times of this kind, which is a sort of, hey, why don’t we do this? It’s reasonably out there kind of proposal and my judgement of the comments, and you’ve looked at them slightly more carefully than me that I looked at them, you know, in the first 24 or 48 hours, I thought they were more positive, and more constructive than most than is mostly the case in comment sections. It’s a pretty sad state of affairs. And nevertheless, the case that even in a really high quality newspaper, like the Financial Times, a lot of the people they’re not super ignorant, and, and just just totally dumb, but what they do is they sort of come on and they make a point. And the point is a perfectly okay, point, one of the points for instances, well, weather forecasting, which was full of praise for is different to economic forecasting, because the weather doesn’t decide to change its mind when it sees a forecast. And human beings do. It’s a very good point. It doesn’t completely obliterate all the points I was making. So if someone wants to come on and say that, that’s fine, I know that, but they’re not really participating in the spirit of things. Another person who wrote a letter to the Financial Times I think his name’s Tim Connington, or Contin, you might know his name. He said that really what mattered was having models that have proper allowance for monetary policy in them well, I’m not against having models that have the proper allowance for monetary policy in them, but it doesn’t really address the point. And then, and then there was some really quite good criticisms. The other thing was really good was that I was approached by a number of people, some of them well, one was a large corporate, which is doing Tetlock in forecasting tournaments internally. That was an interesting exercise. And I’ve been engaged with them. I’ve been they haven’t been paying me or anything, but I’ve been suggesting that they look further afield to the services of people like Warren hatch who you interviewed. On your podcast, he runs a thing called Well, it’s called good judgement. I don’t know whether it’s good judgement Inc. Or, anyway, it’s not the Philip Tetlock project which is run with inside University But it’s an offshoot of it, which is a commercial project. That was interesting. There was another economist, who was really quite pissed off, if I might say this, about the fact that forecasting prowess is not a very strong criterion of promotion inside government agencies that deal with economics include, including government agencies, in which forecasting is a very important matter. And he’s right. And I talked to him about Kaggle. And how Kaggle, the data science forecasting platform that I was involved in, when it started up, has changed the market to a substantial extent because people want data scientists who actually perform well. And you can see whether they performed well or not on Kaggle. And then another person who contacted me was actually from the Bank of England. Now, I’ve not had that experience in Australia, where someone from inside government you publish something. I mean, it wasn’t directly critical of the bank, I suppose you could say it was in a way. Anyway, he engaged me. And he said, Well, actually, we do, too, a little bit of what you’re suggesting. And it’s true, that the Bank of England, which is about my favourite Central Bank, I think they’ve done better than any other central bank in terms of their thinking. Not it turns out in terms of all the judgments about the about inflation, and so on, because we do we require a degree of clairvoyance for that. And they’ve had a recent spate of arguably bad luck in terms of working out the future. But he pointed out that the Bank of England does have a very, very simple in the form of seeking feedback from the community. It asks people for their own forecasts. Well, that’s a good beginning. And it’s better than any other bank that I know. I thought it was a terrific reaction.

Gene Tunny  37:06

Oh, that’s good. Yeah. Citizens panels, I think they call them so I’ll put a link in the show notes. I thought that was really good. And, and it really is heartening to see how open they are. And you’re right. I mean, I can’t remember anyone from a Australian government agency getting in touch or if they did get in touch, it would be all this has to be confidential, and it wouldn’t be an official email. So I think that’s good about the Bank of England. So that was great to see that. Now, just on some of those points, you raise you mentioned about modelling and that was it one comment that said I Okay, the issue was just the specification of the model. And I think you the way you reacted to that was, was was right. And one of the some of the comments I took out of the ft. Like there was some positive really positive comments in the comment section of the Financial Times. It was one about, ah, this sort of approach could have helped us in the early days of COVID. It could have avoided us from having some of your apocalyptic or Yeah, ridiculous, for ridiculous, and I think there was some criticism of the forecast room from his sage. I think they were sage forecasters. Yeah. That’s right,

Nicholas Gruen  38:20

sage, and was a guy who got himself briefly famous. And then arguably infamous. You put his name in these notes we have in front of us, Ferguson. Yeah, yeah. Yeah. Neil Ferguson. That’s it. And that, and you just had to look into that for a while to see that. The model was an immensely complex model. It wasn’t clear what it was useful for. But it wasn’t useful for quickly trying to understand, you know, ask quick, what if questions, it was an ornery monster of a model that produced a different result every time he ran it, because it was so common. Yeah. Just just not not built to certainly not in that situation. It was not built to help people make quick probabilistic decisions. But because it was a model, and because he was at a university Imperial College, as I recall, I hope correctly, then he had the stamp, you know, you had the brand. And so we spent a fair bit of our time with his model. It was pretty low grade stuff.

Gene Tunny  39:31

And so some of the negative comments or there were some people who are saying, Oh, well, look, you’re not you haven’t taken to account the fact that we’ve made all these advances in economic forecasting, and there are these new techniques and you’re unaware of them. I’m not sure that that’s true. And when I didn’t

Nicholas Gruen  39:47

mention any No, I didn’t mention any. So I mean, I’m sure I’m unaware of some of them, but he had no evidence that I was because what he’s or she is criticising me for is St. totally irrelevant. There is a state of the art of forecasting, the Bank of England or anyone else is either at the forefront or a bit back from the forefront. And the way to get to the forefront is to have a process of integrity, where people who are good at forecasting end up with better reputations than people who are not so good at

Gene Tunny  40:21

forecast. Yeah, yeah, exactly. And the point I would make, like when I read those comments, they were almost as I think they are assuming that it’s the model that gives the forecast that’s published in the Bank of England monetary policy statement or, or in any of these statements from economic agencies, it’s actually a forecast directly from a model. And it almost never is, there’s always an element of judgement, the model is one input into the the actual official forecast. And if you read the bank, the publication’s of the Bank of England, that’s very clear. And so your approach is about taking all of the the evidence out there or different views. I mean, you know, it could be in I think there’s something I was chatting with Warren hatch about, if I remember correctly, Warren was saying that, look, there can be value from having people in teams, like some people, someone has a model than there’s others who are more qualitative. And there are others who are looking at different bits of data you want. You don’t want a variety of approaches, I think and perspectives to get better forecasts.

Nicholas Gruen  41:27

I’d say some, I think that’s absolutely right. But I think you can say something more than that. We exist in a society in which governments and agents and organisations are performing for our entertainment, if I can put it that way, at least under the guise of the media, they’re doing stuff, they’re justifying them stuff. They’re got comms people coming out, saying, This is what we’re doing, and they’re putting over a plausible story. And then you get pundits, I would say, like us, except I try not to do this. But almost all pundits and almost all Twitter pundits, almost all instant experts, they come out. And they say what, really what you should do is x or y. But in fact, what you should do is a very complex and acculturated performance. So it will involve technical understanding and modelling. It will then involve judgments, as you say, but then how do you get the people with the best judgement to make the judgments? Well, we haven’t really solved that problem, we just get the most senior people to make those judgments. So it’s like me saying, I want a good COVID vaccine. And this is the process that we should go through to get the COVID vaccine. What I want is a process that has legitimacy, because I believe that if I looked into what that process was, it would add up it would have integrity. In the words of Charlie Munger, the highest form of civilization is a seamless web of deserved trust. In other words, there isn’t a clear line between the pundit class and what you do. If you’re doing anything difficult building a bridge or dare I say, a nuclear submarine pundits can can’t actually say very much, they can say a few things about what would be really dumb. But there’s so much that goes into this. And the public discussion isn’t had in that kind of way. But that, ultimately, is one of the reasons that I’m such a fan of Philip Tetlock stuff on forecasting and creating forecasting tournaments, because it’s one of the few areas where you can start to build some objective relation between reality. And as poor munchkins working away trying to work out what that reality is, and our social and political institutions have done? Well, the job they’ve done might be the best in history, but when you look at it, it’s not all that great, there are plenty of things wrong with it. So, this is a rare case where there is a better way you can see what it is you can understand its principles and we should really try to implement it and also learn from it how how we could extend that since making reality contacting function.

Gene Tunny  44:31

Yeah, absolutely, fully agree there. So, I mean, one other point I just wanted to make is on that, the forecasting the the whatever the you know, best practice or the in terms of technical forecasting. One of the articles there was, it was linked to in the in the comment section, the Financial Times it was an article that was by a number of forecasting experts and one of them was Jennifer Castle’s, who works with David Hendry. And Henry has been on the show. And if you’re interested in these issues, that would be a good conversation to go back to because David talks a lot about the ways that he tries to get his model based forecast as best as possible. Now, that’s, that can be an input into this a super forecasting approach. It’s not, these things aren’t mutually exclusive. But what he’s doing, he’s trying to build an econometric model that can be an input into the forecasting. For the point I’d like to emphasise is that the forecasts that end up in the reports and then end up influencing budget, so they’re never just the outcome of models, because we know that a model is useful. But you there’s always a judgement involved, you’re always going to be tweaking things to make it because there’ll be things in the model you go hang on that may not be realistic in the current circumstances. Yeah, exactly. Yeah, exactly. Right. Oh, so Nicholas. I just wanted that quick catch up. Because I thought, yeah, that was a great article of yours. And it’s got some excellent feedback. And I think it’s, it’s probably achieved what you wanted to achieve, I imagine.

Nicholas Gruen  46:08

Yeah, absolutely. Even though they told me I only had 650 words, and then they only allowed me 570 words. So my nice paragraphs about what a big fan I was of Andy Haldane, who was no longer at the Bank of England, they were all taken out the likes of fanboy helding while he was a civil servant, was my favourite civil servant in all the world. Very good. Yes.

Gene Tunny  46:36

I’ll put some links in to about Andy Hill died. Did you? Have you written this on your club dropout? Or Nicholas? Your? Um, I’m

Nicholas Gruen  46:44

not sure I have I’ve. Yeah, maybe I should. But But no, I have because I published some articles in the Mandarin, which is an Australian Public Policy Magazine, if you like, which is and they’re always backed up onto my blog, and one compared the Australian Reserve Bank, with the Bank of England and the and particularly the blog notes underground. I think it’s called always good to quote Dostoevsky. I suppose when Greg Clark isn’t quoting, isn’t quoting titles from Hemingway, the Bank of England can be can be paraphrasing Dostoevsky in the name of its blog notes underground, I think it’s called. And it has lots of really interesting think pieces. It’s not very standard academic stuff, although there’s some of that as well. I think it’s a very sad thing that government, certainly independent agent, government agencies around the world don’t do that a great deal more. I may be fondly imagined that Andy was one of the movers and shakers behind that. But certainly he did lead a lot of research showing the costs of too big to fail implicit subsidies for large banks and just did lots of use the, the US the independence of the central bank in a way that was very, very helpful in difficult times during the global financial crisis. And in the years after the financial crisis is people trying to work out what had gone wrong and how to fix things. Yeah, absolutely.

Gene Tunny  48:23

It’s, it’s interesting that Yeah, I agree about the Bank of England, probably being the best central bank certainly has the best museum. I guess there’s that literary connection. Yes. And I only learned about this when I went to the museum, Kenneth Graham work there, the author with the willows. Hmm, yeah, I work there. I mean, I have relatively senior position there in the Bank of England because they’ve got a little display about Kenneth grime in there.

Nicholas Gruen  48:53

I missed it. I missed it. I’m sorry that I missed it. Because I have seen that museum. It’s quite small. It’s just a few artefacts as I recall a room or 2am I

Gene Tunny  49:02

wrong. Yeah, it’s a maybe a few rooms, but there’s that great display where you can lift up a bar of gold, you stick your hand in a glass glass box, and you’re gonna lift up an actual gold bar, which I thought was pretty cool. And you know, they’ve got all the currency. Yeah, he got up to the rank of Secretary in 1908. So I don’t think he was he wasn’t the governor, but he got up to a senior position. Excellent. Very good. Okay, Nicholas, thanks. Again. That was such a it was good to catch up because, yeah, good. always interested in economic forecasting, because we’ve had such a, unfortunately a mixed record of it in Australia and around the world. So it’s, it’s good to talk about a new approach and well done for doing your best to advance one.

Nicholas Gruen  49:50

Thanks very much

Gene Tunny  49:53

rato thanks for listening to this episode of Economics Explored. If you have any questions, comments or suggestions, please get in touch match, I’d love to hear from you. You can send me an email via Or a voicemail via SpeakPipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it. Word of mouth is one of the main ways that people learn about the show. Finally, if you’re podcasting outlets you then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.


Thank you for listening. We hope you enjoyed the episode. For more content like this or to begin your own podcasting journey. Head on over to


Thanks to Obsidian Productions for mixing the episode and to the show’s sponsor, Gene’s consultancy business Full transcripts are available a few days after the episode is first published at Economics Explored is available via Apple PodcastsGoogle Podcast, and other podcasting platforms.

Podcast episode

Highlights of last 100 incl. Brad DeLong, Sir David Hendry, Leonora Risse, Andrew May – EP200

In this special 200th episode of Economics Explored, host Gene Tunny is joined by Tim Hughes to discuss some of the highlights from the last 100 episodes. The episode features clips of Brad DeLong (UC Berkeley) describing how we’ve been slouching towards utopia since 1870, Sir David Hendry (Oxford) on the merits of small modular nuclear reactors, Leonora Risse (RMIT) on the benefits of diversity, and Super Forecaster Warren Hatch on what makes a good forecaster, among others.  
Please get in touch with any questions, comments and suggestions by emailing us at or sending a voice message via

You can listen to the episode via the embedded player below or via podcasting apps including Google PodcastsApple PodcastsSpotify, and Stitcher.

What’s covered in EP200

Links relevant to the conversation

Episodes from which clips were taken from:

Slouching Towards Utopia w/ Brad DeLong – EP163 – Economics Explored

The Progress Illusion w/ Jon Erickson – EP166 – Economics Explored

Thriving w/ Wayne Visser, Cambridge & Antwerp sustainable business expert – EP130

Sir David Hendry on economic forecasting & the net zero transition – EP198

Superforecasting w/ Warren Hatch, CEO of Good Judgment – EP176 – Economics Explored

Women in Economics with Dr Leonora Risse of RMIT, Melbourne – EP124

Truth (or the lack of it) in politics and how to think critically with help from Descartes – EP123 – Economics Explored

The importance of physical & mental health for top CEO performance w/ Andrew May – EP193

Link to info about Windscale fire mentioned in conversation between Gene and Tim:

Windscale fire – Wikipedia

Highlights of last 100 incl. Brad DeLong, Sir David Hendry, Leonora Risse, Andrew May – EP200

N.B. This is a lightly edited version of a transcript originally created using the AI application It was then checked over by a human, Tim Hughes from Adept Economics, to see if the otter missed anything in it’s rush to catch fish or star in YouTube videos. It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.

Gene Tunny  00:06

Welcome to the Economics Explored podcast, a frank and fearless exploration of important economic issues. I’m your host Gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode, please check out the show notes for relevant information. Now on to the show.

Hello, thanks for tuning in to the show. It’s episode 200. And joining me this episode to chat about some of the highlights of the last 100 episodes is Tim Hughes, Tim, good to have you with me again.

Tim Hughes  00:56

Hey Gene, good to be here. Thanks for inviting me on, and congratulations on your bicentenary.

Gene Tunny  01:01

Yes, yes. Thanks, Tim. Well, you’ve been part of, you know, quite a few episodes over the years, and I thought it’d be good to get you on to get your perspective as the man on the street so…

Tim Hughes  01:16

Isn’t it the guy on the Clapham omnibus, is that right?

Gene Tunny  01:19

Yes. The man on the Clapham omnibus, I think it is. Yes. Exactly.

Tim Hughes  01:23

Looking forward to it.

Gene Tunny  01:25

Right. Well, that’s the reasonable man test. So yes, what’s the reasonable man on the street think?

Tim Hughes  01:33

Well, I’ll try and be as reasonable as I can.

Gene Tunny  01:35

Okay, so what I’m going to do, Tim, is I’ll play some of the clips that I think are the best of Economics Explored over the last 100 episodes. Now. I mean, there’s so much good content. And I mean, they’re great. There’s great material that I haven’t been able to include. But these are ones that I really think are great. But look, I’m grateful for all the people who come on the show. So yeah, let’s get into it, we’ll go over these ones that I think are, you know, really standouts. So okay, so to start with, I’m going to play a clip from the episode we did, so this was episode 163, last year with Brad DeLong.

Brad DeLong  02:24

And that’s the state of the world before 1870. And that means that unless you’re in an extremely lucky place, or like Australia, or an extremely lucky class, that life is going to be kind of brutal, short and without very many options, which means that in most times, in most places, governance is going to be how does an elite figure out how to grab enough for itself and maintain its rule over society. And after 1870, everything changes, technological progress becomes rapid, the technological competence of the human race globally doubles every generation, you quickly get a world in which people are kind of rich enough that infant mortality falls substantially. And with that falling infant mortality, and with the erosion of patriarchy, all of a sudden, you don’t have to concentrate a lot of effort on having children, to be confident that if you reach the age of 50, you’ll still be able to run your own life. And so you’ll we get the demographic transition, now headed toward a stable world population of 9 billion. So for the first time after 1870, technology wins the race with human fertility, you know, and we begin to look forward to a time when humanity will be able to bake a sufficiently large economic pie so that everyone can have enough. And you know, people back in 1870, and before, you know, they thought most of the problems of society came because incomes were low, and technology was underdeveloped. And you had this elite running a kind of domination and exploitation game on everyone. And once you can bake a sufficiently large economic pie for everyone to have enough, those things should fall away. And the problems of properly slicing and tasting the economic pie, right? Of equitably distributing it and then utilising it so that people can feel safe and secure and live lives in which they’re healthy and happy. Yep, those should be relatively straightforward to solve. And so we today, at least we today in the rich countries should be living in a Utopia, which we are manifestly not. And so the story of history after 1870 is how we’re well on the way to solving the problem of baking a sufficiently large economic pie. While the problems of slicing and tasting of utilise, of distributing and utilising it continue to flummox us.

Gene Tunny  04:59

Okay, so I thought that was a really great clip, Tim and I was talking to Brad about his new book, or new last year, Slouching Towards Utopia. And it’s a the economic history of the world, basically. And, yeah, I thought that was a really nice way that he talked about just all the, the economic gains we’ve had since 1870. He sees 1870 as the hinge of history, before that we’re in this sort of subsistence way of living. And then after that, when the industrial revolution really took off, and we got electrification, then we just had these massive increases in living standards. So we’ve solved well with I wouldn’t say solved, but we’re so much wealthier, and the, you know, our production possibilities are so much greater. But we’ve still got, we haven’t got everything, you know, perfectly right, obviously. And there are issues, arguably issues of distribution. And there are also environmental issues, too, that I wanted to talk about. So I thought that was a good one to kick off with, because it reminds us that when we think about the economy, when, as economists we should be thinking not just about the GDP, not just about production, but we should also think about distribution. And we should also think about other impacts, so impacts on environment, etc.

Tim Hughes  06:29

Yeah, it’s actually a really good one to start with, because it sort of sets the scene. I know you’ve grouped together a few clips, that are of the same kind of genre. So this is a good one to lead off. It gives a good sort of snapshot of the, of where we’re at and where we’ve come from in the last 150 years. So it’s the equitable distribution question, I guess, is, is really a big one. And of course, a very, as simple as it should be, or could be, clearly it’s, it’s not simple to execute it, it ends up with a lot of the equity being in the hands of a few and people are struggling in great numbers around the world. So that slicing up at the pie seems to be a big challenge that we haven’t really cracked,

Gene Tunny  07:14

Well, fewer people struggling around the world than previously. So I think one of the great things about the last 30 or 40 years, particularly since the economic reforms in China, as we have seen hundreds of millions of people get out of dire poverty. So that’s great. And that’s a that’s a real win for market economics. So it’s a, you know, a win for free markets. Now, I think what Brad is, he’s really concerned about what’s happening in the States, because in the States, particularly since the 80s, you you’ve seen a lot of the gains, the economic gains, go to the top, go to the top 10%, top 1%, top 0.1%. So that’s one of the things he’s concerned about. Now. You know, there’s a there’s a trade off there, because there’s this trade off between equity and efficiency. The big trade-off as Arthur Okun called it. Whereby, I mean, you don’t, you need some inequality. I mean inequality is unavoidable to an extent and you need some you need rewards for people taking risks and working hard. Otherwise, people will, they won’t, they won’t be working hard or taking risks, and you can end up with the Soviet Union. Right? So we want to have a system of rewards. But then there is a question of, you know, what are the right tax policy settings to make sure that those who can pay more, those who can afford it pay more? There’s some redistribution, there’s a, there’s a big debate to be had about that what those appropriate settings are?

Tim Hughes  08:55

Yeah, yeah, I know, we get into a little bit more detail in certain areas in the next few clips. But it’s a massive conversation, and knowing our ability to go at great depth with these, I’m going to cut myself off there, because I’ve got more to say on that on the next few clips.

