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Welcome to, rationally speaking, the podcast, where we explore the borderlands between reason and nonsense. I'm your host, Julia Gillard, and today I'm talking with Professor Dani Rodrik. Dani is an economist at Harvard University's Kennedy School of Government. He is the author of many books, including The Globalization Paradox and most recently, Economics Rules The Rights and Wrongs of the Dismal Science. Danny, welcome to the show. Thanks for joining us.

Thank you. Nice to be with you.

I've been following your work and reading your blog for years. But the impetus for for me inviting you on the show just recently was that I just finished reading Economics Rules, which several of my very smart friends had highly recommended to me. And I really liked it. I thought it was a like an admirably nuanced take on how economics works and that you did just such a great job of pointing out some of the limitations or flaws in economics, but also defending ways in which it's maybe misunderstood by people outside of the field.

And regular listeners of rationally speaking, know that admirably nuanced is like the highest praise for me. So I I thought we could start by talking about one of the ways that you point out that people kind of misunderstand the point of economics, that that they often think that economics is trying to be a science in the way that like physics is a science in which physics is trying to discover natural laws of the universe that are kind of fundamental and unchanging. And people expect that economics is trying to do the same with uncovering natural laws of how economies work or how societies work.

What about that view of economics do you think is wrong? Well, I think there's a big difference between the physical universe and the social universe, and I think, you know, the social universe is in some ways infinitely malleable. And we actually take part in constructing and reconstructing and redesigning it over and over again. So I think it's it's the very nature of the social world that it is not fixed, that it varies and it's highly dependent on context.

And so I think that makes economics a very different kind of science, where I think any search for for, you know, universal truths or universal regularities is is bound to go wrong. And in fact, the best of economics is actually fairly contextual. We work with, you know, small scale models and specific causal chains that partially illuminate reality and make clear the dependence of that causal chain or that behavioral result is dependence on on the context. And I think sort of the clarification of of why is it that certain kinds of results depends on the premises.

And I think a key contribution of economics is a science. I have to say, though, that that that we economists are often our worst enemies and the way that we present our science to the outside world, we often do presented as that sort of, you know, kind of universalistic science. And in that sense, we don't do a very good job of portraying our discipline to the outside world. And I think often that's that's how we get wrong.

I mean, as I sometimes say, you know, it's the problem is not with our economics. The problem is what to do with our public relations and our marketing. And we need to work on that.

Well, so I'm sure many listeners will be they will have heard economists and and non economists even talk about laws and economics like the law of supply and demand, that kind of thing. Do you really think that there aren't any kind of really solid non contextual laws that economics has discovered or at least models that are contextual, but we understand how they're contextual, like in science, in physics. We you know, Newtonian physics is kind of contextual. It doesn't really apply at all scales, but we understand which scales that applies at.

So we know when, you know, to rely on it and when we're going to have to bring in something else like quantum mechanics. Do we have anything? You know, it's like supply and demand the closest we have or is there something else?

I think anything, you know, anything that that is really universal in economics that could really be called a law is so, so, so blatantly banal that it doesn't take us very far. I mean, you know, I think well, let me let me actually qualify that, because I think there are certain things in economics that are universal and they are still useful, even though at some level there but not. Let me give you a couple of things.

Examples, though. One of them is, is, you know, incentives really matter. People people respond to incentives. Now, at some level, this is completely banal. On the other hand, you know, we make so many mistakes in the world of policy and business and in sort of all our social relationships by ignoring this very simple principle that we don't actually think through how when we design policy, for example, how will people behave? So if you have you know you know, if you have you know, you test standardized tests in schools, you know what?

How will in fact, teachers then respond to the presence of these tests and that they will start teaching to the test and so forth. And so just just, you know, very simple principle, but but really takes us often quite far in ways that that if you don't take it into account, you would have you would have ignored. You know, the second principle is, you know, that that, you know, people you know that that giving people control over to return to their assets matters.

So sometimes this is like, you know, you know, is put in the form of property. Rights matter. And it's just that it's another version of the same principle of incentives mattering is just but the principle that if you want people to invest, you have to give them sufficient guarantee that they can return, that they can retain to return to their assets, to their investments. Otherwise people won't invest again. You know, seems like a very obvious thing that, you know, people care about, you know, being able to, you know, get rich on their investments or at least live off their investments.

