How to Forecast the Future

will give us some of his best stories. Meanwhile, Lee book Eye, once dubbed the philosopher King of Sovereign Debt Lawyers, is coming out of retirement for us. Salton, posar of Credit Swiss and formerly of the U. S. Treasury, will be on stage with Brad Setzer, Senior Fellow at the Council for Foreign Relations, talking all things bonds and trade.

Will also have some markets the music courtesy of Moral Hazard, the most important country singer in economics, and an early Odd Thoughts guest, and even Joe has promised us a song or two. You can stick around after the show for some drinks and a chance to chat with us and our guests in person, so keep listening to Odd Lots to learn how to sign up to see us live on Thursday, September nineteenth. We hope to see you there. Hello, and welcome to another episode of the Odd Loots podcast.

I'm Tracy Halloway and I'm Joe wisnal So Joe. I feel and I think we've discussed this before, but it feels like the world is sort of at an inflection point right now. Uh you think, I mean you think so? Yeah. Isn't it always kind of an inflection point? Yeah, guess that's true. But at least in markets, it feels like, you know, warnings that were late in the cycle that we could get a recession at the end of this year or in those definitely seem to be heating up.

I think what I would say and where maybe we would uh most likely agree, is not that the world is that at an inflection point per se, but that what comes and goes is periods when suddenly people feel the turn is about to come. And we've had a series of these over the last ten years, whether it was Q four of last year early with the economy

going down, the euro crisis. From time to time, it's like there's this global collective anxiety that rises, and whether it's anymore real or not is up for debate, but I would absolutely agree right now you're getting a lot of like recession calls, bear market calls, started the easy cycle calls, things like that, A wave of global anxiety. That's a good way to put it. And this isn't really, as you mentioned, the first time that we've seen this,

and I'm not just talking about markets. We've seen sort of inflection points happen in politics recently, right, So Brexit springs to mind, the election of Donald Trump as well. Yeah, and I think the fact that we have another election coming up in the US again, it's one of these points where people wanted to call some sort of meaningful turn in something or or there's no doubt we're we're

hearing a lot of that these days. Right. So all of this is a roundabout way of saying that we're getting a lot of forecasts and a lot of predictions, a lot of people trying to call or see it into the future. And so I thought it would probably be a good idea to do an episode on forecasting. I love this idea. I mean, one thing that I've

always wanted to do and never done? Is you know, obviously on TV I talked to people all the time and they make forecasts like, oh, we're in this stock or interested in this sector, or we're telling clients to do this, and I've never gone back and it would just be too much work for me, and like actually made a database of all their calls. But I've always thought that's like a fascinating project or that you know

that because who knows everybody? You know, people just make these calls and how often do they ever get revisited to see if the person was actually right or useful in some way? Totally, And I have a feeling that our guests for this episode is going to have something to say on that particular topic. So without further ado, why don't I go ahead and bring him in. Our guest is Phil Tetlock. He is Annenberg University Professor at the University of Pennsylvania. He's also the author of numerous

books and papers on forecasting. So, Phil, welcome to the show. I'm glad to be here. So Phil, I guess my first question is, you know, one of the thrust of a lot of your work is that experts tend to get forecasting wrong. So is it weird? It doesn't feel weird that you're sort of the expert on why experts fail. Um. I guess I've gotten accustomed to it because I've been

doing it for about thirty five years now. I started out just after I got tenure at Berkeley and been tracking the accuracy of experts predictions pretty much continuously since then. So I'm sometimes called an expert hologist, which is, of course the field that does not does not exist. The basic idea of keeping track of of how accurate people

are is it? Is it really good idea? And and insofar as we all kept track of ourselves and the forecasting, making how well calibrated we are, I think the research suggests we would actually get a little bit better at it. So you've actually done what I've said for years that I've wanted to do, which is creates some database of forecasts and actually go back and look at who's right and who's wrong. How did you get the thought that that would be worth doing initially? And what does such