Gene Tunny  09:12

Very good. Well, I should play some more clips. I thought that, that one from Brad DeLong is Professor of Economics at University of California Berkeley, so very distinguished American economist, a former senior official in the US Treasury, and in the Clinton administration, so very prominent economist, so I was really glad to have him on the show. Okay, so that’s, that was from Brad DeLong. The next clip I want to play is from Jon Erickson, and he’s an ecological economist. He’s from Vermont, and he’s been an adviser to Bernie Sanders. And he’s got some interesting things to say about, well he’s got his, you know, an interesting perspective on the constraints on economic growth. So I’ll play this now. This is from episode 166.

Jon Erickson  10:01

Well, what would an economy look like that was built on maintenance, resilience and cooperation instead of growth, efficiency and competition, right? A late stage maturing economy like yours in the Australia ours in the US. So that’s what I’m asking, you know, an economy, a mature economy should have different goals than an economy at pioneering stages. So it really is about a reprioritization of our goals, especially on consumption, right? Because there’s ample evidence to show that we in the West are over consumers, and our kind of addiction to consumption is creating psychological problems, social problems, that consumption has been kind of become a cure for social ills, right, like a distraction. I mean, the whole advertising industry is designed around the idea of kind of making you and I feel bad about ourselves, right? To sort of fill the void, with more consumption. And I actually think this is one of the lessons coming out of COVID, right? It’s this sort of people were, especially, you know, high income people who, who could weather the storm, better than most, were forced to slow down, were forced to be at home, were forced to kind of reevaluate life’s priorities, and found out that, you know, this kind of ever burning hamster wheel of economic growth isn’t all that it’s cut out to be. So it’s a reprioritization of goals, which is going to have to reprioritize policy instruments. Daly, Herman Daly, used the analogy of a Plimsoll line, I’m not sure I’m pronouncing that right, of a cargo ship, right. So this is the line that’s painted on a ship, very easy technology. And as the as the cargo ship is loaded, it sinks into the water. And when they get to the line, you’re supposed to stop, right, because you’re in danger in danger of overloading the ship. So if we sort of reprioritize and think about the Plimsoll line of an economy, we can’t just more equally or equitably distribute the cargo of an overloaded ship and expect it to be resilient. We can’t just more efficiently load an overloaded ship, and expect it to weather the storm, as the Plimsoll line goes underwater, right. And there’s ample evidence to say that we are a kind of in an overshoot on a lot of environmental parameters, you’re in danger of sinking the ship, especially in stormy waters. So this analogy implies that as we run up against planetary boundaries, planetary limits to growth, the scale of the economic system is way more important to stress than distribution or efficiency. And if we can’t count on a growing system to solve distribution problems, then we’re going to have to quickly think about the fairness of the distribution of benefits and costs of that system. And then and only then can we get to efficiency, which is the priority of economics. So this means that you know, new policy instruments that that focus on scale, distribution, then efficiency is the way to go. And I talk a lot about this in the last chapter of the book, as I kind of wrestle with the idea of, how did I put it, radical pragmatism, right? Lots of pragmatic things that we can do now, for example, to wean ourselves from fossil fuels, you know, home weatherization, and carbon taxation, and, you know, maintenance of our systems, electrification of transportation, transition to renewable energy. But all of these are really hard to do in an economy that continues to bloat, an economy that continues to grow. So we have to be thinking about the scale of the system and that’s probably the radical part of radical pragmatism, right? What’s it going to take to rein power away from the status quo, that part of the system that’s benefiting from this growth model, and create an economy that works for all?

Gene Tunny  14:25

Okay, so that was Jon Erickson, from University of Vermont. Jon Erickson is the David Blittersdorf, Professor of Sustainability, Science and Policy at the University of Vermont. And we were talking about his new book, The Progress Illusion, and I thought that was a great clip, Tim, to play because it’s a completely different perspective from my perspective. And so I’m all for having, well being open to different perspectives and having that conversation. I think he makes you know, some of the points I agree with in terms of what we’ve got to do, I mean, I think long term, there’s no doubt we’ve got to get off fossil fuels. I agree with that. We’ve got to electrify, I’m not disagreeing with that. I’m probably sceptical of what, to what extent we’re, we’re hitting these planetary boundaries already. To what extent we, we should be trying to, I don’t know, he didn’t use the term degrow. But there is this, you’re aware of this term degrowth, aren’t you? And this is something I’m looking at, at the moment for a paper for Centre of Independent Studies. So I think the whole Ecological Economics field, I think, coming out of that there is this, this concern that we are hitting up against these planetary boundaries, we need a, if not degrowing, if we don’t degrow, we at least have to have a steady state economy. They’re worried that we’re just, you know, this, this ever growing economy, ever growing demands for resources that’s causing us a lot of problems. So it’s an interesting perspective. I mean, I’m, I’m a bit sceptical of it. But I did enjoy that conversation with Jon, he’s, he’s a great guy, and I thought that would be a good clip to play.

Tim Hughes  16:09

Yeah, I really enjoyed this one. I think the whole aspect of sustainable contraction, for instance, which we’ve talked about before, as opposed to sustainable growth, like at some point, there’s only so much that can be done, there are parameters. I mean, I see the planetary parameters being quite clearly defined as we get, as the population gets bigger, 9 billion, 10 billion, up to 11 billion by the end of the century, forecasted population. You know, the oceans aren’t infinite, the atmosphere isn’t infinite, the soils, everything that we pollute, you know, we, there’s a point at which, with so many of us on the planet, that, yeah they’re the parameters, and I think they’re quite well defined, Whether people believe in climate change or not, I think the question should be that, given the, the fact that these aren’t infinite resources, at some point, it’s going to be an issue, even if people don’t think it’s an issue yet. And I think we do have the technology and the know-how, and the the will now to, to make ourselves more efficient. So we have less waste, cleaner energy, you know, look after the planet more. So it sort of fits in with, you know, environmentalists that have been talking about this for years. And I think, I think it’s great that it’s come to the fore in conversations around economic policy, because yes, I mean, for instance, I, I firmly believe that it’s really important, it’s possibly the most important thing that we could do, is to be really good in these areas. So talking about, you know, like, so from going from more and more, which we’ve had this incredible growth, you know, going back to Brad DeLong, from 1870 through to now, it’s all been more, more, more. And, and at some point, the question becomes, well can we do it better, better, better, not just more, more more like, it’s, we’ve got enough, like, we’ve got enough to feed the planet, for instance, we’ve got enough to feed everybody on the on the planet. But we don’t distribute it. You know, it’s the whole way of working out who gets what or how we manage our resources isn’t done well enough to do on a good social scale, or a scale that would work financially, economically. And I think that’s the right way to go about it. You know, he talks about the ever burning hamster wheel of economic growth, you know, and that’s a great, great term, radical pragmatism needed to sort of have a fresh look at how we do things. And I couldn’t agree more. I think it’s really good and clearly contentious and not easy to do. But I think that’s the right direction to point in. And there is momentum going towards us, you know, net zero for 2050 fits in with this kind of thinking. And it has a, you know, a lot of support behind it. And I think it’s good.

Gene Tunny  18:50

Yeah, I think the more, well we might have to have a, an episode on degrowth, specifically, because some of the more radical people who are concerned about these planetary boundaries would be saying that we need to do even more than net zero, right? Because conceivably, we could get to net zero, and still keep growing the economy. And there are people who are optimistic. And then there are others who say, well, though, those techno-optimists, they’re naive. Now, I mean, I’m a great believer in technology. And I think technology is one of the reasons that we have had that strong growth since 1870. Or we’ve had all of these amazing gains in productivity, gains and living standards, because we’ve managed to solve our problems, have managed to, we haven’t run out of resources, we’ve managed to, well we’ve explored with our new resources, we’ve switched to new sources. I mean, we switched from whale oil to, to oil to from originally from Pennsylvania and then from the Middle East and all other places. So we’ve managed to, to, you know, to actually to innovate to avoid those constraints. And we’ve historically we’ve been able to do that. Now, I’m not naive enough to think that we, you know, we’re always going to be able to do that. But so far, we’ve had an incredible run at it. Really? So we haven’t really hit those constraints we’ve managed to grow so far.

Tim Hughes  20:22

Well, as far as planetary constraints, I mean, I see that as a resources thing and space, air, water, you know, soil, those kinds of things. That’s what I was, was, yeah, I was reading into that.

Gene Tunny  20:34

And we’re trying to manage those, certainly, in Australia, I guess the problem is in, in other countries and in emerging economies, and you had that great chat with Guillaume Pitron didn’t you…

Tim Hughes  20:44

Yeah, I was thinking of that, too. And it is that thing about, and that’s part of managing our expectations, because the more, more, more in economic growth can be directly transferred over to us as humans materialistically wanting more, more and more, and so our driving, desire for these things, you know, for possessions, is part of that whole story. And so part of managing the planet’s resources better, I think, would also be a question of maybe managing our expectations better as well, you know, like, if we can, I don’t know, become more content with less, you know, which is is often referenced around the world where people seem to be extremely happy with very little because they they engage in, they have strong communities, they don’t necessarily necessarily have a lot of wealth or material goods. But they engage with the things that as humans, we, we need the most, which is social connection. And, you know, that contact, which is lacking more and more, there’s more loneliness in the Western world. And, you know, people unhappy, they have more, but they’re, they’re less happy. So it’s this thing of like, okay, well, maybe we need to look at that, as well as part of this whole conversation about our expectations and who we are as people and what we need as people.

Gene Tunny  22:02

Yeah, well, there’s that concept of the hedonic treadmill. Yeah, so I might put a link in the show notes to that, because you’re right, I mean, you can, at our standard of living, additional increases in standard of living aren’t necessarily going to make us happier, right? I mean, we can, we could be happier on much lower incomes than we do have in Australia. Well, this is what the…

Tim Hughes  22:25

Haha I know I’m kidding, we all we’re all sort of influenced by this, and I…

Gene Tunny  22:28

Or an average or a lower average, lower average income, I suppose. Because part of the problem, I guess, is the yeah, there’s the Keeping Up with the Joneses. And there are a lot of expectations on us.

Tim Hughes  22:40

Well, again, I know, this comes up in one of the other clips, you know, with, in the realms of free market, and you know, the ability for entrepreneurs to do their thing, to be supported or not, etc. And so, with those freedoms come risk, you know, and that’s part of the game if you like, but with most of the western countries, there were social systems that are good enough to help people who are struggling, and you know, that’s, that’s so important to have that, of course, and it gets into the realms of UBI, universal basic income, where that may form part of a fairer society. But you know, it’s, I think, again, it’s, it’s good to point in that direction to see where we might be able to manage, I, you’re the policy guy, I don’t know much about it, until I see it and see what it does as a man on the street, the guy on the street. But clearly, there seems to be a lot of wealth in a few hands and not so much in others. So if it can all be managed better, to be better, equitable distribution, then I’d like to see what that looks like.

Gene Tunny  23:45

Well this is what a lot of the political debates are about. I mean, yeah, again, it’s that trade off, right. I mean, equity and efficiency. I mean, if you have too high tax rates, and you know, you’re not encouraging entrepreneurship. You’re not encouraging people to work hard. But then again, I mean, if if you don’t have some form of progressive taxation, then you can end up with high inequality. And, you know, arguably, what you’re seeing in the US at the moment. You’ve got the, yeah this huge gap between the wealthy and the US and, you know, the middle class, the former middle class. I mean, it’s there’s still a middle class, but it’s not as large as it was back in the, in that post war era. The first 30 years after the war, so yeah, there’s there’s big issues there. Tim, I’d better move on to the next clip, I guess, because I want to play a clip from someone who’s super optimistic. So a South African Professor, I think he was South African, Wayne Visser and he’s, he’s got some role at Cambridge. I’ll put a link in the show notes. And he’s also he’s a Cambridge pracadamic. That’s right. Remember, we were chatting about that? Yes. Actually, he’s not South African. He was born in Zimbabwe. Okay, very good. Well, Wayne, he wrote a book, Thriving, and I interviewed him last year. And that was episode 130. So we’ll hear from Wayne. And he’s got a really optimistic perspective on just how technology is going to help us get out, or get us out of a lot of these environmental challenges.

Wayne Visser  25:29

Eu Green Deal, it’s effectively the Europe strategy on climate change, very, very comprehensive and very ambitious. And it touches everything. It’s got a Farm to Fork area, which touches agriculture, it’s got a mobility area, all around electrification of mobility, it’s got a circular economy element, it’s got a finance element. So yeah, I mean, it’s, it’s a very, very strong policy and it’s being, in some ways, you know, America is, is trying to copy that with the new Green Deal. So, so yes, policy helps with the coherence piece. And then you’ve got creativity, which we’ve talked about a little already. So for things to change for all living systems to change, they need innovation, and that happens through diversity. Again, something we’re working very hard on, but we, we are living in an age of innovation, no doubt about it. And many of our most difficult problems, we are seeing some amazing solutions coming. If we just pick on one, for example, we know electric cars, I’ll leave that alone, but just remember that that is changing much faster than people think. I mean, Norway is banning fossil fuel cars by 2025. That’s just around the corner. And most other countries, you know, UK, it’s 2030. So within 10 years, it’ll really be something to watch. But take food, for example. There’s a whole movement of course around going more plant based, that makes sense from a health perspective, because 20% of mortality can be reduced, just by going more plant based, but also from a climate perspective, and a biodiversity perspective, and of course, animal welfare perspective. But here we see innovation, you know, you’ve seen the Beyond Burger and the Impossible Burger, you know, these are really engineered to look and taste, you know, like the real thing I know that may be a hard sell in in Australia but uh, on blind tests, actually, they they’ve done extremely well. Not only that, but we’ve got cultured meat coming. You know, this is grown in labs, meat essentially grown fermented, grown in vats, like you do for insulin. And this is this is going to completely change everything because again, you don’t have the the input of land and water. You have much lower energy input and you’re not killing anything. So you’re literally just taking cells, live cells from a cow, for example. And you’re creating that is already in Singapore, you can already go to a restaurant that sells cultured chicken. So this is innovation happening very fast, massive amount of investment going into this.

Gene Tunny  28:27

Okay, so that was Dr. Wayne Visser, Professor of Integrated Value and holder of the Chair and Sustainable Transformation at Antwerp Management School. So he wrote a book, Thriving, I was really grateful to have him on the show last year. Tim I thought that was a really good, well, there was some great observations about technological change. And I mean, he had lots of other good examples in his book, I’d highly recommend it. He’s someone who is incredibly optimistic. So I was really glad to talk to him last year. Do you have any reflections on what Wayne said in that clip?

Tim Hughes  28:59

Yeah, I thought it was really good. I’m going to quote from him, he said, “for all living things or systems to change, they need innovation and that happens through diversity.” And it’s great seeing that highlighted as something that is perfectly natural in our world. Like it’s funny diversity is often seen as something that we have to accommodate or get used to and, and bring in, and it’s like, it’s been there all the time. It’s, it’s a perfectly normal part of how we’ve evolved, of how everything’s evolved. And, and the importance of diversity, the role that diversity plays in so many different things. And again, I know this is going to come up with a couple of other clips. But it just shows I mean, and to have, to have it mentioned in this regard, it’s like yeah, great, that that makes so much sense. And also you can see how those things are coming together with technology that fits in with the economic efficiency, if you like or the way of making sure that we can do something better instead of just more and more well, how can we do it differently? You know, what would be a different way of doing this and, and those plant-based meats or meat alternatives are good examples of how we can do something where it’s better for animal welfare, it’s better for human health, it’s better for the environment, there’s a lot of wins with that direction in that area.

Gene Tunny  30:15

Well, I think the cultured meat or the meat grown in a lab that’s effective, it’s effectively the same thing. It’s like the real thing. But you don’t have the you don’t have to raise the livestock and you don’t have the all of the ethical and animal welfare issues associated with with livestock. So I think that’d be terrific. If we could do that on scale.

Tim Hughes  30:36

Yeah, I think there are still a couple of ethical issues around that. But then ethically, you know, as long as it’s safe, and all these different things, as long as it can, you know, tick that ethical box. It’s ethically better than, you know, the the amount of meat that’s going through the current system with the abbatoirs and everything. I eat meat, you know, so like, you know, I wouldn’t, I’m not wanting to be a hypocrite about this and I think meat is important as a choice. But I would like to see, raised ethically, killed ethically, you know, as much as possible. And to have less, you know, I’d be I’d happily eat less meat, with these kinds of alternatives available to, you know, sustain us with our protein intake, for instance.

Gene Tunny  31:18

Yeah, yeah. Very good. Well, I thought that was great from Wayne now, just on this whole theme of economy and equity and environment. Then this theme, I thought I’d play a clip from our recent conversation with Sir David Hendry. So professor at Oxford, he’s an absolute legend in econometrics. And we will, I was really glad to have him on the show. And he made some really interesting observations on the potential role of nuclear energy. So that that surprised me in that conversation. And it’s good that we’re glad that we got onto that subject. So I’ll just play this clip from Sir David Hendry.

We don’t have nuclear energy here, and the opposition party is trying to push it. But then I think there’s going to be a lot of community resistance to that here in Australia.

David Hendry  32:08

Yeah, I can believe that. But do people understand small nuclear reactors? That’s the only ones we’re arguing for, not the big ones, the small ones. In Britain, lots of big ones. And they’ve produced a lot of transuranic waste, that’s going to be a huge problem for humanity. Now, there are two advantages to small nuclear reactors. One, they can use that transuranic waste as their fuel, and greatly reduce the amount of radioactivity that needs to be dealt with from it. And secondly, they’ve been used in nuclear submarines for 50 years, and there’s never been an accident. So they’re very safe, and they don’t have any fissionable material that terrorists might want for bombs. I mean, the stuff they’re using is useless. Other than burning up the waste that’s a problem anyway. If the public knew that these are harmless, that they’re getting rid of a problem, you don’t have nuclear reactors so it’s less of an argument there. But in Britain, people would jump at the chance to cut the amount of nuclear waste that needs to be disposed of burying it, or putting it in deep caves, etc. And these guys can do it.

Gene Tunny  33:23

Right yeah. These are the small modular reactors are they?

David Hendry  33:26

Yes. they are indeed,

Gene Tunny  33:23

Yeah, I think that’s what Peter Dutton, who’s the Opposition Leader here, what he’s talking about.

David Hendry  33:33

Good for him. I think they are actually an important component, but only one possible component, of an electricity provision that would give more energy security and, and be something that can work in almost all circumstances.

Gene Tunny  33:50

Okay, that was Sir David Hendry on nuclear energy. Tim, I mean, we’ve chatted about this conversation with Sir David before, haven’t we? And we both thought, yeah, good stuff.

Tim Hughes  34:00

Yeah it was great and this was something for instance, that I hadn’t heard of before, the SMRs, small modular reactors. And it’s funny that I like it made me very much aware of my own prejudice towards something nuclear, towards being a viable power source because it gets such a bad rap, understandably, from you know, Chernobyl and Windscale and different things around the world where the consequences are catastrophic. And the amount of waste, nuclear waste that has to be buried, like is dangerous for 1000s of years, whatever it like, it’s not great. So the clean, the push for clean energy, seems to be something that would be without anything nuclear. However, it was, it was good because that my first response was like, that doesn’t sound great. But listening to these SMRs, or small modular reactors, and what their capabilities are and what the consequences are. You know, here we are in 2023. There’s a net zero target for 2050. There’s a transition period there of 27 years and in that transitional period, you know, something like SMRs could well be part of that picture to be able to get us through that time or may be part of the future for longer. But I think it helps in opening up the conversation about what these, this range of possibilities might look like. It does not, it seems to be clear, there’s not one thing that’s going to be our main power source. It may be but there’s certainly going to be several. And so if this forms part of that transition, or part of the solution, to be able to get us to net zero, then I think it’s really important to have the right conversations around it.

Gene Tunny  35:36

Absolutely. You mentioned Windscale, so I was I wasn’t aware of that. So there was a fire on 10, October 1957. The worst nuclear accident in the UK’s history. So yeah, I’ll put a link in the show notes to that, I wasn’t aware of that.

Tim Hughes  35:50

Up around the Lake District if I remember correctly around Cumbria? If it’s still called Cumbria, I’m not sure. It is that thing of like, you know, the consequences and concerns or, you know, naturally like, you know, people don’t want to be living near a nuclear reactor. And if they’re big ones, well, the, the spread or the the possible influence of, you know, geographically, the disaster zone is quite big. Right. So, these SMRs, it was interesting. That was something new. And, and hearing the rest of the talk with Sir David, it was like, well, this is coming from a guy who is looking towards net zero, you know, incredibly smart guy and this is the kind of thing that you know my ears really prick up when I see or hear people talking about these things. It’s like, ok, well, this, this is worth, you know, really considering or learning more about.