But it's remarkable how many governments, you know, how many historical periods where basically just just ignore this basic message and and hope that economies would work out fine, even though we're sort of, you know, violating control rights, property rights contracts, and then just assuming that that would not be damage damaging to to the economy. So there are some universal you know, these are universal principles that are in some sense are completely context and institutional free. And I say that if I if I you know, if I, you know, get off the plane in a country that I've never been to before and they come to me.

Professor Roderic, what is it that, you know, we have to do to succeed in this economy? I can list off a few of these, you know, very broad principles, you know, protect property rights, make sure that the business environment is OK, ensure fiscal sustainability, regulate your financial intermediaries appropriately, make sure you take care about incentives. So these are all things that I can say without knowing much about the context. And I can't really go wrong because they are, you know, close to being sort of universal principles.

But but I haven't really help that economy a whole lot because, you know, it turns out that the way that I can actually implement these universal principles can be highly varied. So you can have, you know, people invest in, you know, in American type legal systems with exquisite protections for private property rights and contract law and so forth. But, you know, people can also invest in in countries that look very different. I mean, in Vietnam and China, where you essentially have socialist law and the and it is not that, know, people don't have the right incentives to invest, but that those incentives are actually sustained through a very different legal and informal mechanisms than they are in the United States or in Europe.

So that's where actually a lot of the contextual comes in. And, you know, you mentioned at the beginning things like, you know, the law of demand, of the law of supply. They're actually not laws at all because we know that, you know, even the most basic thing that we teach our students, that there's a downward sloping demand curve, that we actually have something called, you know, a given good, where in fact, that is not true, that, you know, that you can have an upward sloping demand curve.

And just to clarify, for people who aren't already super familiar with upward and downward sloping curves by downward sloping curve, you mean that as the price goes up, demand goes down. People want to buy less of something when it costs more in general except for them goods.

Yeah, exactly. Or that, you know, supply curves, you know, slope upwards and that when the price of something goes up that, you know, more of it is produced and supplied and markets. Now, just as again, in your example with physics, that Newtonian physics does not apply either at very small scales or very large scale. So but but we know exactly what's the scale at which it becomes relevant and where it's helpful to us.

I think economics does that in a way that sort of our frameworks, our models are essentially telling us under what context, under what kind of conditions they apply and what kind of conditions they do not apply. So I think it's that diversity that, you know, that multiplicity of models and frameworks and causal relationships that that actually makes economics interesting and useful. And the tragedy is that when we teach economics, you know, especially in introductory economics, you know, we are so hung up on just one benchmark model.

That's the perfectly competitive market with, you know, nicely downward sloping demand, nicely upward sloping supply. And the market solves all our sort of allocation problems. The markets are efficient. Government intervention is inefficient and undesirable. You know, all these things are hauled in in a very narrow set of circumstances. But as you learn more and more economics, you understand that actually most of what economists do. In the seminar room and into the graduate school and research is trying to understand exactly sort of the diversity of outcomes when those benchmark conditions do not hold right?

Well, I mean, a few minutes ago, you said that the real problem we have isn't with economics, it's with public relations. Do you, in fact, think that economists there are sufficiently attuned to the limitations of that model?

I mean, one of my you know, one of my concerns and I talk about this a lot in the book and also in my other writings, is that precisely this diversity of perspectives and this awareness of the contextual nature of our prescriptions or our results does not get carried over to the to the public domain. And there are a number of reasons for that. One is that, you know, for for one thing, I think the politicians and the media often, you know, do not have much patience for nuance.

You know, this is, you know, the your appreciation for nuance is not generally shared.

It's not a universal.

I found weirdly, you know, the last thing that a journalist wants to hear when they call you up and say, well, what do you think is going to be the effect of, let's say, the Trans-Pacific Partnership or what do you think is the effect of, you know, you know, increasing the minimum wage? And if you say, well, you know, the honest answer is it depends. So let me give you another 25 minutes, you know, lecture on, you know, what it depends on and how much each one of these things I mean, you're not going to get your name in the media with an answer like that.

And neither are you going to be, you know, sort of, you know, becoming a counselor or advisor or a guru or, you know, somebody that politicians and policymakers listen to.