a database look like? Because most forecasts are not particularly binary. People might say I chance or seventy five chance, so usually things aren't just this will happen or this won't happen. And often a forecast can be wrong, but the methodology turned out to be right, or maybe someone unpredicted chance and that was still the right framework and the five percent odds did hit. So how do you go even go about constructing a database of forecasts and measuring what

turned out to be right or wrong? Well, that's what ten ures for it takes. It takes a long time. The key thing to note is that your right that there are only two conditions under which you can definitively say at particular forecast was right or wrong, and that is that the forecast it was rash enough to say a hundred percent chance and it didn't happen, or zero percent chance that it did happen, in which case you

know inclusively that that specific forecast was wrong. But a forecaster says, you know, there's a seventy percent chance, like Nate Silver said there's a seventy percent chance of Hillary Clinton winning the election in November? Uh was was was Nate Silver wrong? Or or do we happen to inhabit a world that was likely on November first? So the solution to the problem is statistical. You need you need

to keep track of lots of forecasts over time. If we collect hundreds of of your forecasts over the course of a year, and we find that when you say there's a seventy percent chance of things happening, those things happen about seventy percent of the time. We say there's a ninety percent chance those things happen about ninety percent

of the time, and so forth. If there's a close correspondence between your subjective probability estimates and the object of frequency with which events occur, you can be said to be reasonably well calibrated. And that's a very desirable feature of forecasters. So when you run your analysis, your statistical analysy says, of all these expert forecasts, and I'm assuming some non expert forecasts as well, what did you find? You find that people are not very well calibrated. For

a start, there's a lot of overconfidence. And when uh, many many experts would would would say things are an eighty or ninety percent likely or even likely, they would occur sixty or seventy percent of the time. So there were big gaps between subject of probabilities and object of probabilities. There's a lot of overconfidence. People were not qualifying their forecast appropriately, and that is one of the better replicated findings in my field, which is cognitive social psychology of judgment.

People are over confident, that tend to be over confident. Everybody's over confident. There are even there are a few souls who were even systematically under confident and who don't have enough confidence in their judgment. But the modal, if you had to bet on what kind of mistake people are making at any given moment, over confidence would be the better bet. You know, I think about something. One of our colleagues, I'll give a shout out to him,

Lorc and Roch Kelly. He works with us here at Bloomberg, and he's very fond of strategists at banks and other experts who love to give a forecast on things like, oh, we see a forty percent forecast of this person winning the election, or forty percent forecast of this country leaving the EU in the next five years, and it's like the perfect number because it's you know, it's still on. It feels unlikely, but it's close enough to that if it happens, you know, you can still say, oh, I

told you it was significant. But if it doesn't happen, you could say it was unlikely. And I'm curious in your findings and in your research. You know, we think of forecasts, our predictions is the point is to be accurate. But it feels like a lot of the reason people give forecast is just to be interesting, just to have their voice heard, just to get their clients to pick

up the phone. And I'm curious how that plays into your analysis of forecasts when the whole purpose is maybe not even to get it right, it's just to provoke a thought or to have your name out in the news. I think that's a very perceptive observation. It's a very delicate dance that people play. On the one hand, you want to say things that sound interesting so people don't roll their eyes and things that this is boring and

a useless conversation. On the other hand, you don't want to say things that are so interesting that they could prove to be wrong later. So you're there seems to be sort of in the sweet spot zone. Or if you were to use language, you say, well, I think there's a distinct possibility that Putin's next move is going to be on Belarus or on Estonia. Uh, it's a distinct possibility, is wonderful, and exactly the same way is

pretty good. I mean, if it happens, I say, hey, I told you distinct possibility, and if it doesn't happen, I said, I merely said it was possible. So you you're you're, you're covering yourself very nicely. It is as though the art of punditry is the art of appearing to go out on a limb without actually going out on a limb. So, I mean, Joe referred to forecasts

as non binary. But I feel like instinctively a lot of people want to know whether something will or will not happen, and yet we have all these, uh, forecasting calls that are sort of, you know, thirty percent chance, forty percent chance. Our probability is a cop out in that case, is it's something that people hide under. Well,