Gene Tunny  36:46

Okay, we’ll take a short break here for a word from our sponsor.

Female speaker  36:52

If you need to crunch the numbers, then get in touch with Adept Economics. We offer you frank and fearless economic analysis and advice. We can help you with funding submissions, cost benefit analysis studies, and economic modelling of all sorts. Our head office is in Brisbane, Australia, but we work all over the world. You can get in touch via our website, We’d love to hear from you.

Gene Tunny  37:21

Now back to the show.

We might leave that theme now and move on to the next theme of of great clips from the last 100 episodes, which has to do with decision-making, forecasting and critical thinking. So the first clip I’ll play is from our conversation early this year, Tim with Warren Hatch, CEO of Good Judgement in New York City. So Warren’s been involved in the whole superforecasting project with Philip Tetlock. So I’m going to play a clip from Warren on what makes good forecasters.

How do you get on this superforecasting panel? Who’s a super forecaster? What are their characteristics?

Warren Hatch  38:05

That’s a great question. And that’s something, and something to keep in mind, too, is that in the research project, that wasn’t part of the research plan at all. They just observed that in the first year, there were some people who were consistently better than everybody else. And being researchers that caused a new research question, what would happen, they asked themselves, if we put them on small teams? Would they get better or would they revert to the mean? And they did not know at all, a lot of people thought there’d be a mean reversion, turns out, no, they continue to get even better. And so we still do the same process now with our public site, where we’ll just take within the top 1% of the forecasting population there, and other platforms too invite them to come and join the professionals. And they have certain things in common. For sure, they gave us a lot of psychometric tests, hours of them before we got to do the fun stuff, you know, when forecast on elections in Nigeria and the like, and then to see what kinds of characteristics correlated with subsequent accuracy. And there’s certain things that really pop out. One has been really good at pattern recognition, right? So you can think of, you know, you’ve got a mosaic about the future that you’re trying to fill in and see what’s coming faster than anybody else and fill in those tiles. And being good at that is a fundamental characteristic of a good forecaster. Another is being what they call cognitively reflective. And basically that means that if you’re confronted with a new situation, you don’t automatically go to what first pops into your head because what first pops into your head might not be right, you might be overfilling the mosaic too quickly and getting the wrong picture. So you want to slow down and in Kahneman terms, let system two be your friend. You know, it’s hard work, but that’s the way you get a better a better result. So those are two very fundamental characteristics that good forecasters have.

Gene Tunny  40:03

Okay, so that was Warren Hatch from Good Judgement. Tim, that was another great interview subject that you lined up along with Sir David Hendry. So well done on getting Warren onto the show. Yeah, I thought that was terrific. Everything he was saying there about the importance of pattern recognition and being cognitively, cognitively reflective. So any thoughts, any reactions?

Tim Hughes  40:28

Yeah, I loved this episode, I got so much from it. We’ll have a round two I’m sure, at some point soon. It was really interesting. And like, it fits in with a lot of the, well, conversations that we’ve had. I mean, for instance, you know, I try and bear those things in mind, you know, if, for my own decision-making, etc. So, so for instance, cognitive being cognitive, cognitive. I’ll start that one again. Gene.

Gene Tunny  40:56

Is that because I struggled with it?

Tim Hughes  40:59

No I’m just trying to make you look good! Being cognitively reflective, is what I, for instance, did with the SMRs that David Henry mentioned. So my first response was nuclear, nuclear, whatever, that doesn’t sound good. But keep listening, keep, keep the mind open as to what that might look like, you know, so there’s good lessons, there’s so much good stuff in that episode, with Warren Hatch, and everything he was doing and talking about. They’re all things that we can all do as humans in our everyday life. So you don’t have to be a super forecaster to benefit from those same practices. You can make better decisions for yourself, for your family, for your colleagues. It’s a good way to approach you know, the way our thought processes are. And yeah, I got a lot from it. I thought it was great. He didn’t actually mention in that clip. But in the episode, he did explain how important the diversity was in getting a group of super forecasters together. Yeah, that’s like six to 12 people and the importance of them not just coming from the same area. The reason that why they outperformed the CIA in a test was because the CIA are all white 50 year old males from the West Coast of America or from a very similar sort of background.

Gene Tunny  42:15

Yeah, East Coast. The old they used to talk about the wasps in the States, you know, from the East Coast, often from the ivy Ivy League schools, so they all went to prep schools, like you know, Phillips Exeter, or whatever.

Tim Hughes  42:31

So the lack of diversity in that regard, held them back as far as like having a better overview or being able to make a better forecast or decision on something. So again, it just showed it was another reason that diversity is such an important part of our build up as humans and you know, to be better as humans make better decisions. And in this case, better forecasts.

Gene Tunny  42:54

Excellent. So just on diversity you because that’s come up twice now, hasn’t it? I’ve got a clip on diversity from Leonora Risse is at RMIT. Leonora is as a former Queenslander. But she’s been doing great work down in Melbourne, she’s involved with the women’s, Women in Economics network, she’s really grown that. And yeah we had a conversation on women in economics in Australia. And we got into this issue of diversity. So Leonora is a Senior Lecturer in Economics at the Royal Melbourne Institute of Technology. So I’ll play this clip from Leonora now.

Leonora Risse  43:36

The issue of diversity, at first glance, it’s about broadening topics, broadening ideas, and broadening the range of issues that are being considered. And then that is really a guard against the risks and the downfalls of what we might call groupthink. If people think the same, you are, by virtue, narrowing your spectrum of potential ideas and potential topics, and then by an extension of that is also the process. So think economics, really an analysis, you know, from identification of the problem, to analysis of the problem to a solution to the problem is a process of interrogation and asking the right questions and deciding on methodologies. It’s all a set of decisions. And what you find in this research is that, that process, you can shortcut it if you all think the same, and you probably just have a standard way of doing things and are less likely to interrogate, you know, are we taking the right decision here? Is there an alternative? Is there a perspective, we just haven’t thought about? Where can we road test this? And if you had that diversity within your pool of minds and brains working on this, you are more likely to engage in those process of interrogation. Now, that doesn’t mean it’s easy this, there’s a quote in the paper to where I found this amazing quote by Justice, the late Justice Ruth Bader Ginsburg. And she talks about dissent, you know, having a different differing opinion. And when they’re, when dissent occurs amongst judges or lawyers, you know, weighing up the evidence, it necessitates a deeper and more robust and more thorough interrogation of the evidence, it forces you to come up with a more convincing argument or to question any assumptions that you may have jumped to. And I love that quote from from Ruth Bader Ginsburg, because I think it has such applicability to economics, where we are, we are weighing up the evidence where we’re making decisions as to what you know, how do we act on this evidence? What gets more weight? What, what do we choose to? You know, what do we judge is good quality or inferior quality? All those all those points of decision making along the way, I think are all ultimately a value based or a subjective choice that we’re making as objectively as possible. But there’s always scope to think, Oh, there’s another way of doing this. So I think the advantage of having diversity of thinking is that it presses for a more robust process. If anyone’s doubtful, then I would, I would say, well, think about the topics that you study, or the the areas of interest that you have, it’s probably been influenced by something throughout your life. So it’s about being shaped by your life experience, which isn’t specifically about gender. It’s just about, you know, those gender, we have gender patterns in our life experiences. And so ultimately, you know, how we operate is a subjective dynamic, because it’s, it’s a function of our view of the world and the bundle of experiences that we carry around with us.

Gene Tunny  47:00

Okay, that was Leonora Risse from RMIT. Tim, what did you think of what Leonora had to say?

Tim Hughes  47:06

I thought it was terrific. It’s right up my street. First of all, Gene, I want to pull you up. You said that Leonora was a former Queenslander and I don’t think there’s such a thing as a former Queenslander…

Gene Tunny  47:18

Aah very good point! That’s a good point yeah exactly. I mean, she’s not living in Queensland anymore, but she went to University of Queensland,

Tim Hughes  47:22

you know what I’m saying?

Gene Tunny  47:24

You’re right. That was poor form on my part.

Tim Hughes  47:28

Ok sorry I just had to point that one out. This was great. And, again, yeah, so diversity of thinking leads to developing robust processes. And it’s so good. There’s so much in there, and it fits in, it dovetails in with so many of the other clips that we’ve just talked about. And it makes sense, you know, the thing that I love about this stuff for me anyway is, like it completely makes sense that accepting of diversity, that necessity for diversity, it’s better. It shows how important it is to stand up for what you think’s right, and to explain why. So that thing of dissent, to push back against groupthink, and all the banal commentary that might come through accepted norms that aren’t good enough, all these kinds of things. And I had to say she didn’t actually mention the quote by Ruth Bader Ginsburg, and I checked it out. And I’m going to read it out here because I thought it was so good. So Ruth Bader Ginsburg said, “dissents speak to a future age, it’s not simply to say, my colleagues are wrong, and I would do it this way. But the greatest dissents do become court opinions and gradually, over time, their views become the dominant view. So that’s the dissenters hope that they are writing not for today, but for tomorrow.” And that’s the thing you know, it needs people to stand up, it needs people to speak their mind, it’s important to listen to hear and you know, not everything is going to be good, not everything is going to make it but you know, by, again, not going with our first what was it? Count? What was that one?

Gene Tunny  49:08

we want to be cognitively reflective…

Tim Hughes  49:11

That’s the one Gene! That’s the one, we want to be cognitively reflective, so not just go with our first opinion, our knee jerk reaction, but to let it settle, give it more thought. And to be okay, listening from places that you wouldn’t normally listen to, I think is a big part of that is so if you find you vote for the red team, listen to what the blue team has to say, in the best possible way and vice versa. And from different news channels, different areas, different people, let it sink in, because it’s quite possible that you can hear something that will land from anywhere. And it doesn’t mean you will agree with everything from that place or person but there’ll be parts of it that maybe should be heard.

Gene Tunny  49:50

Yeah, I think that’s a really good point. Tim, try to genuinely see where the other person’s coming from what their their point of view is. That’s the advice Dale Carnegie, I think it’s it’s been well tested through history that that’s, that’s a good thing to do. So absolutely.

Tim Hughes  50:08

And another thing is to consider, you know, what part you might be playing in groupthink? You know, like, because we’re all influenced by this stuff, whether we’re conscious of it or not. And you could find yourself following or repeating stuff that is just within the group of people you’re with, or political preference, or whatever it is. So be mindful of what you repeat, you know, just blindly I guess that’s, that’s one of the things I get from that.

Gene Tunny  50:32

Yeah. So I mean, I would say that there was a lot of that during the pandemic. So, yeah…

Tim Hughes  50:38

Which is good. So on reflection, this is where, with those, especially when things are heightened in the moment, you know, I guess is how this works. On reflection, we can look back and maybe do a better dissection, you know, with the benefit of hindsight and all of that. But so for instance, with the pandemic as an example, well, the chances are that something like that could well happen again, there’s no reason why it couldn’t happen tomorrow, you know, so, what would we do then? You know, with that benefit of having gone through that, and what would be a better decisions or better decision to make?

Gene Tunny  51:08

Yeah. Okay, Tim, well, I think we’ve got time for one more clip, in this session, I’ve still got the four or five other clips to play, but I might save them for a bonus episode, or for another episode, if we ever catch up. We’ll just play one more clip that’s on this whole theme of critical thinking and, and being cognitively reflective. And it’s from Professor Deb Brown from University of Queensland, who’s in the she’s a philosopher, isn’t she in the philosophy department? And that was someone who, John Atkins, so your friend John Atkins put us on to because she’s been running a project on critical thinking, and was it in the media, evaluating media? Critically, she talks about her Critical Thinking project, yes…

Tim Hughes  51:57

That’s right, with schools, I believe. And yeah, yeah, it was, it looked really good.

Gene Tunny  52:01

So she’s a Professor in the School of Historical and Philosophical Inquiry at the University of Queensland, and we spoke to her early last year. So I’ll play this clip from Professor Deb Brown, Episode 123.

Deb Brown  52:20

And, you know, what we do fundamentally is distinguish between critical thinking, which entails being able to evaluate the quality of one’s own thinking. And so it’s essentially metacognitive. It’s, you know, it’s about, you know, What reasons do I have to believe that, you know, does this evidence stack up, you know, what’s the what’s the contradicting evidence, you know, sort of being disposed to look for not just evidence or reasons that support what you already believe, but actually looking for disconfirming evidence, right, you know, doing doing due diligence in the foundations for what one believes. And we distinguish that from other forms of thinking that that don’t concern that kind of the kind of quality of the foundations for one’s beliefs. So these might be things like, you know, free association, or associative thinking. And that’s very common. And often people mistake that for critical thinking. So, associative thinking is where you’re essentially looking, you know, you’re selecting for information that supports what you already believe. And what you find, then naturally coheres with what you believe, so we all sort of move around the world with these mental models, and the associative thinker, will be looking for things that fit with their mental model. And in you know, in science and in, you know, in other disciplines, this is, this often is connected with what’s called the confirmation bias, right? So that sort of, you know, preferencing, confirming evidence over disconfirming evidence and so on. And it also passes, it also, critical thing is also distinguished from what we call careful thinking, which is where somebody might be, you know, applying rules or procedures, think about, you know, a student in the class, you know, applying their procedural knowledge of mathematics, let’s say to, you know, to derive an answer or value on the basis of the arguments they have, you know, and that careful thinking, people often think they’re being critical thinking when they’re doing that as well. But what’s distinctive of critical thinking, is that critical attitude that one’s, one takes to one’s own reasons, and also to the principles or methods one’s relying on in drawing inferences on the basis of what one understands. So critical reasoning is very much connected up with what Descartes would have called the method of doubt right? Subjecting what one believes, you know, to doubt and, you know, in order to establish a better foundation for, for one’s belief and really being careful about the foundations for one’s belief.

Gene Tunny  55:08

Okay, that was Professor Deb Brown from the University of Queensland, Tim, I thought that was a great clip, I love the idea of thinking about how you’re thinking or metacognition, she really nailed what critical thinking is, and it’s not what you might think it is necessarily. I mean, you might think you’re doing critical thinking, but if you’re just applying a method that you’ve always applied or an algorithm, that’s not necessarily critical thinking, you’ve got to think about, okay, why am I doing that? Is that the right paradigm, the right framework? Is that does that really make sense? What are the implicit assumptions, I think that’s good for economists to do because when we analyse problems, we often go into analysing problems with a specific model in mind.

Tim Hughes  55:53

Again, this was such a good one with, with Deb, and everything she talked about, I found fascinating, because that whole area of like, for instance, to have critical thinking project, delivered in schools makes so much sense. Everybody could benefit from this, but the sooner the better, you know, like, you know, to get these things in as part of your DNA as part of your thought processes. And I think that’s a big part I get from a lot of the guys we’ve just listened to, it’s about the process, you know, what’s your process in, you know, discerning whether something is true or not, or what a good direction is to go in, and what’s a good process. And that’s what these guys talk about. Well, here’s a process that you can use, that’s tried and tested. It can be improved upon no doubt. But it’s, here’s something to go by, because there’s so much bad dialogue in the public forum, where it’s just people shouting at each other or opposing views where, quite realistically, they could probably agree on something that the other person is doing, but because of the party lines, they they have to be opposing and this is quite tedious, you know, to sort of watch, and it’s certainly not a way to come to a good decision. There are better ways out there. And we can employ these individually. And again, like within, you know, for yourself, within families within businesses, you know, with colleagues, here are good processes that are worthwhile going with because they’re better for us as humans, and the better, you come to better outcomes.

Gene Tunny  57:20

Yeah, exactly. Okay, Tim, we might have to wrap up. I’m gonna put links to all of the episodes that all of these clips are from in the show notes. I’ve still got a handful of clips left, but I think we’ll leave that for a bonus episode. There are some others on some other great conversations, but so many great conversations over the last couple of years. aah Tim okay. Yeah, Tim, you did want me to play one clip.

Tim Hughes  57:50

Now you chose this clip, you chose this clip…

Gene Tunny  57:53

I chose this clip that’s right, it’s a good one to finish up on, a good one for your ego so…

Tim Hughes  57:59

No, no, no you chose you chose the clip, it wasn’t about that. It’s about what is said not…

Gene Tunny  58:05

So this is our conversation with Andrew May the Australian Performance Coach, the coach to CEOs on the importance of fitness and business and when, it was funny when he because he hadn’t seen you in years had he Tim? No. And so I mentioned to Andrew about his book Match Fit. And then Andrew makes an observation about speaking of being match fit. So and we’ll just I’ll just play this clip.

How do you go from being a performance coach of the Australian cricket team? If I’m getting that right to coaching CEOs? Can you tell us a bit about that story, please, Andrew?

Andrew May  58:43

Yeah absolutely but before I do, if you want to know about being match fit, look at the guy sitting on your left. I first met Tim 20 years ago, he still looks the same, full head of hair, I’m very envious, so it’s great to reconnect with Tim.

Tim Hughes  58:55

Smoke and mirrors.

Andrew May  58:57

Ok so how did I end up coaching executives and doing mental skills for elite athletes around the world? There was no definitive plan Gene, and a lot of your listeners are going, “What do you mean you didn’t have a 20 year plan?” No. I was a good athlete, not great. I mean I won multiple state championships but never won at the national level, had a scholarship at the IIS in Tasmania. And we moved down to Hobart, which was wonderful in my early 20s. And I just finished studying exercise science. I had a physiology base and then went to the Institute of Sport. And it was a great learning in that high pressure environment. And when I look back, I got to the level I believed I could get to and I believe coaches should coach what they’re good at or what they’ve stuffed up and if you can combine the two you’ve got a really interesting mix. I left talent on the track literally, that any athlete any executive I work with, my real fuel is to help them fulfil their potential. So back to in Hobart, as a runner in Australia you don’t get paid a lot of money. Unless you’re a Craig Mottram or perhaps a Sally Pearson, so I had to supplement my income back then. It’s not politically correct, but I used to walk fat blokes. It’s now called personal training. So the clients I had, that’s Timmy when I met you, when I moved back to Sydney, after I finished down in Tasmania. And Gene a lot of the clients, I were training, they would lose 10 or 15 kilos. And then they’d say, Do you realise I’m not as cranky with my wife or my husband on the weekend, and the kids are not saying I’m an A hole, and I’m actually conscious at their school sport. And I’m not just thinking about what’s going on here. And I’m making better decisions, and I’m more creative. And we’ve opened up this other offshoot in Asia, what have you done to me? And I said I don’t know, just keep walking, don’t drink as much alcohol and keep swimming in the ocean. So I then really started to look into oooh, there’s a link between well-being, physical and psychological well-being and executive performance that was 20 plus years ago.

Gene Tunny  1:01:01

Okay, so wise words from Andrew May there, Tim.

Tim Hughes  1:01:05

Oh, yeah. And, and so just want to reiterate, he was very kind with his comment about me at the beginning. And that’s not the reason that I wanted you to play it. It was very kind, but I just wanted to, because this is the space I’ve been working in myself for the last 17 years and so it’s close to my heart. I’ve known Maysie for many years, even though we’ve been out of touch for quite a few. So it was really good to reconnect. But I just wanted to point out like, I mean, this is one of the areas I think of improvement that we all have at our disposal, which is often overlooked, you know, and that goes back to the pandemic, and all these kinds of things, you know, what can we do next time? Well, next time, the first thing we can do is to get healthier. Now, the healthier the population is, the less devastating anything, any kind of pandemic will be. So that’s like, that’s the first thing I would say. But the link between physical and psychological well-being and executive performance that Andrew was talking about, it’s so true, like we perform better all of us, you don’t have to be an executive or CEO. We’re better when we eat better, and when we move better, and it just makes so much sense. And as far as resilience goes, like with Andrew’s story himself. So he, by his own admission, was a good athlete, but didn’t reach the heights that he he hoped to. But in doing that, like he was able to become a world class coach with what he’s done since then. So he’s, he used that, which some may see that as a failure, ah you didn’t get what you set out to do, you failed. Not at all, like he was able to, it’s a very stoic sort of approach or sort of road he’s taken to say, Okay, well, that didn’t work. Let’s see, why it didn’t work? Or how could it, how could I help that work for someone else, which is what he’s done. And so the research is done on physical and psychological well being helps him, or has put him where he is now as this world class coach. And so for all those reasons, I wanted that to be included. Because I just see that as such a good thing for us all to learn from like we could all the, things he talks about, obviously, there’s there’s detail in there that we don’t have time to go into. But it comes back to the simple things of like, if you can eat well move well sleep well and connect with others, you’re going to tick a lot of boxes that as humans, when we’re going back to one of the earlier conversations about the economy, and in our equitable distribution, all these kinds of things well, just being healthier is, is one of the easiest and at reach, things that we can do. And it’s often overlooked, because we’ve got shiny things, material things that are further away. And these things I think, help us become better humans. And so along with that, the thought processes as well. It’s all part of how we can be better. You know at being, being human.