Wasn't it that there are some president who who said, please, someone find me an economist with only one hand because he was so tired of economists saying, on the other hand, Dwight Eisenhower is supposed to be an economist, but that's really when we were doing our jobs.

He was saying on the one hand. On the other hand, now it's it's but but as I was trying say, it's more than on the one hand. On the other hand, it's just that, you know, it's we know you know, we know what those outcomes depend on. It's just that in real time, it's actually very different to sort out whether we live, you know, to use the new physics analogy, whether we use whether we're, you know, whether the relevant model is the one that we should use when we're talking about the planetary scale or whether we're actually in sort of very microscopic scale.

And, you know, it's in physics, it's easy to tell apart in the real world. It's not that easy to tell apart that, for example, the answer to the question of, you know, is the minimum wage a good thing or a bad thing for employment? Depends critically on whether you think that employers are behaving competitively or the way we put it in economics, not competitively or monopsonistic. That is like, you know, they have some kind of market power in determining the wages that they pay to the people they hire.

Now, it turns out that, you know, in the first case where where employers are behaving competitively, generally raising a minimum wage is going to be bad for employment. In the second case, where employers have some control over the wage that they can play, then in fact, the minimum wage can be a good thing for employment now. So we know exactly, you know, this very opposite outcomes, what they depend on. But it's much harder in real time to figure out whether, in fact relevant, you know, employers are going to be behaving one way or another.

So that's where I think the uncertainty arises.

Well, one of the points that you drove home in the book is that the practice of figuring out when one model applies or a different model applies or how much it applies is kind of more of an art than a science, which isn't to say that economists can't do it kind of reliably or they it's not to say economists aren't doing it at a better than random chance rate, but that explaining why why I am confident in this model in this case, and you're not confident in this model in that case is kind of subjective.

Am I am I conveying that correctly?

Yes, sort of. I mean, I think, you know, there's a quote that I discovered actually by Keynes. After the book had been published and, you know, as usual with Cange, you find that he said something that you were thinking of so much better than you could have said it yourself. But but he said, you know, economics is the science of thinking in terms of models joined to the art of picking the relevant models. And that's that's exactly the point I was trying to make in my book.

So I had I said, why did you need to write a whole book?

So you could have just seen this quote. I mean, I probably would have just written the sentence over and over again and not to write the book, because that's exactly so what he called the art.

I called the craft in my in my in my book. But so what he what he was getting at is that there's a lot of basically judgment and experience and sort of feel. And that is involved in figuring out whether in my sort of the example I gave of the minimum wage, whether we are operating in an environment where firms are competitive or they're monopsonistic. And so because or to take another example, you know, whether we should you know, we should engage in in deficit finance or fiscal reflationary.

Morgan, in the Keynesian environment, or are we more of in sort of a classical kind of environment? Again, depending on which of the two environments we're in, fiscal policy will have very different implications. And and the reason that's sort of an art or a craft is that in real time, you know, it's it's very difficult to analyse the you know, we have very little data. I mean, you know, we have very data that is very low frequencies or very few data points.

And it's very difficult to tell what kind of a world we're in now. You know, 10 years later, we might accumulate enough data and have even better statistical techniques after the fact that we might be able to to do that. But often in policy, you want sort of answers in real time, and that's going to be, you know, there's not going to be enough evidence to discriminate, you know, very clear cut fashion as to, you know, what what is the what is which is the relevant model, which is why I think a lot of the differences among economists are because they come with very strong priors and the real time evidence is not and sometimes even sort of evidence after the fact is not sufficiently strong to dissuade you strongly from your priors.

So if you think that if you're a you know, if you think that, you know, trade agreements have very, very important effects on long term long term growth and productivity and are certainly models of that kind, versus if you believe that, you know, trade agreements have limited effects on productivity and growth, but their first order effects on redistribution that are regressive and there are certainly models of that, too. I mean, you can keep on fighting this intellectual battle over and over and over again and never really reach a irresolution, at which point, of course, you know, a non economist would be just justified to say, but.

But what kind of a science is this if you can't actually ever narrow your your differences and you always, you know, can stick to your priors or to your preferred model?