probable probabilities are not a cop out. If you're participating in forecasting tournaments in which we can systematically track how often you're things happen, and if things you say likely happen forty of the time, you're pretty well calibrated, and

it's not simply being used as a cop out. So, okay, So you've built this database and you've been tracking forecasters ability for years, and you mentioned that there are forecasting tournaments and we can really track this stuff, and that we can track how well calibrated forecasters are, not on any sort of individual prediction, but by whether over time their predictions of likelihood events happened seven out of ten

times and so forth. What are some of the interesting patterns you've discovered besides that people tend to be overconfident, what kind of people, what kind of approaches tend to distinguish the better forecasters from the worst ones, Because ultimately, I think that's sort of the point of your research.

There are there are two classic biases that we have found over the years, and one of them is that people are too quick to make up their minds, and the other is that people are too slow to change them. And it's the combination of those two things that causes

chronic over confidence. So I'm curious, beyond sort of individual characteristics that make people a good or bad forecaster, did you notice any discrepancy in the type of forecasts being made, Like, for instance, did political forecasting tend to be more or less accurate than something like economic or financial forecasting. Oh, it really depends on the timeframe and and the and the types of questions you're asking. I think economic and

political forecasting can be pretty hazardous to your reputation. I think what we noticed more than anything was that the types of forecasters who tended to be better did tend to be a little bit more boring. There they were more likely to say on the one hand, on the other hand, they were they were, they were, they were engaging in more explicit balancing and say, well, there's this causal for us, and there's that causal for us, and

you have to balance them against each other. So the types of forecasting talk that make forecasters appealing to the media tend to details that tend to make forecasts less less accurate. So a forecaster is going to be more appealing to the media, it would seem if they if they can come up with a compelling sound bite and they say something like, well, you know, I think the Saudi regime is going to collapse within the next twelve to twenty four months. That's that's a that's a very

dramatic forecast. It would have a lot of consequences for the Middle East and from World Politics. A forecast who says, well, you know, there are people have been predicting the major regime change in Saudi Arabia off and on for the last forty years. It hasn't happened yet. The base rate prediction is it is it's not very likely. Um, there are some reasons for some concern, but you know, you can steal your eyes start to glaze over. Listening to

the more accurate forecasters tend to to bore people. Yeah, So I'm just thinking so at the time that we're recording this episode, just in the last day, and by the time you're hearing this, this would be old news. But in the last day, uh Deutsche Bank, for example,

announced a major restructuring of its bank. And I'm just thinking about, how like the imperative for the news media is immediately to find people who will come on this morning and say something about whether this restructuring of the bank is likely to be enough, did it go far enough, will restore the bank to robust profitability and so forth, And it sounds like that imperative is almost exactly the

opposite of what's likely to make a good forecaster. And anyone who already has their mind made up but already has a strong view on the efficacy of the plan, at least going by your heuristic that the people that good forecasting is not correlated with a quick judgment or you know, quick decision making, which seems like we're kind of like highlighting most likely highlighting some of the worst

people we could be highlighting. Well, it really you have to make a decision about what kind of business you're in. If you're in the accuracy business, you're going to look for the kinds of forecasters we've been looking for in the work we've been doing with the intelligence community and elsewhere where. These are going to be forecasters who are not very entertaining. If you're in the entertainment business, you're

going to be looking for people who are entertaining. There's a separation in Bloomberg and probably mostly another sophisticated media comes these between analytics and and and the front end. Right, So what about experience, Like to what degree can if you're an expert, presumably you have you know, probably decades of experience, and you know you've been studying a particular subject matter for a long time, you've noticed patterns, or you can reach for historical analogies to describe a current

situation and extrapolate from that. Does experience help offset the problems of overconfidence at all? Sometimes it depends what you do with the experience. Um. People have different styles of thinking and and some people with experience become extremely skilled at creating very comp telling, very articulate justifications why they