Gene Tunny  1:03:49

Yeah, just responding to that Tim, the point you make about connection is really an important point. And this is one of the things I really love about podcasting right? And it’s the ability to connect, I mean, like just us having a conversation helps us connect, right? I mean, I’m learning your perspectives. The guests we have on, people I’d never would have connected with otherwise, someone like Andrew May for instance. And you know, being able to get you know, really eminent economists such as David Hendry or Brad DeLong on the show that’s just amazing and then I’ve got listeners who’ll reach out to me with you know what they think and then you know, some of them I’ll, I’ll have on the show even so it’s just amazing for that connection. So that’s one thing I’d say that’s one reason I’m really glad I started podcasting. So that’s connection. The other point I’d make is that yep, since yeah listening to Maysie and also other stuff I’ve been reading, I read his book Match Fit. I read that other great book by Kelly Starrer, Built To Move, Kelly Starrett. Yeah, that’s right, that’s great. And, and since then, I’ve been trying to not just get out of like going to the gym or going for a walk or, or doing some exercise. It’s so easy to go. I’ve just got so much on. I’ve got so many projects on, I can’t find the time. But the attitude you’ve got to have and I think I got this from Laura Vanderkam who in her book, Tranquillity by Tuesday, I think it was, I think it was from her. If it’s not, whatever mentioned is, her book’s worth reading, is a great book regarding how you manage your time. But the attitude you’ve got to have is that working out or exercising, it doesn’t take time it gives you time. I think that’s so true. Because you’re so much more productive, like maybe you lose an hour or an hour and a half even. But when you go back to work, when you go back to the office, you’re really focused, because you could just have an hour a couple hours at work. Like imagine if you don’t take that time, your last few hours at work, you could just be unproductive. You could be demotivated, you could just be checking out what’s happening on on the news? What’s, you know, what’s on YouTube? You know, it could be? It could be, you could just be distracted?

Tim Hughes  1:06:01

Absolutely. And there’s so much good information out there. The big one is prioritising you know, in your diary in your day, to make sure you have time to do this, because most people would say they don’t have time. Well you do, it’s just not a priority, and it needs to be a priority. Or if it is a priority, you’d be better off for it.

Gene Tunny  1:06:19

Yeah. And I mean, I guess maybe it’s easier for me, because I do work for myself. But I guess if you if you’re an employee, then I guess Yeah, go on your lunchtime, or, you know, maybe have a chat with your manager or your boss and say, This really helps me out get makes me more productive. And I’ll stay a bit longer than than I would otherwise. I mean, there are sort of, you know, I think there are ways you can find that time to, to train.

Tim Hughes  1:06:44

Well people like Andrew May are at the leading edge of how this might work in with, with companies. In fact, we’ve got a round two that we have to do with Andrew, we spoke about executive performance for CEOs. Andrew doesn’t know about it yet. But I’m going to email him this week to talk about the impact, of course, on the workforce, you know, which is like everybody else, but it’s a fascinating area, because, you know, quite often, especially with these podcasts, and first of all Gene, congratulations on 200. It’s a huge achievement.

Gene Tunny  1:07:00

Thanks, Tim. Yeah,

Tim Hughes  1:07:02

and you’ve introduced me to guests and areas that I wouldn’t even thought about. And even though you know, like, you said I represent the guy on the street, which is basically, that’s that was an example of we saw that, you saw the value in that diversity, for instance, because as an economist, sometimes you would see things purely through an economists eyes. And so you told me, you you found value in some of the things that I would come up with, I mean, I know, I’ve probably said some crazy stuff. But you saw value in some of the things I saw from a different perspective. So that diversity, just between the two of us was valuable. Yeah, so I appreciate everyone that has been a guest on the show, especially ones I’ve spoken on, because it’s been great, you know, I’ve been opened up to all these different things. And a lot of the subjects or what you would say are outside our control or in the realms of things that are outside our control, which then brings it back to the health perspective of like, well, that’s really very much in people’s control. So it’s something that you can have an impact on.

Gene Tunny  1:08:17

Yeah to a large extent. I mean, obviously, you can have bad luck in your life. For Yeah, for the majority. Absolutely. Okay, Tim Hughes. Thanks so much for joining me on episode 200. It’s been a blast. And I’ll put links to all of the episodes that these clips are from in the show notes. So if you’re listening in the audience and you want to, you’re interested in checking them out, then you can go, go listen, so yeah, thanks for joining us, Tim. Thanks for for being here. It’s been terrific.

Tim Hughes  1:08:43

Yeah no, thank you, Gene. And I want to extend the thanks again to all the guests that have been on the show and to the listeners and for your feedback. It’s been great, and looking forward to next 200

Gene Tunny  1:08:53

Terrific thanks Tim. Righto, thanks for listening to this episode of Economics Explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via Or a voicemail via SpeakPipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it. Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.


Thank you for listening. We hope you enjoyed the episode. For more content like this or to begin your own podcasting journey. Head on over to


Thanks to Obsidian Productions for mixing the episode and to the show’s sponsor, Gene’s consultancy business Full transcripts are available a few days after the episode is first published at Economics Explored is available via Apple PodcastsGoogle Podcast, and other podcasting platforms.

Podcast episode

Sir David Hendry on economic forecasting & the net zero transition – EP198

Sir David Hendry, the renowned British econometrician, talks to hosts Gene Tunny and Tim Hughes about the state of economic forecasting and the transition to net zero greenhouse gas emissions. Among other things, Sir David talks about how to avoid major economic forecasting failures (e.g. UK productivity), forecasting global temperatures after volcanic eruptions, and the role of nuclear energy in the net zero transition. Sir David is currently Deputy Director of the Climate Econometrics group at Oxford. 
Please get in touch with any questions, comments and suggestions by emailing us at or sending a voice message via

You can listen to the episode via the embedded player below or via podcasting apps including Google PodcastsApple PodcastsSpotify, and Stitcher.

About Sir David Hendry

Sir David F. Hendry is Deputy Director, Climate Econometrics (formerly Programme for Economic Modelling), Institute for New Economic Thinking at the Oxford Martin School and of Climate Econometrics and Senior Research Fellow, Nuffield College, Oxford University. He was previously Professor of Economics at Oxford 1982–2018, Professor of Econometrics at LSE and a Leverhulme Personal Research Professor of Economics, Oxford 1995-2000. He was Knighted in 2009; is an Honorary Vice-President and past President, Royal Economic Society; Fellow, British Academy, Royal Society of Edinburgh, Econometric Society, Academy of Social Sciences, Econometric Reviews and Journal of Econometrics; Foreign Honorary Member, American Economic Association and American Academy of Arts and Sciences; Honorary Fellow, International Institute of Forecasters and Founding Fellow, International Association for Applied Econometrics. He has received eight Honorary Doctorates, a Lifetime Achievement Award from the ESRC, and the Guy Medal in Bronze from the Royal Statistical Society. The ISI lists him as one of the world’s 200 most cited economists, he is a Thomson Reuters Citation Laureate, and has published more than 200 papers and 25 books on econometric methods, theory, modelling, and history; computing; empirical economics; and forecasting.

What’s covered in EP198

Conversation with Sir David:

  • [00:02:27] Economic forecasting: are we any better at it? 
  • [00:05:56] Forecasting errors and adjustments. 
  • [00:08:04] Widespread use of flawed models. 
  • [00:12:45] Macroeconomics and the financial crisis. 
  • [00:16:30] Indicator saturation in forecasting. 
  • [00:21:02] AI’s relevance in forecasting. 
  • [00:24:23] Theory vs. data driven modeling. 
  • [00:28:09] Volcanic eruptions and temperature recovery. 
  • [00:32:26] Ice ages and climate modeling. 
  • [00:37:09] Carbon taxes. 
  • [00:40:10] Methane reduction in animal agriculture. 
  • [00:44:43] Small nuclear reactors: should Australia consider them?
  • [00:49:08] Solar energy storage challenge. 
  • [00:54:00] Car as a battery. 
  • [00:57:01] Simplifying insurance sales process. 
  • [01:01:19] Climate econometrics and modeling.

Wrap up from Gene and Tim: 

  • [01:03:23] Central bank forecasting errors. 
  • [01:07:12] Breakthrough in battery technology. 
  • [01:11:18] Graphene and clean energy. 

Links relevant to the conversation

Climate Econometrics group at Oxford:
Conversation with John Atkins on philosophy and truth mentioned by Tim:
Info on solid state batteries and graphene:

Sir David Hendry on economic forecasting & the net zero transition – EP198

N.B. This is a lightly edited version of a transcript originally created using the AI application It has also been looked over by a human, Tim Hughes from Adept Economics, to pick out the bits that otters might miss due to their tiny ears and loud splashing. It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.

Gene Tunny  00:06

Welcome to the Economics Explored podcast, a frank and fearless exploration of important economic issues. I’m your host Gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory, evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode. Please check out the show notes for relevant information. Now on to the show.

Hello, thanks for tuning into the show. In this episode, Tim Hughes and I chat with the legendary British econometrician, Sir David Hendry. We talk with Sir David about the state of economic forecasting, and about the transition to net zero greenhouse gas emissions. Sir David Hendry is co director of Climate Econometrics and Senior Research Fellow at Nuffield College, Oxford. Previously, he was Professor of Economics at Oxford, and before that he was Professor of Econometrics at the London School of Economics. After the interview with Sir David, Tim and I go over our main takeaways from the conversation. Okay, let’s get into the episode. I hope you enjoy our conversation with Sir David Hendry.

Gene Tunny 01:26

Sir David Hendry, welcome to the programme.

David Hendry  01:31

Thank you very much, Gene. Thanks for inviting me.

Gene Tunny  01:34

Oh, of course. It’s a pleasure to have you on to talk about forecasting. So forecasting’s something that Tim and I have been thinking a lot about. And we’ve chatted with Warren Hatch who’s a super forecaster with I’ve also spoken with John Kay, about radical uncertainty and how you deal with that. And I’ve also read your book on forecasting, the one with Jennifer Castle, and Michael Clements, and I thought that was very good. And who better to, to have on to talk about forecasting than someone who has really transformed forecasting and economics, someone who’s had a major impact on forecasting? So to begin with, David, I’d like to ask, how has economic forecasting developed over your career? To what extent has it improved? To what extent are there still areas for improvement? Could you talk to us about that, please?

David Hendry  02:34

So Gene, I don’t think it has improved. I think technology has but the actual practice hasn’t. The time that I got really interested in forecasting was acting for the select committee of parliament that was looking into economic forecasting, after the debacle of Nigel Lawson’s budget and then crashing the economy in the early 1990s. And what I discovered, and acting for them as an advisor, is that 90% of the evidence he got was people actually forecasting and only 10% was looking at how you should forecast, what should you do, what goes wrong when you forecast with no analysis at all? So we started a long programme of analysing what can go wrong in forecasting and why. And once you know that, what can you do about it? Well, obviously, there’s nothing you can do about things that are unpredictable. Right, so the pandemic, unpredictable, forecasters shouldn’t kick themselves because you’ve got it completely wrong forecasting December 2019, for 2020, to discover that it’s vastly different. I mean, the biggest ever fall in GDP in Britain, you couldn’t possibly have forecasted that, that’s not a problem. And we can’t do that, it’s you can start to improve the forecast as you go through 2020. and realise that things are going badly wrong, but you can’t forecast in advance. So we isolated two key features that go wrong in forecasting. One is unpredictable events like that, that shift the data. So data is going along, and then either shifts up sharply, like inflation, or shifts down sharply, like output. But once it has changed, you can do a great deal about it. Some methods now don’t work. And some methods do work. And the methods that don’t work are the methods that stick to what went on before. So they carry on at the same level. And that’s completely wrong relative to the new level. So you have to have very adaptable methods that jump as soon as the forecast has gone badly wrong. You use methods to try to adjust for that. We call them robust methods. Right? So they’re after the shock to GDP. They’re robust. So the Office of Budget Responsibility in Britain, forecasts productivity per decade, completely wrongly, every year, they were wrong for 10 years, if they’d used our methods of adapting because productivity had been growing at about 1.7% per annum up to 2012, and suddenly it stopped, we don’t know why it stopped. But it’s come back to the levels that we had in the 19th century. Point seven. But if you keep forecasting 1.7, we just get massively wrong forecasts all the time, very bad advice for governments. And our methods would have adjusted to that within a year, saying, Okay, it’s changed, it may change back. But meantime, you better forecast along this direction. So the actual, if you like, the forecast errors that people make today are very similar in size to the kind that were being made in the 1960s.

Gene Tunny  05:56

Right. Yeah, that’s a that’s a shame. I should I forgot to introduce Tim. Tim, do you have any questions for Sir David on that?

Tim Hughes  06:04

No, it’s, it’s interesting. I mean, this isn’t my level of expertise. I’m here as the layman in this partnership with Gene. So I tend to look at things from a macro view and more from a guy on the street sort of perspective. But I’m really interested in that when you say that, well, for instance, it hasn’t changed since since the 60s. What’s the delay in the take up of these modelling systems for government?

David Hendry  06:27

Well, one of the reasons it hasn’t changed is that the frequency of large, unpredictable events hasn’t changed. And they’re very common and much more common than people realise, except to see the pandemic has been, Oh, quite unusual. Of course, we’ve had lots of pandemics, some of them happened like SARS too, not to go anywhere. Others like the COVID have gone everywhere. Inflation in Britain in the 1970s. It’s very similar to what it is today. And for very similar reasons. Now, I think a lot of forecasting that you hear about comes from central banks. And that’s the kind of forecasts we can analyse because they’re made to publish it. We don’t see the forecasts within many major institutions like JP Morgan, or Citibank, or whatever, they tend to keep them to themselves unless they do really well, in which case, they tell you oh, we were doing really well. But when you look at Central Banks, say we take the Bank of England as a paradigm, their model collapsed with the financial crisis, it just fell apart, and they said it fell apart. So we started to build a new one, we pointed out to them why it had fallen apart. They’re using a method of mathematical analysis that works fine if things don’t change, but becomes like navigating around the globe using Euclidean geometry when things do change, that just, it just doesn’t apply. And its widespread use has been a disaster in my view, for macroeconomics, and is the reason so much of it has gone wrong, because it assumes that the method that these models are built on assumes there are no sudden, unexpected large changes. Whenever they occur, the models fall apart. And we had a letter recently in the Times saying the bank should try testing their models from the 1970s. And they would find it’s a shambles. It doesn’t work at all. Because the 1970s in Britain was filled with crises, 3 day weeks, IMF coming in, interest at 25%, inflation, etc. And their model just wouldn’t cope with that. And we’re now in is not quite such a bad situation, but we’re now in a similar sort of situation where a wage price spiral is kicking in, these models don’t have wage price spirals. They didn’t allow for the fact that people had saved a great deal during the pandemic, because they couldn’t spend it wasn’t, it was forced saving if you like, and as soon as the pandemic ended they started spending, the supply side had improved to meet this high level of expenditure. So of course, you have all these factors coming and they’re not in their model. So naturally, the model was A they said inflation wouldn’t go up and B they said when it did go up it would be transitory, whereas we were saying, it will go up and it will not be transitory, it will be very persistent and very hard to dampen down.

Gene Tunny  09:24

Right. So this is a letter in the Times I’ll have to have a look for that. That sounds interesting. And it’s a bit of a concern that the Bank of England hasn’t improved its, it doesn’t sound like it’s improved its models very much at all, because in 2010, so you gave a talk to the Institute for New Economic Thinking, and you were talking about the problems with the models that central banks were using. And this was in your conclusion, you said that “there are huge costs to underspecified models and I think the financial crisis is partly due to central banks having very badly under-specified models in their repertoire.” Would you be able to explain what what you meant by that? Is that what you’re talking about here? They’re not allowing for structural breaks. But are there also are there variables they’re not including? Could you just unpack that a bit, please?

David Hendry  10:16

Yeah, there are variables they’re not including and often including variables in the wrong way. So for example, the Bank of England includes wealth. Now some wealth is expendable, like your house, some wealth is potentially spendable like money invested in stock markets and bond markets. In some it’s very spendable, which we call cash, deposits and demand at financial institutions. And it makes a huge difference, to break these up, because wealth itself can change a lot but it doesn’t change expenditure because house prices go up, or house prices go down. But it can also change a little bit and hugely changes expenditures because people run out of money, they have to start borrowing, and they haven’t got time to sell their house or the bond markets in disarray. And financial markets have fallen hugely, and you don’t want to make big losses. So you need to think very carefully about how you include variables in models, as well as which variables to include in models. I was referring to the fact that the housing models in the US when the financial crisis started, were very weak, they didn’t cover all the aspects that that matter, because in some States, if your house price falls greatly, and leads to a large indebtedness, if it was sold, you can just hand back your keys and walk away. You can’t do that in other States. And the subprime crisis generated articles, even from central banks, saying that it’s really important to get poor people onto the housing market, because that’s where how you build that wealth, of course that led to all sorts of speculation, and then house prices crashed. And that’s poor people who end up suffering most and we got a very bad financial crisis. But you guys didn’t have it. Right Australia avoided it, because it hadn’t got engaged in quite such nebulous activities as the AAA assets that were worth nothing.

Gene Tunny  12:16

Yeah, yeah, we avoided it. I mean, partly because of mining. And then the Treasury and the government here, they would say that they had a timely fiscal policy response. I mean, there’s debate about the extent to which that was relevant. But yeah, we were we were lucky. And maybe we hadn’t had as much crazy financial activity as in the States and Britain. We’ve got our regulators too. So yeah, a variety of reasons. But yeah, that’s, it’s fascinating.

David Hendry  12:46

I was gonna say, the way macro economics is taught in almost all major universities around the world still relies on this approach of believing agents optimise across time into the future. And you can’t do that in a world in which you suddenly get big shifts, right? You’re what looked optimal one day becomes a disaster the next, for example, Royal Bank of Scotland trying to buy this Dutch Bank looked optimal to them in the state of the world before the financial crisis and did become an absolute massive disaster after it. And that isn’t something that’s taught in macroeconomics courses that I know off.

Gene Tunny  13:29

Yeah. Yeah, unfortunately, a lot of the macros become very mathematical. And you’ve got all of these forward looking models, these Ramsey type models, and yeah, but I wonder about the just how applicable, they are. So good point. Can I ask you about your methodology David? So you’re famous for having promoted this general to specific methodology, if I’m getting that right. Could you just explain roughly what that is and how its implemented and what the modern implementation of it is? I mean, you’ve got this automatic forecasting system. Could you tell us a bit about that, please.

David Hendry  14:11

The whole idea started in the 1970s, when it was quite clear that the then big models in the US and Britain didn’t really incorporate enough information. And if any, if you leave a variable out of a model that matters, say you didn’t include housing in a macro model, and suddenly you get a big change in house prices, the model will go wrong, because it should be, housing should be in the model, and it’s not there, and it shifts and that then shifts the reality relative to the model. So it became clear you needed to think very hard about all the things that might matter. And that then required you to put statistical method that could discriminate between what does matter, and what you thought might matter but does not matter. And so we had this paper in the mid 70s, on the consumption function in Britain, showing that you could explain everybody else’s consumption function failures by a more general consumption function that pointed out why they went wrong. And that led us to develop this general to specific as a very general approach. Now, it evolved greatly in terms of, as we realise, more and more the importance of shifts and outliers in forecasting, we began to develop these methods, which at first, I have to say were greeted with not scepticism but total disbelief that you could do it. So that to take the basic idea. Say you’ve got a relatively short time series that’s got 10 observations. And you think that within those 10, there might be a discrepant observation, somebody wrote down 10, when he meant one, right? You just fit the model to it, or it’ll go very badly wrong. So what we do is we create an indicator variable for every observation. So it’s one for that observation and zero elsewhere. So you get 10 of them. And you put them in in big blocks, say five, and then the other five, and they won’t do anything, if there is no shift, but they’ll pick up the shift when it happens. And we call this indicator saturation, because you put in as many of these indicators as observations. Now why would anyone think of doing that? Well, it was serendipitous. I was asked to participate in an experiment in econometrics, to model food demand in the United States, from 1929, which isn’t a great date to start, any time see, through to 1986. And I looked at what everybody had done, and they had all thrown away the data before 1946, they couldn’t model it. So I built a model of it and looked what had gone wrong in the interwar period, and discovered there were two gigantic outliers in I think, 1932 and 33 but don’t guarantee that it could have been, but round about that period. And Mary Morgan kindly went to the archives and discovered, guess what, the US had a food programme? Well, will a food programme affect the demand for food, you bet it will. So I put in indicators for those observations and immediately got a very good model for the whole period, for the period up to 1946. So then I thought, right, let’s fit the cost period, including the early one. But we’ll put in indicators for all the observations, which is the kind of forecast test and found the Korean War I think had one big outlier, but otherwise, it was fine. And then about a year later, thought that’s funny, I had put in indicators for every observation. All the ones for the pre war period and all the ones for the post war period. And it had worked, I got the best model of anybody. So I started talking to Soren Johansen, a famous econometrician statistician, he said, “You’re nuts. You can’t do that!” And about a month later, he emailed to say, “Yeah, I think you can do that and I think I know how to analyse it,” which because if you don’t analyse it in economics, they just ignore it. And so we published several papers showing detailed analytics of why it would work for impulses, we then extended that to steps and then trends. So we can pick up trend breaks, step breaks etc. So for our 10 observations, we might end up with 40 variables. Most statisticians look at you, you’re nuts. But actually, you can show it will work. Because if there’s no break, no trend, they’ll all disappear. If there’s no step shift, they’ll all disappear. There’s only one outlier, you’ll be left with one outlier. And that’s it. So that’s how we do general to specific now. And that’s why you need automatic modelling. Because a human can’t do that. The number of possible things is far, far too big. The computer programme can of course, do it in seconds, at worst, maybe minutes if it’s a huge data set, because it’s got many, many things to look across.