Right. There's a phrase some people I know use that reference class tennis that refers to the disagreements that you can get into where, you know, you and I have different intuitions about, like whether this like startup is promising or whether this technology is ever going to be developed. And and we just keep sort of bouncing the conversational ball back and forth because I can feel like we are. But the startup has these features that like, you know, it's in this industry and like startups in that industry tend to fail.

And you can be like, yes, but it has like this the team has these features. And that makes me optimistic. And we just like keep feeling like different reference classes are relevant and those different reference classes are just different outcomes. And it's just a really hard question to settle if it could be found at all about which, you know, what's the right thing to use in forming our predictions.

But I mean but I think, you know, even in the worst case in economics, at least in principle, we know why we disagree. That is to say that that we know exactly what our disagreement hinges on so that when a Keynesian versus a classical economists are debating, they understand exactly what features of the economy they are disagreeing on. So that actually at least has the prospect of this debate being resolvable and the fact that often it does not should.

I don't think. We shouldn't necessarily hold it against, you know, we should also weigh that against the fact that often it does get resolved. So, you know, we have, you know, you know, in the 50s and 60s, for example, that there was this notion that, you know, peasants and agriculturalists in low income countries, you know, were very, you know, insensitive to price incentives, that they were really not, you know, sort of responding to prices or that, you know, they were to set in their old traditional ways of doing things and they wouldn't, you know, adjust if circumstances change, for example.

And a lot of development economics, for example, was built on that view of, you know, the traditional irrational peasant. And I think, you know, we accumulated enough evidence, you know, in the 60s and 70s to actually learn that that is not true. And I don't think, you know, you can get away now with sort of making that argument. So we have now, you know, we know that, you know, you can be very low income, but you're still going to respond to incentives and prices matter and things like that.

So we also do do do resolve some of these things over time. So and I think it's you know, it's it's again, in the worst possible case, at least, you know, we know what our disagreements depend on. And that's much better than than simply, you know, you know, just having these fights with, you know, no sense of, you know, where is this disagreement coming from is I forget his name, but it was the famous chemist who, you know, you know, used to to criticize or cut down his colleagues by sort of walking out of their seminars by saying, oh, you know, you know what he was talking about.

You know, he's not even wrong.

Right, MOFOKENG Polly. Polly. Yes, exactly. And and so, you know, I think that's that's one charge that I think, you know, economists can can avoid. And I think actually a lot of social science has that kind of quality to it, which is that you can listen to many talks and and not know exactly. OK, so under what circumstances might this actually be a wrong argument? I mean, how would I actually know whether this is right or wrong?

And I think the virtue of sort of economic models and the way we think is that that that we cross all the T's and dot the I's and we know exactly when it would be wrong to go back to physics again.

There is an expression that you can just add more epicycles to a model which comes from you probably know this, but for the sake of some of our listeners, earlier astronomers who thought that the planets revolved around the Earth in these circular orbits observe that that theory didn't match the data that they saw of how the planets actually moved across the night sky, but they didn't throw out their particular orbits around the earth theory. They just said, well, OK, maybe circular orbits around the Earth.

But also the planets are moving in these smaller circles as they're moving in the larger circle. And those smaller circles are epicycles. And that amendment to their theory allowed them to keep it, you know, in in spite of the contradictory data. And so I guess I'm wondering to what extent you think this also happens in economics where there are these these models and we get, you know, data and some economists can just say, yes, but and then propose, you know, a reason why we shouldn't expect that model to apply in this particular context.

But the model itself, the fundamentals are still strong. That to me, I feel like I've seen some of that. And it also seems to me like the kind of thing I would expect to happen in in a field like this, you know, even even assuming everyone is like very smart and trying to working in good faith, et cetera.

Well, you know, that happens actually I would say surprisingly little in economics. And I think that is because, you know, that that we have a habit of of in terms of working and very simple model. So simplicity is a virtue in what we do. And I think anything that you do to sort of say, well, I'm not going to add no layers upon layers to make this thing fit a little bit better, some kind of an anomaly.

It is not a style with which we work. So what happens in economics is actually that when you encounter an anomaly, you you know, you you develop a new model. And so let me give you the sort of example of. This theory of analysis of of markets and economics is actually a bit like that, where, you know, you have, you know, economists sort of develop one framework and then hit another anomaly and then they develop another framework.