must be right. Uh. So experience can actually solidify dogmatism for people with that cognitive style, and either for other people, experience melos of them and they become more tuned to the limitations of their prior world views and uh, they introduce more appropriate qualifications, may become better calibrated. So but but it's not a one trajectory. Be people people mature in different ways. Talk to us about how you train

people to get better. So obviously, like let's start with the assumption that there are some people that aren't just looking for media sound bites and maybe as uh to use your example there in the intelligence community, and they really want to make better forecasts about how things will happen in the future. They want to be better at predicting save Vladimir Putin's next move? What is that? How do you start and how do you what's the general

approach to becoming better at that? Well, I would say the starting point is again not going to be all that exciting and it and its bears a strong resemblance to what Danny Kahneman proposes in his best selling book Thinking Fast and Slow. Uh, it is start with the

base rates. And if you look at the base rates, you you'll see something quite interesting, and that is people frequently claim, to go back to the beginning of our conversation, people frequently claim that they're at an inflection point in history. If you if you look at how many inflection points there have been, it's just a very long list. The vast majority of claims about inflection points have been false positives. So you you would, naturally, I think, be wary of

claims about inflection points. Another claim you'd be wary of is military coups, the revolutions. They're relatively rare events. So someone who's making a dramatic claim but it's going to be a regime change in a particular country within a particular timeframe, the likelihood their being right is pretty low. Still.

Joe mentioned us in the intro, but what role do you think accountability plays in the sort of forecasting industry because it feels to me like, given the volume of media that's out there right now, you know, either social media or traditional forms of media, it feels like you have a lot of people who will make, you know, say a hundred predictions, and maybe one or two of those are right, and then they get trumpeted for those right calls, or you know, they laud themselves for those

correct calls, and people sort of forget about the other nine calls that were wrong, and no one bothers to go back and check on them. Because there's just so much forecasting and so much information out there in the world. So how can we develop accountability for forecasting by running

forecasting tournaments? Um? What prediction markets are? Forecasting tournaments are excellent ways of allowing people to tract their accuracy on judgment calls for which there aren't ready financial market equivalence. How do these I've never I'm not familiar with these,

So how does they are forecasting tournament work? You ask people to put subjective probabilities on events that are specified by well defined questions, such as whether Prutent is going to be the president of Russia after and those probability either in that in that case, it would be a probability that would be yes or no, and it should sum up to one point zero. There are lots of ways of doing it, but they all boiled down to

the core idea, which is which is keeping score. Sticking with the intelligence community framework, how does the role of group think play into this and avoiding group think? Because if we think back to what are considered a lot of the intelligence disasters over the last several years, the idea of some idea takes hold and no one feels comfortable yelling stop, and suddenly everyone can deals on the

same idea. Is that something that in your work you focus on sort of like these cascades where someone puts forth an idea and everyone feels compelled to fall online, or there's extreme pressure to voice a concern or voice skepticism on things. And are there are ways or strategies that aspiring forecasters can use to eliminate or reduce that bias. Group think is a big problem, and that's why in forecasting tournaments we typically have people make judgments independently of

each other, um at least initially. It doesn't mean that all team or group decisions are going to be bad ones, But it does mean that forecasters need to value accuracy above all. If your primary goal is pleasing your boss, and you have an opinionated boss, it's going to be very hard for you to offer that boss an opinion with a probability judgment that points to a policy different from what the boss prefers. So it it's another version

of the question what business are you in? Are you in the entertainment business, are you in the pleasing your boss business? Or you in the accuracy business. Accuracy business is often not the first business people are in, talking, not even the second business people are in. People are trying to have successful careers, they're trying to avoid embarrassment. There's a lot of other things people are doing a scie from accuracy. But so forecasting tournaments create a really

weird social environment. They create a world that's only one thing matters, and that is minimizing the gaps between your probability judgments and reality over the long term. And that's it. That's that's that's that's the sole objective I'm still trying to understand. So, as you point out, like forecasting tournaments are very weird because in the real world, that's not how predictions are made, and people are aware of what