Tim Hughes  19:27

Actually, this probably feeds into one of my questions for you, David, which was, you mentioned about the modelling and the mathematics, and the current uptick in artificial intelligence, in AI, is that something that has made a big difference with the work that you do?

David Hendry  19:45

Now our programme is a sort of AI programme date back a long way. Because it’s experimenting with everything. It’s a programme that’s designed for data that keep changing. Most AI programmes are not. Most AI, it takes all the cases and trains the computer to identify things in those cases. But if the cases suddenly change, that’s not going to work. And so AI has itself, the way people have used, it has not made a big impact on forecasting yet. They have to adapt AI to learn from the data, and be ready for it to be adaptable into the future in a way that if you were trying to teach a programme to identify measles, you probably would just take all the cases of measles and the programme would be able eventually to look at the spots and say, Yeah, that’s measles. But if Measles can suddenly evolve, as say the pandemic did, what you’re trying to pick up by AI would no longer be relevant. It would look different, and AI would misclassify. So AI has got to be hugely changed to be relevant for forecasting, which is about a changing world. We’ve got climate change, we’ve got pandemics, we’ve got wars, we’ve got crises, we’ve got inflation, we’ve got changing population levels, etc, etc. Unless it can adapt to that it won’t be useful in forecasting.

Tim Hughes  21:15

In your view, do you think that that is quite likely that AI will get to the point where it will be more predictive and not just reactive?

David Hendry  21:22

Well, we’ve shown you can do it, ours is very simple AI, it’s nothing like the kind of complicated neural networks that are being used in some areas. But it does show that you can do it for forecasting, and it does matter. And in the M4 Forecasting competition, which was run from Melbourne, the AI ones or machine learning, as they were then called, did not do terribly well. We came seventh in our very simple one. And and it turned out that we spent about a 50th of the time that most of the other teams did.

Gene Tunny  21:57

Was this of a motorway was it was at the M4 motorway?

David Hendry  22:01

no no. M for Makridakis fourth forecasting competition. The M four we’re now at five. It’s currently ongoing. Makridakis is a Greek forecaster, who decided the only way to improve forecasting is to find what worked. So he asked people, here’s 1000 time series, we’re not going to tell you what they are, model them, and send us your forecasts for the next 10, 20, 30 observations. we’ll analyse those and see who did best. So at the M4 there was 100,000 time series to model. And you then have to forecast I think, up to 20 years ahead for some of them. And you’ve to send in all your forecasts. And they then worked out who did best and got closest to the actual numbers in the future. Actually Uber did, Uber won the competition, Uber, yeah, the car hire people got algorithms of the kind that could be applied to forecasting. But what they did, we think was accidentally wrong, that they looked across, say, nobody knew what the time series were. But it does turn out that some of them were, say, GNP from 1950 to 1980, and somewhere from 1990 to 2010. Right. Now, they looked across, do some series help us in forecasting other series. And we think they actually included the future of the series they were to forecast in the, seeing if these series helped it, which is why they forecast much better, because we’ve mimicked their method, when all the series are completely independent, and it doesn’t help. So they had to be doing something like that accidentally, I don’t think they realise that, some of the series where the future of others of the series…

Gene Tunny  24:00

Okay, yeah. Can I ask you a question that’s related to that? It just reminded me, because you were saying that they don’t tell you what the data series are. Now. There’s this debate about, well, to what extent do you use theory and you’re modelling, you know, theory driven versus data driven, is it the case that you can get a reasonably good forecast without any theory whatsoever or without any understanding of what the underlying what the data are actually measuring? Or do you need theory? How do you think about the role of theory in your modelling? David?

David Hendry  24:33

Well, when we were forecasting week ahead Covid deaths and cases in the UK, the model only used the past data. And for the first six weeks we were by far the most accurate forecasters relative to epidemiologists with their big models and taking account of whether, you met people who had it and all the rest of it, and that’s because their models needed about 10, 12 weeks of data before they even began to be useful, whereas we could forecast immediately without any theory. I mean, I understand the big models and why they work, but we thought you can’t use that. And it’s because the way COVID hit, it did big jumps, measured on very few cases. And suddenly, like Bergamo, you had 50 people dying in a day, right? And so you get these big jumps and our methods adapt rapidly. So in that area, you could do extremely good modelling without theory. But when it comes to economics, how many variables are there? 5 trillion, possibly in the economy, if you think of everything that’s going on, so you have to have some theories and say, well, most of those don’t matter. We just can’t deal with that. So we use a lot of theory in our models, but we embed it in the general. So say you have a theory, let’s take a very simple theory that only income causes consumption, consumers spend their income and that’s it. So consumption is related to income, period. Okay, we keep that and embed that within a model in which things like well, maybe interest rates matter, maybe wealth, maybe liquidity, maybe, etc, etc, etc, matter. But when you’re searching, you don’t search over the relationship between consumption and income, you always keep it there. And if it doesn’t matter, then it will turn out to have a very small coefficient, and you can decide to drop it in the end. But if it does matter, and it’s the only thing that matters actually our method will give you the same answer as your theory model. So we embed theory in such a way that if it’s correct, that’s what you will get. And if it’s wrong, you’ll get a better model. So it’s both theory driven and data driven.

Gene Tunny  26:53

Okay, we’ll take a short break here for a word from our sponsor.

Female speaker  26:58

If you need to crunch the numbers, then get in touch with Adept Economics. We offer you frank and fearless economic analysis and advice, we can help you with funding submissions, cost benefit analysis studies, and economic modelling of all sorts. Our head office is in Brisbane, Australia, but we work all over the world. You can get in touch via our website, We’d love to hear from you.

Gene Tunny  27:27

Now back to the show.

And so you start off with a very general specification, lots of data in your database, lots of variables, if I’m getting this right, and then you allow for the potential potentially all of these structural breaks things where things go a bit crazy, you jump up to a new level, like during the pandemic or, or whenever, and we we dropped down from the trend growth path we were on maybe we were cycling around it and suddenly we’re somewhere we’re in this hole and so you’ve got models that can adjust for that sort of thing is that if a fair way of thinking about it,

David Hendry  28:05

Yes, it’s post, yeah. Yes. Once it’s happened, then the model will pick it up. So quite a good example that might intrigue you is finding out where all the volcanic eruptions occurred over the last 1000 years. And when the when one of my colleagues gave a paper in our methods at our General Environmental Conference, all the volcanologists were intrigued and asked us can you use these adaptive methods to show where volcanoes were and measure how they work? Well, the answer is yes, we adapt our methods instead of them being one zero zero. They’re kind of like a V. Because when a volcano erupts, the temperature drops immediately. But it then recovers roughly half a half a half a half of what’s left. So the V shape picks that up. So we found all the volcanoes from 1200 AD in the data set of tree ring growth dendrochronology. And the key thing about that Gene is as soon as you’ve got the first observation of the volcano erupting and the drop in temperature, you can very accurately forecast all the remaining observations to the recovery, by having this V shaped go half a half a half. And we showed we could forecast, okay, forecasting after a volcano in 1650 isn’t all that interesting today, but it tells you that the next time we get a world explosive forecast like Tambora or Krakatoa, we will be able to tell the world after it’s stopped erupting, how quickly the temperature will recover to the previous level. It also lets us adjust the so called baseline temperatures that IPCC use. That in fact have been several quite big volcanoes that have dropped the temperature a little bit for a few years, and that actually means that they’re cheating by using a slightly improper average over the periods they’ve picked, as they shouldn’t include the volcanic eruption, right? Because that’s when you should use the natural level that had been over that period overall, if that makes sense.

Gene Tunny  30:22

Yeah, that’s fascinating. And so your, so is that an application of your method? Or are you, the point you’re making about the volcanoes there? Or are you saying that you can apply some theory to get a better forecast? I’m just trying to understand

David Hendry  30:36

It’s our methods purely. And it’s just the knowledge that when volcanoes erupt, the temperature falls, but it goes back again. So the question is, what form do you use for that? We just invented one that says V shape, and then we put in a V for so, it’s just over, I think we had about 900 observations. So we’ve put in 900 of these Vs in big blocks, but it only picks up a significant g if there was a Volcanic Volcanic eruption, right, because otherwise, it doesn’t help fit the model. So it then just picks up all the volcanic eruptions, and the volcanologists started using this method, we’ve done one to get a new archive of volcanic eruptions since zero, like 2000 years ago,

Tim Hughes  31:24

Actually this probably leads us on, do you have anymore questions Gene?

Gene Tunny 31:30

No not at the moment, go ahead Tim.

Tim Hughes 31:33

David, I was gonna ask you about climate econometrics. So you’ve written a book on that with Dr. Jennifer Castle. So I was interested to see exactly what climate econometrics is, and how it might be able to help us tackle climate change.

David Hendry  31:44

Yes, climate change is caused by our economic behaviour. All our methods were developed for modelling economics. So it would be quite unsurprising that they would work from modelling climate change, which is due to economic behaviour, CO2 emissions, and nitrous oxide emissions, the way we travel, the way we live, the way we eat, the way we warm our houses, etc. All these things are economic decisions. And so if the methods work for the economic behaviour, they’ll work for explaining climate, they’ll actually also work for claiming, explaining things like ice ages, even though there’s no humans around then, because they, the kind of dynamics of ice ages how the amount of co2 in the atmosphere, the amount of past sunlight falling on the Earth, that’s created the temperature, the amount of ice that’s around, etc. All of these carry forward into the future and there’s really good data on ice ages, I mean, 800,000 years of pretty accurate data and how it evolved. And we can fit our models to that, again, very general. Now, why would you want to put in indicators? Well, of course, there’s often a lot of dust in the atmosphere. And dust falling on ice turns it black, which turns up the amount of heat that absorbs. So if you have a period of massive volcanism, which does occur, I mean, often you can have 50 years of vulcanism puts up so much dust, it actually changes the pattern, and you can pick that up, and the sudden jumps in temperature that were unexpected, for example. So it can be applied to all these issues. We’ve been applying it to modelling ‘How well is the UK doing in getting to net zero?’ Now we were at a particular point that we had very good data on all the ingredients that lead to CO2 emissions, the amount of coal, which was huge in the 19th century and up to about 1970 was pretty large in Britain, but then began to drop dramatically, because it became inefficient relative to other sources, but also because it was banned in household fires. When you were not allowed to have fires based in smoky coal because smoke, so you get the demand for coal falling, and that led to the discovery of natural gas in the North Sea. Prior to that the gas system was coal gas, which required you to burn coal to get the gas but it’s very inefficient so that got rid of coal and natural gas is much more efficient. And oil was throughout beginning to replace the use of coal in many industries particular. And then in 2008, the government banned it from being used to produce electricity. And that’s the death knell for coal in Britain’s there’s almost none used nowadays. Now, 2008 is something The Climate Change Act of 2008 amazes many people, both parties unanimously voted for it as did the Lib Dems is completely we need this, let’s do it. And you get a huge, very rapid drop in the amount of CO2 emissions in the UK. Now Britain’s been moving towards a service economy from a manufacturing one. But it hasn’t been doing that to get rid of CO2. It’s been doing that because World Trade Organisation rules meant you couldn’t put extra taxes on people who are cheating in the way they were pricing their products. And so they killed off a lot of British industry. So I don’t accept that the offshoring has anything to do with climate change and claim that our domestic reductions. So Britain’s come down from 12 tonnes per person per year to four and a half tonnes per person per year over that period, which is a very dramatic reduction. America is still at 15. So it’s still above the highest it ever was in Britain. And one of the explanations we came across recently is that in Britain, cars went about 20 miles to the gallon in 1920. Now on average, they’re going 55. In America, they went about 20 to the gallon. And now they’re going about 20 to the gallon. And there’s many, many more cars, and they’re driving much further. So they’re consuming vastly more oil, and therefore gasoline. And therefore pumping out much more CO2, nitrous oxide, particulate matters, etc. They’ve had no efficiency gain, whatever, because they’ve gone for these bigger SUVs, much heavier, much bigger engines and petrol, gasoline has never been taxed in the US, whereas in Britain, the tax is about two thirds of the price of a pump.

Tim Hughes  36:54

Yeah, it’s expensive. Yeah, it’s a lot.

David Hendry  36:57

Yeah, it hasn’t discouraged people from driving. Right, people are still driving, there more and more kilometres on aggregate in Britain driven every year despite these high taxes and gasoline is one of my reasons for believing that carbon taxes will not by themselves solve the climate change problem. We need technology we need to adapt until we’ve written several papers, proposing a system of what we call five sensitive intervention points. That can be used to exploit how people behave without trying to change their behaviour, but to make them do things that will then be climate optimal. So for example, cars in Britain last nine to ten years on average, and then become obsolescent. So instead of buying another internal combustion engine car, price electric cars so that they automatically move over and buy an electric car. And if we did that, over the next 30 years, we’d end up with every car being electric, and nobody having suffered and have got the new car that they wanted at each point in time, but switching over gradually. But that requires you to be providing more electricity all the time to meet this, which requires upgrading the grid and installing more wind farms or solar cells, and maybe more small nuclear reactors and perhaps investing more in fusion in the hopes that the current breakthroughs can be made useful for society before 2050, and so on. And the paper tries to spell out how all these steps interact all the way down, clean right down to farming, how we get rid of the massive amounts of nitrous oxide, methane and even CO2 to come out to farming. That’s a huge concern in New Zealand, your neighbouring country, poor farmers, they’re objecting to fart tax. I don’t blame them. I mean, so how can they deal with it? Right? It’s, it’s not like you can deal with the tax when cars were getting more efficient when they’re driving less or getting an electric car. They need to think of the technology that will reduce methane emissions from animals. I don’t know if you know that there’s an island off Orkney called North Rolandsay, where the sheep are not allowed off the Shore, there’s a wall around the island and all the sheep are kept on the shore, and they eat seaweed. But methane…

Tim Hughes  39:25

Yeah, I heard about this recently. And because I was going to say I agree with what you say about technology, making these changes. So you know, rather than forcing people’s habits to change or you know, doing something drastic with our food chain, etc, the technology will contribute towards those changes. And yes, I saw that the seaweed or additives made from the seaweed could be one of the solutions for for methane. So just by adding it to the food. Obviously, it’s early days to see if that may or may not work on scale. But it’s encouraging It is encouraging to see those breakthroughs.

David Hendry  40:02

I think the breakthrough that’s needed is to synthesise the chemical. that does it. Because I don’t think you can grow enough seaweed to feed all the world’s cattle, sheep, goats, etc. I think that’s not on. But knowing that asparagopsis taxiformis, which is the one that seems to be best for stopping the thermogenic reactions within animals, it could be synthesised in the way that aspirin was taken from willow trees, and then Bayer worked out how to synthesise it. And I think these these things are possible. So yeah, I mean, our paper suggests that all of it is possible. Some of it needs subsidies, I don’t think tax is the right way to do it. Because we saw the uprising in France from the yellow vests. And that’s happened in Sweden, people object to their lifestyle being disturbed. This doesn’t disturb their lifestyle. It just says, oh, you know, you’d be better off if you do this. And then you can keep manufacturing going making cars but electric cars and wind turbines and solar cells and heat pumps and so on. All of it’s out there. And the thing that I do emphasise when I meet sceptics is by the end of the 19th century, we had cars that were electric with rechargeable batteries that could go up to 50 miles between recharges. We had solar cells, on roofs, we had wind turbines that were being used on farms, we knew that climate change was caused by excess CO2. And everything was in place for an all electric society, we knew how to generate it from hydro power, from wind power from solar power. But then the Americans discovered oil and the internal combustion engine. And that

Tim Hughes  41:54

So that technology was there at the end of the 19th century. You’re saying?

David Hendry  41:57

At the end of the 19th century, all of it was there. And we trace who invented it, how they invented it, how it developed? Yep, it was all there. Not LED lighting, that’s an important, more recent development.

Gene Tunny  42:12

Yes. Can I ask about that? That paper? I’ll have to look it up. It sounds fascinating. So have you you’re you’ve done modelling, have you of this path to net zero for Britain? Is that what you’re saying?

David Hendry 42:20

That’s what we’re saying yes

Gene Tunny 42:24

Okay. And yeah, it’s feasible. If there’s this technology, some technology shifts, technological improvements, but also that there may need to be some subsidies for electric vehicles, I think, was that what you were…

David Hendry  42:37

For the electric vehicles, but also for the grid. You need a massively improved grid, both because there’s vastly more electricity, but it’s got to be more resilient to climate change, because climate change is going to happen. Irrespective, even if we managed to reduce everywhere, it’s still going to carry on for a long time, because the oceans and the air have got to calibrate the temperature. And that takes a long, long time to happen. So sea level rise will continue, the Earth will continue to warm but at a slower and slower rate, if we stop pumping out quite as much CO2. And obviously, if we can find ways of extracting it, to research that, that would help. One of the things that does extract it, believe it or not, is basalt. Stuff that volcanos erupt, right? Now, if you look at photographs of volcanoes, the land around them is very fertile. So you can actually replace artificial fertilisers by ground up basalt. And that will act as a fertiliser, because it’s got all the minerals in it, but it also absorbs CO2. So it actually helps reduce CO2 whereas artificial fertilisers in making it they produce CO2, they produce nitrous oxide, etc, etc. So one of our proposals is that we start switching quite rapidly to using ground up basalt which costs next to nothing. There’s 300,000 cubic kilometres of basalt in India. That’ll take a long time to use that up.

Gene Tunny  44:12

Right, I’ll definitely have to check this out. I mean, this is a big issue for Australia. How do we get to net zero? And I mean, Britain’s probably got some advantages over us, you, you don’t have as big an area. I mean, we’re gonna have to build all of this transmission to connect up the renewable energy. Like we don’t have nuclear energy here and the Opposition party is trying to push it, but then I think there’s going to be a lot of community resistance to that here in Australia.

David Hendry  44:37

Yeah, I can believe that. But do people understand small nuclear reactors? That’s the only ones we’re arguing for, not the big ones, the small ones. In Britain, lots of big ones. And they’ve produced a lot of transuranic waste, that’s going to be a huge problem for humanity. Now, there are two advantages to small nuclear reactors. One they can use that transuranic waste as their fuel and greatly reduce the amount of radioactivity that needs to be dealt with from it. And secondly, they’ve been used in nuclear submarines for 50 years, and there’s never been an accident. So they’re very safe and they don’t have any fissionable material that terrorists might want for bombs. I mean, the stuff they’re using is useless. Other than burning up the waste, it’s a problem anyway. So if the public knew that these are harmless, that they’re getting rid of a problem, you don’t have nuclear reactors, so it’s less of an argument there. But in Britain, people would jump at the chance to cut the amount of nuclear waste that needs to be disposed of, burying it or put it in deep caves, etc. And these guys can do it

Gene Tunny  45:52

Right Yeah. These are the small modular reactors, are they?

David Hendry 45:56


Gene Tunny 45:58

I think that’s what Peter Dutton, who’s the Opposition leader here, what he’s talking about,

David Hendry  46:02

Oh good for him, I think they are actually an important component, but only one possible component of an electricity provision, that would give more energy security. And, and be something that can work in almost all circumstances.

Tim Hughes  46:18

This is an area that we’ve talked about a few times, and one of the things that comes up is that the most likely scenario would be to have a suite of different options as to where they get the power from. So for instance, we’re very lucky here in Australia, we have abundant supply of sunshine. And so that’s clearly one of the options open to us, which we currently use, and it will grow. But there’s also hydro, there’s wind, there’s other options and having the various different things available. So that for instance, I mean, I know in the UK, for instance, like to rely on solar isn’t something that you’d want to rely on fully. So it would be the same everywhere I imagine and that that those suite of options or those suites of available power supplies would be different around the world. But it does seem to be that a lot of this is driven by the market, which we’ve noticed here and it has come up in conversation, which is that’s that seems to have been a big change, where that it’s been widely accepted that climate change is real, and that most people do want to have clean oceans, clean atmosphere, clean fuel. And so that driving force from the market, seems to be also then instigating the technology from the suppliers of those options, you know, people like Elon Musk, or, you know, these, these people who can make things happen very quickly, much more quickly than governments can. So it seems to be accelerating and going in the right direction. And so the net zero target is 2050. I think, is that right for the UK?