So, you know, if you will, I mean, going back into the whole, you know, Adam Smith's invisible hand and, you know, was this notion that basically in competitive markets, you know, that that efficiency would be taken care of by simply these decentralized consumers and producers acting in pursuit of just their own self-interest. And you would get sort of markets produce this, you know, wonderous allocations that would be efficient, even though there wasn't any kind of plan or anybody ensuring that now, you know, very soon thereafter, already, you know, in the in the 18th in the 19th century, there was a lot of work in markets where, in fact, you know, you had reason to believe that, you know, producers were not behaving competitively.

So you had no models of Monopoly's, models of oligopoly, few producers. And, you know, but these were not you know, they were basically, you know, models that were not grafted on the Smithton model. They were alternative models. And in my in my terms, what I would say is that these models were useful because they would say, look, you know, the world doesn't always behave in a way that Adam Smith described. Sometimes they behave in the way, let's say Augustan cornered described, which was a model of a duopoly.

A French economist of 19th century. And then, you know, this is the Nicanor model is the one, the appropriate one. And you just have to figure out whether know the debate, the baseline conditions are you know, it's because it's more relevant to apply to Smithie, the model or the or the Kruno model. But it wouldn't stop there. I mean, you know, we had then, you know, all the way to, you know, you know, to take a relatively more recent example when economists began to sort of, you know, take information into account that there might be sort of there are goods that you cannot tell whether the consumer cannot tell all the relevant attributes of the good, whether it's high quality and, you know, whether you're buying a used car.

Has this card been used carefully? You know, sort of what kind of a condition is it? Is it a lemon or is it a high quality car? And then you get sort of models of, you know, you know, Koloff type models with asymmetric information. And but this wasn't just sort of grafted. And I don't think George Akerlof ever meant to say that. Let's say the market of, you know, four lemons, you know, is a substitute for the and model that said that, you know, no, it's just that there are some circumstances, such as when you're buying a used car or let's say when you're borrowing money from a lender, that there is such huge asymmetric information between the two sides of the market that applying the Smithers model is not going to make a whole lot of sense.

On the other hand, you know, if you're talking about the markets for apples and carrots, I mean, you know, maybe the smidgin model is OK. So I think there's there's a there's a, you know, sort of that's my preferred interpretation of of of economics, as you know, a series of of models developing over time that they tend to tend to tend to shed much better light to various sort of the variety of outcomes that we get.

And and but they're not meant just because we not have a model of imperfect competition or a model with asymmetric information doesn't mean that the essential insight of Adam Smith, you know, has been lost. It just means that, you know, we just need to be careful which model we're applying.

I mean, I could be misunderstanding, but it still feels kind of like like it has the risk of of the epicycles problem in that. I mean, so so you're talking about these models as separate models that are being being added to our set of of, you know, to our toolkit. But you can also conceive of it as as addendums to or amendments to the model, the model where you're saying like. Yes, but it doesn't apply in these particular cases that that seems like an amendment where, you know, if you take that to it, to the extreme, you're kind of overfitting.

You're like, you know, taking your curve and just like fitting it to every data point to the to the point where it's not helpful.

I think there's an essential, you know, philosophical distinction between saying that. There are universal laws of motion of planets, and what we're trying to do is derive those and with every epicycles, with every amendment, we're getting closer to actually doing that versus saying, which is what I'm saying, which is that, you know, this is not the right philosophy of science for for economics. You know, that's you know, that's just going to keep us, you know.

You know, first, you know, there is no reason why to think that there is a universal model of economics because we're talking about, you know, social reality and social reality is constructed reality. So it's you know, it's not it's not closed. It's not it's not a closed physical system. And secondly, because, you know, the more complicated and actually in some sense realistic you make your models, the less useful they get. I mean, this is another point I make in the book, which is that, you know, simplicity of these models, the fact that they capture only part of the reality.

But if you using them wisely, if, you know, doing your art and the craft correctly, that you actually, you know, applying sort of the relevant model is that it actually gives you an insight in a particular situation that are much more complicated. More realistic model would not. It's sort of like, you know, you're walking out of your house and you need a map to take you where you're going. Now, if you're going to be, you know, using the subway, you're going to take one map with you.