other people are thinking and talking about. So when you consult and when you talk with people, how do you foster a culture? And I guess this is really what matters to the end consumers of your research, is how do you foster a culture where more people feel that they're in the accuracy business. It really helps if it comes from the top. People are looking at your typically look up for the normative cues about what's appropriate. So if you have a boss who's open to being wrong,

that that helps a lot. So, Phil, I know the majority of your work has to do with statistical analysis of probabilities of forecasts, but I'm curious, could you give us a sort of case study that you've come across of a forecast that has gone very, very wrong and that sort of brings together some of the themes or

lessons that you've been talking about. I can go back to the very beginning and when I was doing this work and everybody thought we were at a major inflection point, virtually everybody, and they were more or less right about it. I mean, the most inflection point calls are wrong, but they were right that the Soviet Union At the time I was starting off on this work was at an inflection point. People didn't have any idea where the Soviet Union was going to go. The Conservatives thought that the

Soviet Union was incapable of reforming itself. The liberals thought that Reagan was driving the Soviet Union into nio Stalinist retrenchments would become more aggressive. Yet Gorbachev came along in March of five. He became the General Party Secretary, and he proceeded, with Glasgow's embarrasster to liberalize the Soviet Union

in ways that we're really astonishing now. After the fact, the Conservatives said, hey, we forced them to do it, but they didn't really expect the Soviet Union was capable of reforming itself beforehand, and the Liberals said, well, we knew, we knew it all along because the Soviet economy was crumbling and the Soviets were needed to do this, and

Reagan had no role in it at all. So the paradox was that nobody was really very close at all to predicting what would happen, but everybody after the fact had a confident explanation for what would happen. Is there anything that people could have done better prior to the events unfolding that could have made their forecast better, or is it the kind of thing it was so novel and you know, it's kind of so unexpected that this would just be a really hard thing to forecast in

any meaningful sense. It was a hard thing to forecast. But there were clues that Gorbachev was different, and even a conservative like Margaret Thatcher was signaling that based on our early meetings with Gorbachev, I think that the key factor here is how fast are you willing to change

your mind in response to the the incoming evidence. So after Gorbachev became General Secretary in early there were there were lots of little bits of news that suggested this was going to be a different style of leadership, and maybe not just a different style of leadership, a different substance different that a different substance of policies would be pursued, and it would be it was your willingness, I think, to make small, rapid adjustments in response to the news.

It wasn't and there was any one big item that absolutely turned the case, but there were lots of little bit, little bits of news that created over time that good, good forecasters could it could attend to. And I think that's one of the finding features of the best forecasters is that they're more granular. They make distinctions among more

degrees of maybe than normal people do. An old joke in my field there's people can can really only distinguish three degrees of uncertainty, yes, now, and maybe the best forecasters are people who know the difference between a forty sixty bed and a sixty forty bed or even so it's some someone analog I said, the would be no doubt. For example, if I said, you know, good poker players could do that, and you say, well, sure, they must be able to do that in a repeated play game.

We get rapid quantitative feedback. But I said, well, good geopolitical and economic forecasters also do that, and you say, well, it cannot really be possible, and and answers, yes it can. So do you have a favorite forecaster? You know, it's like asking who are my favorite children? Right, I know I'm not I would I wouldn't take any particular person as a favorite forecasting but I think there are lots

of very admirable people out there. Well, in the beginning, you mentioned Nate Silver and his prediction or not prediction, but his assessment maybe a better way to put it, that Hillary had a sevent chance of winning the election. He's someone who has a very sort of clear understanding of probabilities and he puts h you know, he has a difference between eighty five and seventy and fifty and

twenty and probably fits very well into your database. By and large, do his seventy forecasts proved to be right about seven out of ten times. I have not analyzed Nate's data, but Nate does have data, and he analyzes it himself, and he has reported how well calibrated his boy is the forecast on the five. I tend to be in both sports and political forecasts, and I think they're pretty good, uh, in the sense of being well calibrated. I don't know the data on resolution, but on calibration,

they're scoring pretty well. There are two key fassets of being a good forecaster when you're doing subjective probability scoring. One of them is what I mentioned earlier, calibration. So when you say seventy percent likely to seventy percent, things happen seventy percent of the time. The other is resolution. Now, so there's there's a sneaky and lazy way of being