David Hendry  47:51

That’s right, it’s too far in the future. But we’ve picked it because the costs of adjusting to it are near zero, and probably even positive benefits from doing it slowly, in terms of machinery running out, cars getting obsolescent, trucks needing replaced. Developments, I mean, in solar cells, for example, Perovskite cells are now able to produce 30% of the energy from the sun as against the standard solar cells 22. That’s an enormous improvement. And that technology will take a little while to get commercialised and applied. And then people will have much, much more efficient solar cells to put on their roofs.

Tim Hughes  48:32

And the infrastructure needed for electric vehicles is obviously going to be enormous, especially in the built up areas. So it’s going to take some time for it all to happen.

David Hendry  48:44

Absolutely, but if the market prices correctly, it can be profitable for them to instal all the connections, it doesn’t necessarily cost much in the same ways they built filling stations. I mean, they didn’t build them for fun. They built them to make a profit to build these guys to make a profit as well.

Gene Tunny  49:03

Yeah, there’s some big issues here. Tim, one thing I would say on the solar I mean, even though we’ve got abundant sunshine, the challenge is, it we’ve got to store that solar energy for when it’s actually because yeah, that’s one of the problems because you don’t have it at night and yet there’s a big peak in demand when everyone gets home from work. And yeah, that’s why we’re having to build hydro where the State government’s here is investing heavily in hydro and trying to progress some couple of hydroelectric plants quite rapidly, which is, which is what you need to do so

David Hendry  49:33

Yeah, Britain’s rethinking hydro again, taking the Great Glen and converting it to a massive lake to reservoir to bag more hydro and Norway has always produced most of its energy from hydro. The first ever house to be lit by electricity was driven by hydroelectric. Armstrong, the gun manufacturer, built a hydroelectric system for just providing his house with electric lighting. That’s the first in Britain. So that’s part of the 19th century that we could have got an all electric world. And storage is a big problem Gene really is. And we’ve recommended using nighttime much later nighttime surplus energy to produce hydrogen. There are several methods, let’s not go into them parallel assistant electrolysis and creating liquid hydrogen, right. And liquid hydrogen is a fantastic storage of power. Okay, and you can use it either for heat, or to provide the electricity that you don’t have otherwise, indirectly or as a high heat source for industry if we’re going to get rid of coal and oil, they’ll need a high heat source. Well, you just see NASA’s rockets taking off and you realise you can get a lot of heat using liquid hydrogen mixed with oxygen

Gene Tunny  50:56

And so do you think that could be commercially viable, we’re trying to build a hydrogen industry here, not me, but the state government and the industry. And I know the Japanese are very interested, Mitsubishi and companies like that they’re looking at, they’ve got all these exploratory projects up and down the coast here. I mean do you think it could be commercial?

David Hendry  51:16

Yes, definitely. I don’t know what the cost to steel makers is of their energy provision. But if the hydrogen is made from the surplus energy at night, from things that wind turbines, which often have to be switched off, because you’re producing electricity that can’t be consumed, but it will always be able to be consumed from making surplus hydrogen, that’s our surplus energy for making hydrogen. And the cooling will also require a lot of energy. So I think it could actually, they could actually end up paying people to make hydrogen. Right to stop the wind turbines being turned off when a large percentage of your electricity is coming from wind turbines. And it’s coming at night, when you know three in the morning, the demand is zero. So I think there’s strong possibilities of using that.

Tim Hughes  52:10

That’s good battery technology is really another area as well, of course that is is going ahead. I was just trying to remember the name of the technology, I think it’s single cell batteries. If that sounds right. But I know Toyota, for instance, have invested a lot of money in this next generation of batteries. And it’s been talked about in the realms of that there will be sufficient enough in a car that you’ll be able to power your house from your car. So it’s that kind of capability that is being expected. Remember John Atkins, mentioned this in one of the

Gene Tunny  52:42

Yes, we might have to go back to that Tim and have a look and put some links in the show notes.

Tim Hughes  52:49

It’s a thing, it’s a thing. I didn’t make it up, I promise.

David Hendry  52:51

Okay, so you have to remember that using that kind of technology, a glider went around the world. Right? Yeah. It was a glider, but they did do it, and Britain has several electric aircraft for short distance travel, which are all electric. And trains. I think both Germany and Britain have been developing trains that ran off fuel cells of the kind that are driven by hydrogen. And they do produce enough electricity. But at the moment, the machines that do it are enormous and very heavy. So they have to find some way of producing fuel cells that work from much less expensive and heavy technology. But why not have solar cells in the roof of your car? Well, at the moment people would rip them off, of course, thieves would just take them, but they become ubiquitous. That may be one of the routes that we could do. Another as you mentioned, Musk, at one point, Tesla put up a video on the car being the battery. And they used graphene tubes filled with electricity all the way around the car, and then it provided enough electricity to drive the electric engine for 1000 miles. They dropped the video very quickly. And we don’t know if they dropped it because it was giving away secrets of they didn’t want or it didn’t work to try to match it didn’t work. I think it should work. I can’t believe that I mean graphene is a super capacitor can store enormous amounts of electricity. And there must be a way of using it and making graphene has now become very straightforward. You can take waste plastic, it was a laser and you turn it because it’s a carbohydrate you turn it into carbon and the carbon can be turned into graphene. Just pick up the tiny bits and join them the way that they originally discovered graphene in Manchester.

Gene Tunny  54:57

Incredible. That’s just incredible what’s going on, David we’ll have to put, I’ll put a link in the show notes to your research group on climate at Oxford, isn’t it. So I’ll put a link in because there’s links to all sorts of great stuff you’ve done and all great articles. There’s one more thing I wanted to cover before we wrapped up, because I know we’re getting close to time. There was one thing that you mentioned in that talk that you gave in 2010. This was to the Institute for New Economic Thinking, if I remember correctly, I think was George Soros in the audience? It was incredible. It kept showing George Soros in the audience because I think it’s his, his Institute, or he funds it. But there’s a mention of the insurance company, you talked about this large US insurance company that you’ve done some modelling for or they were using your approach and 5000 variables, and but only a few 100 data points? And could you give us a flavour of what type of modelling that was? I mean, without revealing anything commercial, I was just…

David Hendry  55:55

Yeah, okay. So the 5000 variables are things like a 28 year old, single woman living in Texas with one car, and no children and owns their house. And that’s a variable. Right. So they then price their insurance for her house, knowing all these factors. They were finding that the sales were getting too difficult. And they wanted some simplification of what was actually driving their profit. So Jurgen Doornik who actually did this work, already had auto metrics, working with quite short time series on these sorts of various people. But you could then model all the things that might be taken into account and discover, actually, it didn’t matter that they were single, it probably didn’t matter, they’re female, it probably didn’t matter they’d no pets, right? What mattered was that they owned the house or didn’t own the house that did have a mortgage or didn’t have a mortgage and whether it was fixed term or so very few variables turned out to matter. And it allowed the company to dramatically simplify the number of people that they employed for sales, right, they came way down until when the financial crisis hit, their cost base was vastly lower. And they survived the financial crisis in the way that many insurance companies didn’t, because they, you know, they lost so much money in housing, for example. So that, although it says 5000 variables in most of them could never have mattered when being male would not apply taking female, for example. So males would not have been entered in the models for females and age that older people can’t be young females. So you can see immediately, although they talked about, we talked about 5000 variables, and there were most of them wouldn’t have been relevant in any given situation.

Gene Tunny  57:56

Right. And so this was the autometrics or autometrics is that part of

David Hendry  58:00

OxMetrics. That’s part of the OxMetric system. And it’s written in Ox, Ox is a computer language that Jurgen developed in the 1990s, early 2000s, it was the first attempt to get fully automatic modelling. And I have to say, our first attempts were really ridiculed by the profession, the idea that you can automatically model it didn’t require human intervention. Well human intervention is of course, thinking about what goes into the model. After that, itt’s pointless spending hours trying to see which is the matter when the programme can do that much more efficiently, much faster and much more generally. So it gives, we believe it gave much more time for thinking and much less time wasted in front of the computer desperately trying to find the model.

Gene Tunny  58:51

Yeah, it sounds to me like our central banks and treasuries and finance ministries should be, yeah they should, if they haven’t got a copy of your programmes, they should get a copy of them and start applying them because we certainly need to do better than the, than we have been in terms of forecasting. So…

David Hendry  59:09

RBS definitely has a copy, the sorry the RBA.

Gene Tunny  59:14

Okay, yeah. Yeah. It’s hard to know what they rely on some, I guess they they’ve got a model. I don’t know to what extent that it informs their policy actions. They got this Martin model, which is Yeah, yeah, it’s I don’t know. I don’t it doesn’t I don’t know whether it’s was developed using your

David Hendry 59:25

No, it wasn’t.

Gene Tunny 59:30

No I might have to come back to that because it’s a it’s a rather complicated model not not today, but just just good to Yeah, it’s good to good to find that out at least they’ve got your software if they and hopefully they’re making use of it to some degree. Okay, Tim anything more for Sir David?

Tim Hughes  59:50

No, I just think so it was really interesting. Thanks again for your time, and I think it just reinforces the, the way you talk about the modelling and the conversation we had about AI. Again, it’s something that’s come up in other areas where it’s a really powerful tool, but there’s a human discernment at some point to sort of like, bring it all together. And without that, it’s only so useful. And so I think it’s always encouraging to see that we need that human intervention to make sense of things. And sometimes humans get in the way of good modelling. I imagine, you know, that, but if we can give that amount of work over to the models and the AI to do the work for us as a tool, then, yeah, it’s very powerful.

Gene Tunny  1:00:35

Very good. Okay. Well, Sir David Henry, thanks so much for your time. I really enjoyed it really appreciate it really learned a lot. So thanks. Thanks so much.

Tim Hughes 1:00:40

Thanks for your time.

David Hendry  1:00:43

Thanks a lot. Thanks for having me interviewed.

Tim Hughes 1:0046

You’re welcome.

David Hendry 1:0050

Take care.

Gene Tunny  1:00:54

So Tim, that was an amazing conversation with Sir David Hendry, what did you think?

Tim Hughes  1:00:59

Yeah, that was fascinating. I really enjoyed it. I was a very happy audience member for most of that but yeah, I really lapped it up.

Gene Tunny  1:01:06

Ah, you’re more than just an audience member Tim! I mean, I think you are. You’re participating in that conversation. And you’re asking some good questions. So yeah, it was good to have you onboard. And what I found fascinating is I mean, I had the I had the line of questions about forecasting. And then we went broader than than just the forecast. And we started talking about climate econometrics. And you know, what he’s doing the modelling of getting to net zero for the UK, which I thought was absolutely fascinating.

Tim Hughes  1:01:40

Yeah. And he mentioned that the modelling hadn’t actually progressed in many ways, like, not necessarily with the climate econometrics, but with the other modelling that we were talking about in the first half of the conversation, which was surprising to hear.

Gene Tunny  1:01:53

Yeah, the forecasting. I mean, not what he’s doing, because he’s really a leader in forecasting

Tim Hughes 1:01:59

Yeah so his model he could back

Gene Tunny 1:02:03

Yeah, I mean, and of course, he’s going to back his own modelling, but I’d actually believe it because he’s one of the gurus of econometrics. So when I was studying econometrics, or first started studying back in the 90s, he was one of the big names. And yeah, the approach that he had this general to specific approach where you’re, you’ve got this very general specification, you’re trying to hone in on this more specific specification, you’re searching for the right functional form the right way to express the equation, the right variables, the right number of lags, and some clever things to get things back on track. If they’re shocked things like error, they call them error correction mechanisms. You remember, he was talking about the volcano modelling the global temperature after a volcanic eruption, I thought that was really interesting how he had this clever little functional form this V shape to get to get it back on track to model that I thought that was really clever. And I mean, he’s renowned for doing that sort of thing in his economic models. And, yeah, I was surprised that there hasn’t been a more widespread take up of that approach. And I think my takeaway from this is that, yeah, there needs to be more education or more outreach from from David’s group, I guess, at Oxford to really, you know, promote their their methodology, I guess they’re they’re doing it, they’re trying to do the best they can. And it looks like, you know, central banks or reserve banks got a copy of their software, the OxMetric software, which has PcGive and the other, the other parts of that software, but from what I’ve seen, it’s not as widely used as it probably should be

Tim Hughes  1:03:50

With any modelling, couldn’t that just be run in tandem as a hypothetical to see how it might have performed against the current models? Is that how it would work?

Gene Tunny  1:04:00

Yeah, yeah. And hopefully, they’re doing that. But

Tim Hughes  1:04:03

yeah, because like, you would think at some point, you know, if it’s outperforming, everybody’s interested in, you know, the best outcome s o it would be interesting to see, didn’t, didn’t actually ask that direct question. But that would be interesting to know if that might be feasible or possible, or if it’s been done?

Gene Tunny  1:04:20

Well, I think hopefully, the central banks and Treasuries are using this approach, or they, or I hope they they’re experimenting with it, they should use it more from what I can tell. What I found interesting about our conversations, he was talking about how I forget whether it was the Treasury in the UK or the Bank of England, they were overestimating productivity growth in the UK for a consistent period for like a 10 year period or something. And so that sort of mistake could have real consequences because if you, if you’re overestimating productivity growth, then you’re overestimating what you’re GDP growth is, what your economic growth is, your, in your forecasts, and therefore, what that would mean is that you may not have your policy settings, your monetary policy settings, right, because you think GDP growth is going to be faster than it actually will be. And so maybe you’re not giving, you don’t have the bank rate low enough to to help, you know, promote economic growth. So that sort of forecasting error can have material consequences, if you know what I mean. So it’s important to get these forecasts, right, because if you’ve got those forecasts of where the economy is going wrong, then that can affect what the Bank of England does, or what the government does with its budget.

Tim Hughes  1:05:41

Yeah. And it seems to be human. The human influence, which is the most unpredictable, or the, the element that is most likely to bring around an incorrect forecast.

Gene Tunny  1:05:53

Yeah, I mean, I guess the human element and yeah, I mean, all sorts of things. But the problem is that the economy Yeah, I mean, ultimately, it’s about humans, because they’re, they’re the the units in an economy. But the economy is so complex, and so many moving parts, it’s just very difficult.

Tim Hughes  1:06:13

There was some, also, with the second part of it, the climate, econometrics, which was fascinating, I didn’t realise the extent to which Sir David had been involved in that. And so he was very deeply involved, and it was really interesting, that part. And I know we’ve talked about climate and getting to net zero and the the challenges faced with that, and the changes in the technology that is driving us towards that, which is obviously an ongoing and very interesting subject. And I have to say, so I made a, I was trying to recall this information that our friend John Atkins initially mentioned to us about solid state batteries. So I was trying to remember what it was exactly. So this, these is the one of the new generation of batteries, which, you know, is still in development. So we’ll link in the show notes, I found something from the Guardian of July 2023 that explains a little bit more about solid state batteries.

Gene Tunny 1:07:00

Oh, good. So what does it say?

Tim Hughes 1:07:05

It says, just briefly, that basically Toyota has made a breakthrough that will allow it to halve the weight, size and cost of batteries, it would be that we are, da da da make batteries more durable, and believed it could now make a solid state battery with a range of 1200 kilometres or 745 miles that could charge in 10 minutes or less. And the company expects to be able to manufacture them as soon as 2027. So this is obviously not, it hasn’t happened yet. So this is a projection. But it seems that they’re quite close. So this is the sort of, I saw this a few months ago, it wasn’t the Guardian article, it was something through ABC, I believe in Australia. Along those lines of about Toyota has worked with solid state batteries. Clearly, there’s technology being, you know, pushed forward all around the world on different areas, and which ones come to the fore or make it to market like, you know, we can only speculate. But it certainly seems that there are changes coming and more efficient, effective ways of storing energy and producing energy, that are all moving towards that target of net zero by 2050.

Gene Tunny  1:08:16

So this is something that’s better than the lithium ion batteries. Yeah.

Tim Hughes  1:08:21

And like everything else, there were problems at the beginning that they’re trying to work out. Yeah. So yeah, it says that for benefits compared with liquid based batteries. I think one of them was I saw that it doesn’t, they don’t burst into flames as easily, which is a good thing, like so when they say there’s no spills, for instance, from a solid state battery, if they get in a prang, or whatever may happen. I mean, I’m sure that there’s pros and cons with most new technologies. And I’m sure there’s none. You know, it’s no different with solid state batteries. But it seems to be one of the ones that’s coming through, it’s been around for a little while, and it seems to be progressing. But it’s just one of those areas that by the end of this decade, it’s going to be very different. And of course, by 2050, there’ll be things we haven’t even heard of yet, that will be key parts of the whole target net zero target,

Gene Tunny  1:09:10

We might have to get someone on the show who can explain to us batteries, solid state versus other types of batteries, because I’d be fascinating to know about the technology and what minerals are required. Right? Because I mean, you had that conversation with Guillaume about the Dark Cloud, wasn’t it? And that’s, you know, the, the fact that we do need to mine all of these, these critical minerals and that’s that’s got consequences, of course. So yeah, we’ll be good to have that conversation. Tim, one of the other things I found fascinating, yeah a couple of other things in the conversation. I found David’s point about all the technology that was available at the end of the 19th century. I thought that was fascinating. That was fascinating.

Tim Hughes  1:09:55

I didn’t I didn’t know that at all. That surprised me. It surprised me big time.

Gene Tunny  1:09:59

Yeah. yeah. And also the point about nuclear energy I was, yeah, that was really surprised by that, that he was so positive about it and thought it could work here in Australia, and that perhaps some of the concerns that we have in Australia, about nuclear energy are misplaced.

Tim Hughes  1:10:20

So that was specifically modular.

Gene Tunny  1:10:23

Yeah, the small modular reactors, and he’s saying they’re a lot safer. And this is something I talked about Ben’s, I talked with Ben Scott, about a couple of years ago, I think, on this show, the potential of small modular reactors, I though that was good that David brought that up.

Tim Hughes  1:10:39

Yeah, that was interesting. I hadn’t actually heard of that before. And I know that we’ve, like spoken about it before with Josh Stabler, for instance, with the the likelihood that there’s going to be different solutions to the energy provision in the future. So it’s not just going to come from one main source it’s going to come from most likely several different ones. So if modular nuclear power stations are a part of that, that’s quite possible. But it’s clearly going to be not just one thing. And just on that subject as well. There was it was mentioned about graphene, David mentioned, oh, yeah, I know this, that’s come up in a conversation we’ve had before here in Brisbane. There’s GMG, who are involved in that, here in Brisbane,

Gene Tunny  1:11:23

with GMG. So G stands for graphene I guess, and there’s…

Tim Hughes  1:11:27

Graphene Manufacturing Group. And so it’s in the space of renewable energy, and this whole push towards clean energy.

Gene Tunny  1:11:35

But what’s graphene got to do with it? Because graphene is a material, isn’t it?

Tim Hughes  1:11:39

Very good question, Gene. And this is something that I will get back to you as soon as I know. Yeah, because this is actually my solid, solid state battery moment in the in the wrap up, I managed to get another one in. So we’ll put something in the show notes to do with the graphene, but I know it was, it came up in a conversation we had with

Gene Tunny  1:12:02

ah apparently it’s going to create products with a better efficiency than the existing ones.

Tim Hughes  1:12:07

Yeah so it’s part it’s part of the all of this emerging technology towards better energy storage. Like most other things, it’s happening in many different parts of the world at the same time but we do have this, this company in Brisbane,

Gene Tunny  1:12:19

it looks like Yeah, yeah. Sorry Tim I just noticed it looks like someone’s built a graphene solar farm. So it looks. Okay.

Tim Hughes  1:12:25

Yeah. So to be to be explored, like, because it’s not something I know a great deal about, or, you know, so I think that w’e’ll definitely will, will earmark that for the next conversation we have about clean energy and where that’s going.

Gene Tunny  1:12:36

Yeah, for sure. Because this is it’s an ongoing issue. And I mean, the conversation, this isn’t going away. And you look at this northern hemisphere summer, and yeah, I mean, that’s just going to intensify this conversation, I think. Yep. Yeah. Okay. The other thing I thought was good about the conversation with David, I liked your question about AI and what’s happening with AI? And David pointed out, well, what they do, their algorithm, their automatic model selection algorithm, their auto metrics, that’s a form of AI. I thought that was a good point that he, he made there. Yeah, yes. Yes. So yeah, it was good question. So all in all, what a terrific conversation. And yeah, I really thought Sir David was amazing. He’s someone I would love to have here in Australia participating in the Australian policy debate on energy in particular, I think he could be that he could provide that sage perspective. He’s someone you’d pay attention to, he’s someone who’s very thoughtful, you know, good communicator, and as well as being a real gentleman.