If you're going to be taking your bike, you're going to take a bike map. If you're going, you know, on the highway, you're going to take a map of the highway. And if you had a, you know, a universal map that actually had, you know, one to one every detail of all the bike paths and all the subway tracks on all the highways and all the sort of, you know, walking paths, and that would be very realistic.

It would be what also would be completely useless. You couldn't you know, it wouldn't be the world.

Exactly. Yeah. And and so and so you think that and I think economics is is the map for that kind of a world. And I think therefore it's useful only when it's simple. So its simplicity, its lack of realism is, you know, it's a feature, it's not a bug. And secondly, you know, it's an almost immediate implication. You know, you need to be you know, you need to be syncretic. You need to carry multitudes of these models in your mind that if you all just get fixated on the one, you're just going to keep getting the world wrong.

And going back to your to the point about it being a craft and not a science of when we apply the models, it reminds me a little bit. So so assuming that economists actually are they have some skill, even if they can't even if we don't have a precisely specified description of how to apply model such that it would be a science. Nevertheless, there is such a thing as expertise, like economists are better than random at figuring out which models to apply.

When it reminds me a little bit of chicken sexers, which sounds weirder than it is chicken sexers there, I'll explain. There are people who are able to tell what sex a chicken is like, pick up a baby chick, examine it, say this is male or female, which is actually much harder than you might just intuitively, naively guess does not like any obvious like thing that you can check. But some people are really good at reliably predicting whether the chicken is male or female, but they can't explain how they're doing it.

And all of the skill is happening at this kind of subconscious pattern matching level. So there's no, like, explicit body of knowledge that we could transfer from one person to another about how it affects the chicken. But, you know, especially now that that machine learning as a field is really taking off. And and we have so much data and computing power, I wonder if the kind of like like unconscious, like bodies of judgment that really good economists have is the kind of thing that could be externalized.

Like, do you see a future for economics where the process of deciding which models apply in which contexts is. I think that a machine learning algorithm with a lot of data fed into it could just do really reliably, even if it can't explain what it's doing.

Well, I mean, first, I mean, I think there's a sense in which we do economics and injustice, you know, and I know I've been using the term art and craft. But I mean, I think it's not just a you know, it's not a skill that cannot be taught and taught within economics. I mean, I think it is true that I think the best economists who do this, you know, have sort of developed this kind of skill over time without necessarily thinking too much about it.

But effectively, what they're doing is something that can be that can be taught because there's a method to it. And the method is it's an extension of what we were talking about before, which is that not that, you know, every model has not only relies on certain explicit premises, but also has a lot of implications about how the world should behave. And these provide, even if not formally, informally, a way in which you could test one model against others in real time in practice.

So there is a scientific element to that craft, except that because of the paucity of data, you cannot necessarily have a very definite answer. Or if your priors are very strong, they're not necessarily going to change a whole lot. But it is not simply, you know, you know, having some kind of innate skill and some people are better at it. And those are and it's something that I think can can be taught now. We do a terrible job, actually.

We don't do it at all in graduate school. And but but it is something that is actually more than simply, you know, just, you know, this sense, you know, a gut sense of of of what is the right was the right model.

But let me let me get back to you or sort of the questions about, you know, computational methods, big data, machine learning, or maybe to clarify that question a little more, I guess I'm talking about doing less explanation and more prediction on the margin. That's right.

So I think that's so that's a that's a big issue. And I'm I'm doubtful that you can have prediction without explanation in the sense that that, you know, you can have sort of, you know, a very complex pattern of correlations, you know, that keep on having very good predictive value. And then but if you don't understand what that depended on, it could be that from one day to the next that, you know, structure of correlations completely disappears because there is some change in the system.

And you don't understand that that existing structure of correlations depended on on, you know, that thing remaining constant. So so I think you certainly I mean, and that's, again, maybe a difference between scholarship and, you know, being an economic consultant, if you will, or an economic forecast or not. If I were, you know, an economic forecaster, you know, I would be very hopeful that that, you know, big data and machine learning and these mechanisms can actually, you know, provide a lot of useful guidance as to what's how I can predict to predict the future.

But the researcher and the scholar in me tells me that that's really not not science. I mean, that's it's it's that we can get, you know, that especially in economics, sort of you know, these regularities are highly specific to things that might be changing. And and so there's there's two things. One is, you know, we can have we can have systems that are predicting the right outcome time and again without providing any understanding. And so these are like black boxes and we don't understand why are we getting the right answer, but we're getting the right answer.