well calibrated. So if you're a weather forecaster in Seattle and it rains sixty percent at the time, and you say, hey, I'm just gonna say there's sixty percent likelihood of rain every day, and you know what, I'm going to be well calibrated because rain will happen sixty percent of the time. You you would be you'd be well calibrated, but you'd

be very uninteresting. So there's another property you need to ask a forecasters beyond calibration, and that is you need to ask them, are you good at assigning much higher probabilities to things that happen than to things that don't. Are you good at being justifiably decisive? So you want forecasters who are two things that you want there appropriately humble,

which means well calibrated, but they're also justifiably decisive. They say interesting, decisive things when they have a warrant for saying those things, so that it's a combination of those two things that makes some most a so called super forecaster in our work. And um, but I think that the Nate Silver group at five three is doing doing the right things and I think a number of other organizations are starting to do the right things as well.

So on that note, how should forecasters deal with tail risk events? Because of course, as you as you put it, you know, you could just sort of do an average of probabilities and you might look very smart and very well calibrated. But at some point there is a chance that a big unexpected event is going to come out of nowhere and sort of shift the entire regime of statistics in some way. How should forecasters deal with those

kind of unforeseen risks? I think you want to think in terms of shades of gray rather than black and white. It's not that things are there, there are some things are foreseeable and other things are unforeseeable. That there are black swans and then there are white swans. There are swans of varying degrees of grayness. And the best forecasters, I think, recognize that there is a continuum um and that tail risk is a problem, and you have to

judge how important it is. You will never miss a war, or you'll never miss a disaster if you always predict disaster, but that will that the cost you're you're paying in false positives is ridiculous. The question is how high a price are you willing to pay for making lots of false positive predictions about you know, the DATO is going to fall below two thousand in the next six months, that sort of thing, and and and and you know by by by futures contracts based on that belief. Skin

in the game, as it were. Those are judgment calls, and you never escape making probability judgments, even though the probabilities may be extremely small. Is very difficult to say whether someone is well calibrated and distinguishing between events that are one and ten thousand likely and one on a chillion likely. Right, But there's a huge difference between one and ten thousand and one and the chillion right. So, Phil, you mentioned you know, people predicting war or natural disasters.

It does feel sometimes like the people who forecast negative events, you know, recession is coming, war is coming, Donald Trump is up ending the global order, those sorts of things that they seem to make more waves or more in roads than people who predict either a status quo or positive trends. Do you think people like to hear dire

forecasts more than extremely optimistic forecasts. I don't know. If they like to hear them more, but they seem to find them more interesting and they pay more attention to them. Can the best forecasters, or the super forecasters as you call them, can they always articulate their approach or do some people who just have some sort of deeper intuitive sense. And I'm thinking about you use the poker analogy. And one of the things about poker is that there's different

ways to play it. So some people are extremely mathematical in their forecast, they calculate everything. Others seem too much more clearly operate on feel, and they just have a good feel for whatever reason that turns out to be a successful strategy for them too. Is there arrange in the approaches that people use? Or some people can very methodically lay out their approach like a date silver where they build all these models versus a more intuitive fuel

based approach that maybe can't be written down as well. Yeah, a long time ago, Malcolm Bladder wrote a book I don't know if you remember it called Blink. And there are some people in my fields who wrote a much less well known book. So a rejoinder called think You've Got a duel? Isn't here between people who endorse blink and people who endorse think I lean towards the think end.