Tim Hughes  1:13:43

Yeah, I really enjoyed it. And I found it very eye opening. And yeah, I think there’s, like you say, it’s an ongoing conversation. So it’ll, it’ll keep evolving. And hopefully, if we can, maybe they’ll be a round two with Sir David to continue that conversation.

Gene Tunny  1:13:59

We can only hope, so Tim, anything else before we wrap up this, this debrief?

Tim Hughes  1:14:05

No, I think I think I should stop while we’ve still got enough room in the show notes for

Gene Tunny  1:14:13

you introduce a new concept – then I ask ‘Tim that’s fascinating can you tell me more?’ Nope!

Tim Hughes  1:14:20

They say a little knowledge is dangerous. On this one I was lethal, but no, it was fun. I enjoyed it. And yeah, it’s it’s something that affects us all. And it’s something that’s changing very quickly. And so yeah, we’ll we shall return to that conversation no doubt.

Gene Tunny  1:14:36

Absolutely. Tim Hughes thanks for joining me on this conversation.

Tim Hughes 1:14:40

Thanks Gene.

Gene Tunny 1:14:43

Righto, thanks for listening to this episode of Economics Explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via or a voicemail via SpeakPipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it? Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.


Thank you for listening. We hope you enjoyed the episode. For more content like this or to begin your own podcasting journey head on over to


Thanks to Obsidian Productions for mixing the episode and to the show’s sponsor, Gene’s consultancy business Full transcripts are available a few days after the episode is first published at Economics Explored is available via Apple PodcastsGoogle Podcast, and other podcasting platforms.

Economic update

Mounting evidence of Superforecaster success

There is mounting evidence of the superiority of the Superforecasting approach, which Economics Explored hosts Gene Tunny and Tim Hughes discussed with Warren Hatch, CEO of Good Judgment, on an episode earlier this year (see How to be a superforecaster, or at least a better forecaster). Superforecasting is an approach to forecasting that, as the blurb for the 2015 book Superforecasting notes, “involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course.”  

The success of Good Judgment’s superforecasters in forecasting the US Federal Reserve’s policy decisions was profiled in the New York Times last month. Good Judgment has been asking its superforecasters an ongoing series of questions about the upcoming three meetings of the Fed, asking if they will cut, hold, or raise. For the four meetings so far in 2023, the superforecasters were spot on with their probabilities for three hikes and a pause. For the next three meetings, they forecast two hikes followed by a longer pause. 

Good Judgement data scientist Chris Karvetski has prepared an analysis showing the superforecasters extraordinary performance in forecasting the Federal Funds rate targeted by the Fed (see Superforecasting the Fed’s Target Range). He has calculated Brier scores of forecast accuracy, where 0 denotes perfect accuracy and 1 denotes perfect inaccuracy, for different sets of forecasts. The Superforecasters are doing 3x better than CME futures for the Federal Funds rate, with far less volatility.

Separately, superforecasting pioneer and Good Judgment co-founder Philip Tetlock and his research colleagues just released a study on existential risk with interesting approaches to generate forecasts for low probability but high impact events, such as an AI apocalypse (see Results from the 2022 Existential Risk Persuasion Tournament). This study was summarised by The Economist earlier this month: What are the chances of an AI apocalypse? Thankfully, as The Economist observes:  

Professional “superforecasters” are more optimistic about the future than AI experts.

For more information on the superforecasting approach, check out the Economics Explored podcast episode from earlier this year:

Superforecasting w/ Warren Hatch, CEO of Good Judgment – EP176 – Economics Explored

Several clips from the video of the interview are available via YouTube. The first clip is “What Makes a Superforecaster?”:

It identifies the importance of being cognitively reflective and having good pattern recognition skills. Incidentally, one way to identify people with good pattern recognition is to test them with Raven’s progressive matrices, as noted by Warren Hatch in this clip:

Another clip covers how we can overcome our own prejudices and biases to make better forecasts:

Tips from Warren in this regard include:

  • self-awareness;
  • getting feedback; and
  • forecasting teams in which members can interact with each other anonymously so everyone’s views are considered solely on their merits with no prejudices.
Podcast episode

Superforecasting w/ Warren Hatch, CEO of Good Judgment  – EP176

What are the characteristics of superforecasters? How can a superforecasting team be developed? Hear from Warren Hatch, CEO of Good Judgment, a leading global forecasting business based in NYC. Accurate forecasts from Good Judgment superforecasters have included the scale of the pandemic. In early 2020, Good Judgment superforecasters estimated the United States would have over 200,000 deaths from COVID-19 with 99 percent certainty, an estimate that was considered by many as excessive at the time. Warren gives show host Gene Tunny and his colleague Tim Hughes some valuable tips on how to become a superforecaster. 

Please get in touch with any questions, comments and suggestions by emailing us at or sending a voice message via

You can listen to the episode via the embedded player below or via podcasting apps including Google PodcastsApple PodcastsSpotify, and Stitcher.

What’s covered in EP176

  • The Good Judgment forecasting business [2:41]
  • What are the characteristics of superforecasters? [6:47]
  • How to identify someone who is good at pattern recognition? Raven’s matrices [9:24]
  • Link between subject matter expertise and forecasting ability [10:40]
  • What are some of the techniques that are used to help super forecasters rid themselves of prejudice and bias? [12:57]
  • How large does a super forecasting group need to be to be successful? [20:35]
  • Tips for being a super forecaster [25:59]
  • Using the percentages to retrospectively see how you’ve gone [27:56]
  • Bayes’ Theorem [31:41]
  • The importance of being open to a range of different views [42:47]

About this episode’s guest: Warren Hatch, CEO of Good Judgment

Warren Hatch is Good Judgment’s second CEO, succeeding co-founder Terry Murray. 

Before joining Good Judgment, Hatch was a partner at McAlinden Research, where he identified thematic investment opportunities in global markets for institutional investor clients. Previously, he co-managed a hedge fund seeded by Tiger Management and was a portfolio manager at Morgan Stanley.

Hatch holds a doctorate in politics from Oxford, a masters in Russian and international policy studies from Middlebury Institute of International Studies at Monterey, and a bachelors in history from the University of Utah. He is also a CFA® charterholder.

Links relevant to the conversation

Good Judgment’s website and Twitter: and 

BBC Reel featuring Warren Hatch:

Warren’s talk on YouTube which Gene quotes from in the episode:

What is Superforecasting? – Warren Hatch, Good Judgement

Article by Nicholas Gruen:

Making better economic forecasts 

Links regarding foxes versus hedgehogs:

Transcript: Superforecasting w/ Warren Hatch, CEO of Good Judgment  – EP176

N.B. This is a lightly edited version of a transcript originally created using the AI application It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.


Fri, Feb 17, 2023 7:01AM • 47:45


forecasting, forecasters, warren, question, people, economists, judgement, probability, super, good, recession, models, world, economics, bias, episode, views, bayes, thinking, big


Tim Hughes, Gene Tunny, Warren Hatch, Female speaker

Gene Tunny  00:07

Welcome to the Economics Explored podcast, a frank and fearless exploration of important economic issues. I’m your host Gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode, please check out the show notes for relevant information. Now on to the show. Hello, thanks for tuning into the show. In this episode, Tim Hughes and I chat about Super forecasting with the CEO of good judgement, Warren Hatch. good judgement is a very successful forecasting business based in New York City. Warren has a background in funds management, and he holds a doctorate in politics from Oxford. I’m very grateful to Warren for providing some actionable insights into how we can make better forecasts. And I suspect you will get a lot out of this episode too. So please listen to the whole thing. And stick around to the end because I have some additional thoughts after our conversation with Warren. Okay, let’s get into the episode. Warren Hatch from Good Judgement. Thanks for appearing on the programme. Thanks for having me. Excellent. Warren. Yes, we’re keen to chat about all things forecasting. Forecasting is a big issue. Well, I mean, everywhere in the world, but in Australia, we’ve had a bit of controversy around interest rates. And we’ve got a reserve bank governor who’s in the spotlight or under under a lot of criticism because he was predicting that interest rates wouldn’t rise until 2024. And we’ve had a succession of interest rate rises, which are causing financial distress for families. And it just brings into the spotlight the problems of forecast even by people who are, you know, you think they’re well informed? And Tim saw, I think, Tim, you saw Warren on a BBC show, didn’t you?

Tim Hughes  02:03

It was a BBC real little eight minute video, which is really good. And we’ve discussed these kinds of issues before ourselves. Gene mentioned all that sounds like the super forecasting book by Philip Tetlock and Dan Gardner. And of course, as it was, it was exactly that, you know, your association with those. And so we came full circle. And I reached out and thank you for making the time to talk in an area that we’re really interested in.

Gene Tunny  02:29

Yeah. And so to kick off, Warren would be keen to understand what’s your work, good judgement involved? What are you doing there? And, broadly speaking, can you give us some insight into how you work, please.

Warren Hatch  02:41

By all means, and by the way, it’s not just Australia with central bankers that don’t have a very good track record recently, when it comes to forecasting by any means. Our own Federal Reserve here, had some pretty spectacular misses, as well. And they’ve had to do some pretty severe course corrections. There’s, as you know, and the good judgement project itself came out of some pretty spectacular forecasting failures on the part of the US intelligence community where they had forecast weapons of mass destruction that weren’t. And then they missed 911, of course. So after that experience, they did some very deep soul searching to genuinely try and find ways to improve the forecasting skills of the intelligence community. And they ran a big competition. And as you know, that good judgement team did very well defeated all of the other university based research teams. And then four years after it started, it came to a conclusion, and they wanted to commercialise those findings. And the government, US government supports that kind of initiative as a way of showing the taxpayer dollars are being well spent. And so what we set out to do was to break down this big research initiative into smaller pieces that could be useful out in the real world. And we do a few things we do consulting for some more deeper engagements. But then we also provide a lot of workshop training. So organisations that want to improve the forecasting skills of their analysts and their teams can do so. And then we also have the super forecasters themselves, who are available to forecast on client questions. And we also have a public dashboard where we contribute to the public discourse in our way. And the questions are basically posed by organisations in the private and public sectors to improve their own decisions. Having probability estimates about uncertain events, that’s what we’re all about, is to come up with a number in our forecast rather than a vague word that lacks accountability. But then we also provide the context for those numbers. So it’s not just a dataset that we’re generating, we’re generating the stories that go along with it.

Gene Tunny  04:58

Okay, so buy in Number rather than a vague word, are you talking about a probability? So you’re saying that we are forecast is that within the next 12 months, there’s a 60% probability of recession or something like that? Is that what you suggest?

Warren Hatch  05:16

That’s it? That’s, that’s exactly the way we can frame it is, what is the probability of a recession in the next 12 months, or by a particular date? Or in different time spans? Will there be a recession this half of the year or the next half of the year, and so on?

Gene Tunny  05:32

And that’s about keeping forecasters accountable, is it and if you’re a forecaster, and you give a forecast like that, you can assess your track record, so to speak and adjust your forecasts in the future? Is that correct?

Warren Hatch  05:44

That is correct. And I am using a number it does, it does a lot of really good things. That’s one of them as you get feedback, right? So if you say maybe there’s going to be a recession next year, you’re not gonna get feedback from that. The other thing is that allows us to communicate in a shared language, right? If I say, Well, there’s a possibility of a recession, and you say, maybe there’s a recession? How do we kind of compare our thinking, how do we come up with something that reflects our joint wisdom. And that’s what this is about as having a wisdom of the crowd approach with a shared language with accountability with feedback, and a way to compare forecasts on different topics.

Gene Tunny  06:21

Okay. It’s amazing the type of work that you’re doing. So I had a look at your website a few days ago, and I saw that some of the things you’re forecasting, you’re providing advice to clients on this, you’re providing advice on, what’s the probability that Putin doesn’t survive, or like, what’s happening in Ukraine and all of that? So it’s a wide range of things that clients are interested in? Is that right?

Warren Hatch  06:46

That is correct. And what we’re looking for is the topics that affect decisions. And where there’s a lot of information and conflicting views out there, where our panel of super forecasters can take all of that publicly available information, and filter out a lot of the noise because there’s a lot of noise out there these days, and try and find the signal through their process, and then turn that in into a number on things like Putin’s future on things like Will there be a ceasefire between Russia and Ukraine? These are all quite consequential? Yeah.

Gene Tunny  07:19

And what, how do you get on this super forecasting panel? Who’s a super forecaster? What are their characteristics?

Warren Hatch  07:26

That’s a great question. And that’s something. And something to keep in mind, too, is that in the research project, that wasn’t part of the research plan at all. They just observed that in the first year, there were some people who are consistently better than everybody else. And being researchers that caused a new research question, what would happen to ask themselves? If we put them on small teams? Would they get better? Or would they revert to the mean, and they did not know at all, a lot of people thought there’d be a mean reversion, turns out, no, they continued to get even better. And so we still do the same process now with our public side, where we’ll just take within the top 1% of the forecasting population there, and other platforms to invite him to come and join the professionals and they have certain things in common, for sure, they gave us a lot of psychometric tests, hours of them before we got to do the fun stuff, you know, and forecast on elections in Nigeria in the light, and then to see what kinds of characteristics correlated with subsequent accuracy. And there are certain things that really pop out. One is being really good at pattern recognition, right? So you can think of, you know, you got a mosaic about the future that we’re trying to fill in, and see what’s coming faster than anybody else and fill in those tiles. And being good at that is a fundamental characteristic of a good forecaster. Another is being what they call cognitively reflective. And basically, that means that if you’re confronted with a new situation, you don’t automatically go to what first pops into your head, because what first pops into your head might not be right, you might be overfilling the mosaic too quickly, and getting the wrong picture. So you want to slow down in economy in terms let system to be your friend, you know, it’s hard work. But that’s the way you get a better a better result. So those are two very fundamental characteristics that good forecasters have.

Gene Tunny  09:24

Right? And how do I tell someone with good pattern recognition is that someone who maybe they excel at Pictionary or at certain games or certain board games and trying to understand how would you actually judge that

Warren Hatch  09:37

being good at Pictionary is a good quick and dirty way to do the more formal way is it’s called ravens matrices. And this comes from the UK originally, during World War Two. They used it as a way to identify people who would be good pilots during the war, because when the war first started, they went to you universities grabbed everybody put them in a in a cockpit or in a submarine. And of course, that means their life expectancy wasn’t very high. And they needed to be able to replenish pilots and submariners. And this was a way to go out to the countryside and identify people who perhaps didn’t have a formal education to an extent, but we’re very sharp, very good. And it turns out, that was a great way to spot good forecasting talent, and you can look them up to Ravens. You can see them out on the internet. And it basically what it does is it tests your ability to see different patterns and what rules there are to anticipate what those patterns will become.

Gene Tunny  10:40

Okay, I’ve got one more question. I’m gonna hand over to Tim, because I’ve just got one bill burning question. This is fascinating. What’s the link between subject matter expertise and forecasts inability? Is there any correlation? Because the best economic forecasters actually economists, for example, are? I mean, I’m guessing the best weather forecasters are meteorologists? Is it different across disciplines? Do you have any insights into the relationship between subject matter expertise and forecasting ability? Warren? That’d be great. If you could respond to that plays?

Warren Hatch  11:12

That’s a wonderful question. And what we have found, and the research shows is that there isn’t necessarily a connection between being a subject matter expert and a good forecaster on that topic. Subject Matter Experts are very good at telling us how we got to where we are, they’re also very good at asking the questions, we should be asking ourselves about the future. But they’re not always so good at saying what the probability of one outcome might be relative to another. And one reason for that is that experts, by definition, have models of the world. They have, you know, heuristics, they have shorthand, ways of interpreting what’s going on in the world. And in moments of a lot of flux, there might be small, subtle things, that their models and their expertise will just filter out as a matter of course. And by having a skilled generalist as part of that activity, then they don’t have those blinkers. They don’t have those fixed models. And they might detect something subtle that they go, Wow, this is actually something potentially quite significant. And so what we found is that rather than have experts versus skilled generalists, you have them both and and let them interact with one another on a forecasting platform, one way or another, and then you get really positive strong results we want but our favourite Boolean a good judgement is

Gene Tunny  12:48

and yeah, it’s not either, or, is that what you’re saying? It’s a no. Yeah, exactly. More crisply. Gotcha. Okay, good. Excellent. That makes sense. So just just wanted to make sure I understood it. Tim, do you have any questions?

Tim Hughes  13:00

Yeah, I do, actually. Because I remember in a little bit of research, seeing what you said about experts and skill generalists, and also the diversity in a group of super forecasters, which helps bring different perspectives to a decision, or a forecast. And I was gonna ask about the we’re all influenced by prejudice and bias, whether we’re aware of it or not. Some of it is hardwired survival biases, and, and others, we have more control over. I was interested to ask Warren, what your thoughts were on prejudice and bias and with super forecasters, what kind of techniques or if there are any sort of habits that are encouraged with those guys, to be able to rid themselves of those prejudice and bias to be able to make better decisions or forecasts?

Warren Hatch  13:53

Yeah, good question. And is goes to the foundations of what we’re trying to do. And we might usefully think of two categories of bias. There’s the kind of the bias that we all have the cognitive biases, the things that interfere with our judgments that are just built in to our wiring, right? Most people are overconfident, it just is built right in. Most people will get anchored on a high status individual, for instance, who was the first to speak at a meeting and everybody gets anchored on that it just happens. And for those kinds of cognitive biases, well, the psychologists debate a lot, whether you can eliminate those sorts of things. Some say it’s impossible. Some say there are things you can do. What we do know is that for that category, being aware of them, at least can let you counteract their effects, like being overconfident. You can measure and getting that feedback can get your over confidence in check. So if somebody asks you what your confidence about a particular forecast you might be making, you might say, oh, yeah, I’m 90% sure about that, or 90% sure about some particular fact, in the you can measure that. And it turns out, well, maybe more like 50%, right? Not 90%, right in those situations, so you can recalibrate yourself. Those sorts of cognitive biases, we can identify spot, and do at least some mitigation techniques to rein in their effects on our judgments. The other category is the kinds of biases or prejudices that we might acquire, as we live life, and we have different life experiences. And that will shape the way we interact with others think about issues in all kinds of different ways. And that can be a lot tougher to be sure to deal with, what are the two things that we can do is one, we can level the playing field so that we know as little about each other, when we’re forecasting as a team as possible, right. So if we were on a platform, we would all adopt made up names, we’d have no idea where we came from, we’d have no idea, ethnicity, or gender, or religion, or political beliefs or anything, as much as possible. And all that’s going to matter is, is the quality of our comments that we can contribute. And by doing that, we can at least hold those things at bay, we don’t eliminate them. But we kind of, you know, we put on our white lab coats when we go to the forecasting platform. The other kind is some issues are just really difficult. Because they are, they’re emotional, or they deal with very troubling topics. And that’s a difficult thing for forecaster to deal with. For instance, a lot of the work we did when COVID was was running rampant, is really tough. And a lot of forecasters just said, Look, I have a really hard time with these questions. I’m going to step aside or election questions, I’m gonna just step aside because my personal beliefs are interfering with my judgement. Yeah, the one little tool that you might do. And this comes from the head of our question team, and a super forecaster that I thought was just great to try and create at least a mental distance on these kinds of issues, is imagine you’re an Anthropologist on Mars, observing everything through a telescope, right? By doing that, at least for him. And for some others, too, it makes it easier to engage with these more emotional issues, not all the time. But it can be a helpful tool.

Tim Hughes  17:41

So a level of detachment as much as possible, and that self awareness to to not be involved from what your previous experiences may have been in those areas,

Warren Hatch  17:50

as much as possible. When you’re making your forecast. Then once you’re done, you take off your lab coat, you can go down to the pub, have a beer and just, you know, let it rip.

Tim Hughes  18:01

It’s really good. Like, because it’s come up in conversations we’ve had before. Along the same lines were softening the language around. Like we’ve had conversations around the truth. For instance, like politically and everywhere, like since the beginning of recorded history, there’s always been questions about what’s true and what’s not true. It’s certainly no different nowadays, like, we know, there’s still the same issues of like, is that true? Or is it not? And softening the language around what we consider to be true or not seems to be a good approach, which seems to be something that is adopted with using probabilities and percentages to say, the probability of something being true or not or happening or not. So that seems to fit in with being receptive to new information that may come in that allows you to change your position more freely. Is that sound familiar with what happens at Super forecasting?

Warren Hatch  18:53

Yeah, yeah. And a lot of our process is trying to think about how well do we know what we know? Right? So epistemic uncertainty, is the phrase that they that they use so and being humble about how much we really know. And being aware that there are pockets where we may not be able to quantify uncertainty on certain issues, we run up into a wall of irreducible uncertainty and we should respect that that is something that’s there and not get carried away and go beyond it. And because on that other side, there may be a different kind of uncertainty with a call Alia Tory uncertainty, right? And that’s the kind of randomness that’s just there. And we’re not going to be able to rationalise it away. It’s just, it’s sets a limit on what we can and what we can know. Now, what’s really fascinating, of course, is part of what all of this research project and a lot of what we do do is, is that for some topics, that wall is farther out than we had thought before, right? That irreducible uncertainty, that zone is maybe not as big as we might have thought. So we can quantify more than we had previously recognised. And we can also quantify it with more precision than we had been able to do so before. And putting those two things together means that we can come up with forecasts where we can have a much better informed judgement than we could before.