So that's unsatisfactory because, you know, we don't understand it.

And I guess you can't really intervene on the system if you don't know the causal structure, because you don't you don't have the structure of causality, then then you can't intervene because you don't know exactly, you know, exactly what a model does. What a simple model does is precise, to tell you what that effect depends on. And in these complex systems, you don't have that because now it's you know, I think I think people who do, you know, big data are increasingly realizing that.

So it's not I don't think that sharp distinction between. Explanation and and prediction is one that's really going to stay, it was one that's going to stay with us. I mean, I think in terms of doing science, I think explanation of causality will always remain a big part of it, because I think people who do that understand that if they don't have that, they don't have a clue as to why, you know, they're getting the results.

They are then, you know, they're going to be always surprised because things will change because we are not talking about, as I said, we're not talking about the physical universe where know ultimately it's all about in just a fixed set of rules. And, you know, we are conscious agents that are, you know, always remaking the rules and and adjusting to the rules. And I think that's, again, you know, provides for a much richer set of outcomes that that any given pattern of of correlations in any given time may not may not fully, fully describe.

And the moment we try to understand what a complicated model does is, again, it's just going to be in terms of these simple models. So, you know, you know, in my own field, for example, we have, you know, a lot of you know, what are called computable general equilibrium models of trade. So you want to understand, you know, what's the effect of NAFTA? What's the effect of the Trans-Pacific Partnership when you have, you know, you know, thousands of producers, you know, tens of different sectors and different types of labor and so forth.

So we have these, you know, highly complicated multi-sector models that try to generate, you know, implications for what's going to be the effect for the you know, for, you know, the manufacture of this kind of employment or that kind of employment. And you get some some numbers out. And the fact is that unless we can relate those numbers to some very simple models, that we can actually understand them in terms of those simple models, I don't put any credence at all on those numbers because otherwise they just blackbox.

And we have no idea what has produced them and they're not credible. It's only to the extent that you can explain those numbers in terms of simple models, simple benchmark models are we're getting this result because you see the presence of increasing returns to scale here is causing those kinds of adjustments and so forth. Then it sort of begins to make more sense and I think they get more credence. Got it.

We're almost out of time. I, I had originally thought that we would talk about epistemology for a little while and then also talk a lot about globalization. But I should have I sort of figured that once we started talking about epistemology, the time would slip away from me.

So I'm going to have to get you back so much rather talk about globalization, which I know something about it, and epistemology, which I know nothing.

Oh, no, you do. No. Well, I mean, maybe you don't call it that, but you're I would consider your book Economics Rules to be a book about epistemology. So that's how I'm using the term. But no, I'm very glad that we talked about that. That was really illuminating. And I'll just have to invite you back again to talk about globalization, because I have many questions about that. But before I let you go this time, I want to invite you to nominate the rationally speaking pick of the episode, which could be a book or an article or website or just anything that has influenced your thinking in some way.

Do you feel like you have a pick for us?

You know, one book that I keep coming back to when I think in, you know, and I think it's partly relevant these days, given where the world economy is, is is is the Great Transformation by Karl Polanyi. It was written back in 1944 and Polanyi was talking about how the an earlier era of globalization, the classical gold standard, came to an end in the early part of the 20th century. And he talked about the conflict of the the paradoxes of trying to have a liberal economic order.

You know, in a world where, you know, people are demanding control over their lives and have social systems and political systems that are diverse. And I think in many ways today we're facing up to the same challenge where we've we've tried to construct a world of globalization and trade and finance, you know, have markets go global while governance still remains very much local and national. And I think much of the backlash against globalization can be understood in terms of of that.

A famous argument that Carl Polonia put forth back in 1944 in that book, so it's something it's someone I would recommend to anyone who is trying to make sense of our world currently wonderful.

And coincidentally, I just ordered that book like two days ago. So that's that's very apropos. Wonderful. Well, Danny, thank you so much for being on the show. You're a terrific guest, so I hope to chat with you again soon.

Thank you. And thanks for having me. This concludes another episode of Rationally Speaking. Join us next time for more explorations on the borderlands between reason and nonsense.

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