I'm not precluding the possibility that when you're dealing with events that occur over and over again, and there's lots of repeated play, and there's lots of opportunity to build up deep experience and automated cognitive processing, that some people can become intuitively very good at it. Uh And they may even be doing rather complex calculations in their head very rapidly. So it's not that the intuition is is

eesp here. It could it could be that some of the best forecasters are not have simply overlearned the probability calculation heuristics to the point where they got they unfold virtually automatically. This isn't like a master pianist, right, It doesn't have to think about every key and just you know, the great tennis player doesn't have to think about where his arm is. That sort of thing. So, Phil, I feel like I have to ask, will you give us

a forecast? Okay, I'll give you a forecast, even though I'm not a forecast, just just for you, just for you, I will, I will, I will give you a forecast and that is I don't think the forecast and forecasting practices are going to change very fast. I think that the majority of people are making forecasts because they don't want to offend people in power, They don't want to offend clients because they want to be entertaining in media

performances or elsewhere. Uh. And the accuracy will continue to be a very, very secondary goal, but that gradually over the next ten twenty, through years of forecasting tournaments and prediction markets, will become an increasingly common way for people to resolve certain categories of disagreements. Well, hopefully your your appearance on this podcast will help marginally change the trajectory of the forecasting industry over the next several decades. But

really appreciate you coming out a fascinating, fascinating discussion. Thanks so much. Okay, take care. So Joe, I'm just going to point out that Phil's forecast did not contain a probability oh huh. Even though he did a pretty uncontroversial forecast, then he didn't put a precise number on it. But

I really, I really like that conversation. I think it's just a great topic because we see this all the time and not just in the fact that people come on give forecasts and never are really held accountable for them,

but just in what is the purpose of forecast? And how many times we all encounter people who give forecasts but whose job is clearly not accuracy, and I'm thinking a lot of their I think there are a lot of asset manager is like this who use stories as a way of gathering clients, and maybe they tell a bearished story or a conspiratorial story about the fed or whatever, but that story really has nothing to do with how

they then go on and invest. Right, certainly in the investment industry, there are plenty of people whose positions just don't add up to the world viewpoint that they tend to express, which you know, again, I would think of a lot of the really bearished people out there who for the past eight years have been saying, you know, move into cash by gold, the end is coming, and yet clearly they are in the business of putting cash to work in some way or another. So you know,

it seems a little bit out of sync. You know, we didn't you know, we only talked a little bit.

And I'm sure if we read his work, uh and studied it, there'll be more depth, but in terms of becoming a better forecaster, this idea of just sort of looking at the the the default and what point is well, coups are pretty rare, and wars are pretty rare, and regime change is pretty rare, and sort of starting there, I mean, another thing in stock markets is like bear markets are really rare, and stock market crashes are really rare.

And so this sort of idea of like, as you put in the beginning, and as we're talking about we're at a moment in which lots of people are talking about inflection points and it feels like things might turn. But just starting from this assumption that we're probably not an inflection point and that most of these turns that we think are they're bound to happen now probably won't

happen now, it's like it's an interesting starting point. I mean, obviously that's not enough, because sometimes the turns do happen, but starting from that assumption, it's like that you're probably the right move is to fade the expectations of a turn.

See it feels like an interesting toll hole to get into two then move from there, right, But see, I think this is where human psychology and references come in, because no one is going to remember or reward you for continuously calling the status quo correctly, but they will probably remember you if you did call the big regime

shift or the big bear market. And that's why so many people remember quite a few names that predicted the two thousand and eight financial crisis, but no one remembers as clearly, you know, people who called the gigantic rally that we had after two thousand nine the only you know, the only one, and like an exception to someone like Warren Buffett who just buy stocks and doesn't do anything fancy and has done so there's no seriously, like there's a few people like that. I feel like Warren Buffett

is always the exception. Yeah, that's true. Like you can't just point to Buffett, but that's true. All right. Well, on that note, h this has been another episode of the ad Thoughts podcast. I'm Tracy Allaway. You can follow me on Twitter at Tracy Alloway and I'm Joe Why Isn't Though? You can follow me on Twitter at the Stalwart. You should follow our guest on Twitter, Phillip Tetlock. He's at p Tetlock, and you should follow our producer on Twitter,

Laura Carlson. She's at Laura M. Carlson, as well as the Bloomberg head of podcasts, Francesca Levi at Francesca Today. And be sure to check out all of Bloomberg's podcasts on Twitter under the handle at podcasts. Thanks for listening.