Tim Hughes  20:35

When you put the left code on the ego, it can’t be there as well, I guess,

Warren Hatch  20:39

as much as possible, right? Yeah, then you can only go so far, of course. But having that kind of an approach, at least gives you a shot at coming up with something that’s that’s good. And you’ll find out of course, because if over a lot of questions, your ego was actually creeping in, after all, it’ll show up in the feedback, you’re receiving the scores that you get on your forecast.

Gene Tunny  21:02

Okay, we’ll take a short break here for a word from our sponsor.

Female speaker  21:08

If you need to crunch the numbers, then get in touch with Adept Economics. We’ll see you Frank and fearless economic analysis and advice. We can help you with funding submissions, cost benefit analysis, studies, and economic modelling of all sorts. Our head office is in Brisbane, Australia, but we work all over the world. You can get in touch via our website, We’d love to hear from you.

Gene Tunny  21:37

Now back to the show. Warren, I’m just wondering, from what I’m hearing, it sounds like yeah, you need to it’d be good to have a diversity of views. You need people who question who act as a counter to other people’s biases? How large does a super forecasting group need to be? I mean, do you have a sort of, is there a rule of thumb you need I need at least half a dozen people, you need a dozen or you need dozens? I mean, is there a is there a rule of thumb about that,

Warren Hatch  22:06

you pretty much got a good rule of thumb is six to 12 is a good number to have, especially when you put your thumb right on it, you’ve got a diversity of perspectives and play definitely want to have people with different approaches, different philosophical views, different different life experiences, too. And they’re all bringing, you know, different pieces, right? So we’ve got that mosaic that we’re trying to fill out. And if we all went to the same schools all have the same backgrounds, we’re basically all going to be bringing the same tiles to our mosaic. What’s the point? What we want is people who have different experiences different perspectives, who can fill it out as quickly as possible to get the best possible result. And that’s one thing we see time and time again, is that working on teams is going to deliver a superior result over time. Even the best single super forecaster will not do better than a team of forecasters over time,

Gene Tunny  23:08

Ron, another question, and this will probably be my the final one I want to ask are prepared for? are you competing with mathematical or numerical modelling? Or is what you’re doing? Is that a compliment to it? Because, like I see in meteorology, for example, I think they’ve made some impressive improvements over the last 20 to 30 years, I see the huge range of data that they’re ingesting into their models, and they’ve gotten better economics. I mean, our models have actually not got any better. And if you rely upon a computerised, like a computer model for an economic forecast, you’re going to end up with something silly. So there’s always judgement involved in any economic forecasts that come out from treasuries or central banks. Just wondering how do you see the role of, of modelling? Is it compatible with what you’re doing?

Warren Hatch  23:59

Absolutely, yep, it is very much complementary. And a lot of individuals super forecasters have models that they build, and they craft and they put together. So on that side of the forecasting, process models are very integral. Also, when we put our forecast together and aggregate them, we have a model to help us do that with a machine learning element that will monitor for the accuracy of the forecast so that we can deliver the best possible signal. And then on the user side, what we create that number will go into into different models like quant funds, or regular users of our of our forecast because we’re quantifying things that they couldn’t otherwise get in the form of a number. And looking ahead, I certainly see that’s something that’s going to continue, where there’s a lot that the machines can do that models can do, and they can do it fast and they can do it better in increasingly doing the heavy lifting, that we would other why’s have to do and I love that the word computer itself used to be a person, right? When somebody would be added adding machine typing away furiously? Isn’t that a fine thing that a machine can now do that which lets the human go off and do things that the machines still can’t. And there’s a lot that the machines still can’t do when it comes to judgement when it comes to forecasting, especially how people will interact in an uncertain world. The machines are not there yet, maybe they’ll get there. But what we’ve seen in the research and the results is that right now, there’s a nice division of labour to be had, where the machines can really tell us a lot about a history of a particular forecast area, the base rates, right? So the comparison classes that we should have in mind when we’re thinking about a new situation, but then synthesising them, and converting that into something about the future is something that we do. So it’s a nice division of labour.

Gene Tunny  25:59

Yeah. When you mentioned base rate I just remembered in your, you gave a great talk. It’s on YouTube, I’ll put a link in the show notes for you mentioned, a few tips for how to be a super forecaster and one of them was starting with the base rate. So looking at, well just look at what in the population, what’s the probability that that this would occur? I think it was with Harry and Megan, I’m trying to remember if that was the example, if you’re thinking about what’s the probability that their marriage will, will last, then you know, just look at the start with a base rate for the population itself, and then go from there. I thought that was a good tip. If I remember that example correctly, and then record your forecast, compare with others, update it with new information and keep score. So look at how you’ve gone over time. So I thought they were really good tips. And I’ll put those in the show notes. So yeah, I really enjoyed that. That presentation. Yeah, no, no, that wasn’t a question. Just that observation is, that was really good. But if there were any thoughts you had on that, Warren, feel free to throw them in?

Warren Hatch  27:00

Yeah, that was a great distillation. So it’s all about process, right? And you want to have a checklist, and you’ll have your own checklist. But the five things that you just went through are really important things to have on anyone’s checklist to to come up with a better forecast, there’ll be other things that might be useful from time to time. But even just going through that in your head for a minute, right, can give you a better result, especially when things are you’re confronted with something you don’t know anything at all about. Oftentimes people will say, Well, I don’t know. It’s 5050. And they’ll say, Yeah, I’m 50%. But you know, pause, how often really, is 50% being neutral on something? Not very often. And by just going through a few steps like those, you can maybe come up with something that gets you in a better position than you otherwise would? Yeah, yeah, for sure.

Gene Tunny  27:55


Tim Hughes  27:56

one of the things with using the percentages, I remember, hearing you say as well with, it allows you to retrospectively see how you’ve gone. So you can, if there’s something for instance, that is a regular prediction, you can then start to see how you went as a super forecaster, not necessarily yourself, but like anyone who’s trying to forecast to see how they went. And yeah, have a sort of checks and balances, so that you can see how accurate you’ve been. So an interesting thing that came up along those lines was, for instance, if your football team is 80% chance of winning a game. Our inbuilt prejudice and bias, I guess we refer to before would say, well, we’re pretty much home in house. But the reality is, there’s a 20% chance that they won’t win, which of course, is still possible. And so we we sort of edge towards what we want. And also we take something over 50% as being a bigger likelihood, then maybe it is. So it’s really interesting to sort of think in these terms. And it’s a very honest way of assessing situations. And there seem to be a lot of other benefits from approaching decisions and forecasts this way. It was along the lines of what I was asking about before, I guess. But is there a big influence of philosophy in what you do? Because I can see parallels with stoics. And I think you mentioned pragmatism as an influence in what happens that good judgement.

Warren Hatch  29:22

Yeah, and epistemic theology. Yeah, we were talking about earlier is how do we think about uncertainty, all very essential. And one other really important element is, of course, Bayesian ism, where recognising that you can better understand the world with probabilities of this sort, is something that’s very critical if you’re going to be using this kind of a process. And there are those who really genuinely believe that that approach is not useful in it for people like that, that are not going to be very good forecasters, and in this sense, but as you go around and you’re looking for tools to To help think about the world, those are very important touchstones for most people, whether they have studied formally or not, they certainly acquired a lot of those principles through experience and the feedback that they have, that they have received. So epistemology

Tim Hughes  30:17

is how we know what we know. So the other one was, did you say Bayesian theory? Yeah, so

Warren Hatch  30:23

b Yeah, Bayes Thomas Bayes?

Tim Hughes  30:25

Could you explain that one, please? Yeah, I think Gene knows everything. So. But I’m not familiar with that one. So could you would you mind explaining that one, please?

Warren Hatch  30:33

Well, gene can probably do a better job than I can. But at its foundation, it’s just that when you’re thinking about the future, that you can think about probabilistically. And you will identify different variables along the way, that would affect that probability that you have shaped that you’ve started with. So if we are thinking about if there’s going to be a recession in Australia in the next 12 months, there are things that we might be looking for along the way that would get us to update our forecasts of that probability. And identifying what those might be in advance. For really important things like that means that we can attach different probabilities to those different factors today. And then as we move forward into the future, and we find out more about how those variables are actually playing out, we can update those pieces. And that will inform our update of the bigger question as we go. How would you say a gene?

Gene Tunny  31:39

Yeah, that sounds fair enough for me. It’s an A. Yeah, it’s yeah, the Bayes is there. And there’s a good book on the theory that wouldn’t die. I might have to cover it in another podcast episode. So we can go deep on it that Yeah, I think that’s, that’s great, Lauren. Oh, yeah, really, really appreciate that. I was I was thinking of Bayes theorem, but whether I should ask you about that. But that was good that you brought it up independently. So that’s excellent.

Tim Hughes  32:05

That was gonna say it’s good. Common sense, though, isn’t it like it means that you’re less entrenched in your views, and that you’re open to change your mind, because anybody’s opinion is only as good as the information it’s based on. So as you receive more information, your opinion should be more well informed and be a better opinion. Ultimately, I guess that’s based in theory work.

Warren Hatch  32:26

Yeah. But here’s the thing is that not everyone subscribes to that view, for sure. There’s some people who genuinely believe it’s better to stick to their guns, you know, the people who are very fixed models of the world, that tell them how things will unfold. I mean, Karl Marx, not so much of a Bayesian, really. And people like Nouriel Roubini, not so much of a of a Bayesian either when it because he has a view of the world. And we and we kind of hear the same thing over and over. And this new incremental information is something that they will tend to dismiss rather than bring in and update their own views.

Gene Tunny  33:03

Yeah. And I’d target you’re familiar with the John Maynard Keynes, quote, aren’t you, Warren? Yeah, it’s a wonderful one. Yeah. When the facts change, I changed my mind. What do you do, sir? I think that was it. It’s very good.

Tim Hughes  33:17

I mean, the the tragedy with this is this is how we vote in governments around the world. And they often come from entrenched beliefs, with a lack of willingness on all sides to listen to new information. So it has a massive impact at a very individual level in how we vote. I think, you know, if we could all adopts Bayesian theory and in how we vote, then it might make give us better politicians and better outcomes.

Gene Tunny  33:44

To we might have to do a deep dive in a future episode on Bayes theorem and look into it for the intricacies of it. So we might go into that in a future episode. Yes. All right. You’ve been generous with your time has been fantastic. Do you have any final thoughts before we wrap up,

Warren Hatch  34:01

maybe a couple that might be useful, just based on what we were just talking about? One is, and this can be useful for, as we, as we think about politics, and debating issues of the day, right is that most of the time, these really important issues involve people yelling and screaming at each other. Right? It’s very adversarial. And one of the things that can be done with the sort of framework we’re talking about here is if we can get adversaries on opposing sides or multiple sides to come together and identify what are the really important things that they think would support their view, but be very difficult for the other side? What does that look like? Right? And once we’ve identified what those issues might be, we can then collaborate the ideas adversarial collaboration, and say, Okay, well, here are the things that matter for these different worldviews and And then we can, you know, let time unfold to see whose position is supported by the data by events as they unfold. But then we can take the extra step and pose those in the form of questions to a population of forecasters. And by applying that process, we can bring that future into the present and get a better sense of how those issues are going to be unfolding. From here with the input of the adversaries in a much more collaborative framework. I think that’s a wonderful approach. We’ve done a little bit of that others have to, we look forward to doing even more. And I think it can also very much apply in the world of economics, where there are very strident competing schools about what causes recessions. And so let’s get the Keynesians and the monetarists together to have some collaboration in that way, engage on a real world issue, like what’s the probability that there’ll be a recession in Australia in the next 12 to 24 months is a wonderful thing to do. The other thing that I think are useful to maybe think about is economists themselves, why they don’t do better. And I think one reason is that many of them continue to practice their craft, using state of the art techniques from the 19th century, in the way they model things and think about things and exchange things. And the sort of process that we’ve been talking about here, much more dynamic, much more nimble, and much more team based might be really interesting. So for instance, it’d be really, I think, potent, to do a survey of economists about the probability of a recession in the next 12 months, where we just take their snapshot like all these surveys already do. But then put them together and have them compare notes and probe one another’s reasoning. Yeah, and have an opportunity to update as a result of those different views, even anonymously. So their official forecasts could still be the same. But they could have kind of a informal forecast that they make through this process with kind of a shadow version of themselves. And I’ll wager that the number that comes out of that informed crowd is going to be better than any one single economist, Rod.

Gene Tunny  37:31

Yeah, that’s a good idea. I mean, we do have the economic society runs a poll of economists, but I’m not sure it forces them to give answers in a consistent numerical format on these questions where they are asking a question like that. So yeah, I’ll have to have to think about how that could work. Have you seen that work in economics or any other discipline anywhere in the world? Warren, that type of approach?

Warren Hatch  37:54

It’s happening at organisational levels. Okay, that’s, so we definitely see that, where were the things that are important to the organisation, they’ll use that kind of a framework to think about things, we also do it on our public site. And that’s one way to do this is that they could all just go and invent names, Mickey Mouse, whatever, and make a forecast on that very question on that platform, completely anonymously, and see how they do. The other thing, too, that I think is really interesting, is that often it’s rare, where even the word recession gets defined with some precision. Yeah, so one problem is that we all interpret it in different ways. We think of different thresholds, different, you know, different ways of defining what it is. So right from the starting gates, we are forecasting different things, and just having a shared understanding of what that means itself would do a world of good.

Gene Tunny  38:55

Yeah, exactly. I mean, there’s the is it two negative quarters of GDP? And I mean, you can get some odd results if you use that or is it is that the NBR declaring a recession? Yeah, you have to be very specific. I think that’s a good point. I just want to ask about the organisations that are doing this is is one of those organisations is it Bridgewater Ray? Dalio is Bridgewater, I’m trying to remember I read that in his principles book that he, he really tries to get people to be very specific and about what they’re forecasting or predicting.

Warren Hatch  39:26

They definitely do. They don’t work with us. But they’re doing it on their own. And obviously very successful at it by applying a lot of the same things that we’ve been talking yeah, here with with a lot of rigour

Gene Tunny  39:39

I might have revisit that book and just check whether that’s what exactly what they’re doing, but it just, yeah, it rang a bell in my mind that Oh, is that what Ray Dalio is doing? Because he’s very rigorous about in his thinking and questioning his judgement because he got something spectacularly wrong in the 80s and it almost destroyed I think it just destroyed his business at the time and so he learned a big lesson from that. Yes, yes. Okay,

Tim Hughes  40:03

wasn’t it 11 economists predict 11 out of seven recessions is that right?

Warren Hatch  40:13

Yeah, that was a great quote from an economist. Samuelson was his name. And he was writing in Time magazine in the 1960s. And and he that’s when he made that statement, that economist, I think it’s have predicted nine of the last five recessions and the ratio holds.

Tim Hughes  40:33

That’s, um, I love the idea of adversarial collaboration. I think that’s such a smart way to go around things and get better outcomes. And I think there’s so much to take from this. For everybody, like, way outside, the area of forecasting just seems to be a way to be a better human and to a good way to approach life. But so yeah, I’d really like to hear more about that. As you guys do more of that. We’d love to speak again, on the on that regard.

Gene Tunny  41:02

Yeah. Yeah, it’s been terrific. Warren, we really appreciate your time. So I’m really happy with that. And yeah, just incredibly grateful and excited. That was a really learned a lot. And I think it looks like you’re doing some great work there in the methodology or makes all makes sense to me. And it’s, from what I’ve seen over the years, I’ve understand why I’m thinking, why I’m forecasting or predicting certain things or what could be wrong with that question and trying to get other opinions. So yeah, and partly those because I read Philip Tetlock book, partly because I’ve seen the problems we’ve had with forecasting financial crises and recessions in the past. Yeah. So all great stuff and keep up the good work and really appreciate your time.

Tim Hughes  41:47

Just to finish off, I just want to say So one good judgement does work for people, if they want to work on a project, they can approach you guys how to how does that work? Warren? Is there a particular areas you guys work in? And how do people contact you.

Warren Hatch  42:01

So the way to contact us is we can just go to our website, good And reach out there. And we do we do consulting work on projects, where organisations may want to bring in some of these things and customise and adapt their own processes. They may also just want to have training workshops. And we do an awful lot of that, especially in finance and economics. That’s a big part of what we do globally. And the third thing is the super forecasters themselves, where we’ve got a subscription service on a lot of topics that are nominated by the user. So it’s a crowdsourcing of the questions as well as the crowdsourcing of the forecasts, as well as doing custom question work for organisations, as well. And I very much look forward to that.

Tim Hughes  42:50

Once I get going. It’s hard to get me to stop. You’re in good company at all,

Warren Hatch  42:54

I’ll look forward to to picking it up again, in due course, and perhaps even meet up. I’m working on a way to do a project over in your neighbourhood.

Gene Tunny  43:03

Oh, very good. Yes, definitely. Yeah, we’re in Brisbane. Yeah. If you get up here, that’d be great. So if you have an event in Sydney or Melbourne, just let us know. So yeah, we’ll have to talk more about that. Yeah. Good one. Yeah. Well,

Warren Hatch  43:16

we have super forecasters in Australia, including a couple in Brisbane.

Gene Tunny  43:20

Oh, very good. Okay. I wonder if I know them. It’s a it’s a little secret. Is it so hush, hush.

Warren Hatch  43:28

Ah, no, no, I’ll put you in touch with a male.

Gene Tunny  43:31

Very good. Yeah. Be very interested. Orrin Hatch from good judgement. Thanks so much for your time. We really appreciate it.

Tim Hughes  43:37

I predict that we’ll have another talk in the not too distant future. Okay,

Warren Hatch  43:42

I look forward to it. Thank you, Tim. Thanks. Good. Thanks, Ron.

Gene Tunny  43:51

Okay, I hope you enjoyed our conversation with Warren hatch from good judgement. To me the big takeaway from the episode is the importance of being open to a range of different views. Think critically about your own forecasts and be open to changing them if you hear someone making a compelling argument for a different forecast. I really want to put some of Warren’s ideas into practice, including the idea of a super Forecasting team. It wasn’t explicitly mentioned in the episode. But one important concept is the wisdom of crowds. good judgement is relying on groups making better forecasts collectively than any one individual. But as Warren mentioned, you need to set up a process or a forum for doing so which is meritocratic, so the group’s forecast is only influenced by the quality of arguments presented rather than by any biases. I must say I was glad that Warren said there is still room for numerical modelling as an input into super forecasting. I really liked his advice about the importance of getting subject matter experts and non experts together to come up with better forecasts. One thing I wished I’d asked Warren about is the distinction between hedgehogs and foxes. This distinction comes from the philosopher Isaiah Berlin. According to Berlin, the fox knows many things. But the hedgehog knows one big thing. Philip Tetlock who popularised super forecasting, he’s observed that foxes make better forecasts than hedgehogs. Someone who’s more widely read and thinks more creatively can be a better forecast. And then someone who has deep expertise in a field but who doesn’t take in a lot of inputs and views from outside of the field. This reinforces the needs to be open minded to think critically about your own thinking and to actively seek out other views. If you’re a subject matter expert, you need to make sure you’re open to other perspectives, and that your thinking isn’t constrained by the conventional wisdom of the discipline. Arguably, this was a problem for many economists in the lead up to the 2008 financial crisis. In my view, economists need to go out of their way to become more like foxes and hedgehogs. I’ll put some links in the show notes about the foxes versus hedgehogs distinction, along with links related to concepts covered in our conversation with Warren. One of the links is to a great article making better economic forecasts by my friend and colleague, Nicholas Gruen, who’s appeared on the show previously, next, a big fan of the super forecasting approach, and he wants central banks and treasuries to adopt it. In his article, he also writes about the potential benefits of running economic forecasting competitions. So please check out that article of next for some great insights. Okay, please let me know what you think about this episode. What were your takeaways? Would you like to learn more about Super forecasting? Would you like a closer look at some of the things covered in the episode such as Bayes theorem, feel free to email me at contact at economics I’d love to hear from you. rato thanks for listening to this episode of economics explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via contact at economics Or a voicemail via SpeakPipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it. Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.


Thank you for listening. We hope you enjoyed the episode. For more content like this where to begin your own podcasting journey head on over to


Thanks to Obsidian Productions for mixing the episode and to the show’s sponsor, Gene’s consultancy business

Full transcripts are available a few days after the episode is first published at Economics Explored is available via Apple Podcasts, Google Podcast, and other podcasting platforms.

WP Popup
Exit mobile version