The world is unfair — but how much of that unfairness is inevitable, and how much is just contingency? After centuries of efforts to arrive at formal theories of history, society, and economics, most of us still believe and act on what amounts to myth. Our predecessors can’t be faulted for their lack of data, but in 2022 we have superior resources we’re only starting to appreciate and use. In honor of the Santa Fe Institute’s new role as the hub of an international research network exploring Emergent Political Economies, we dedicate this new sub-series of Complexity Podcast to conversations on money, power, governance, and justice. Subscribe for a new stream of dialogues and trialogues between SFI’s own diverse scholastic community and other acclaimed political economists, historians, and authors of speculative fiction.
Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.
In this episode, we talk with SFI President David Krakauer about the goals of this research theme and what SFI brings to the table. We discuss the legacy of long-standing challenges to quantitative history and mathematical economics, how SFI thinks differently about these topics, and a brief outline of the major angles we’ll explore in this sub-series over the next year-plus — including the roles of dimension, causality, algorithms, scaling, innovation, emergence, and more.
Subscribe to Complexity Podcast for upcoming episodes with an acclaimed line-up of scholars including Diane Coyle, Eric Beinhocker, Ricardo Hausmann, Doyne Farmer, Steven Teles, Rajiv Sethi, Jenna Bednar, Tom Ginsburg, Niall Ferguson, Neal Stephenson, Paul Smaldino, C. Thi Nguyen, John Kay, John Geneakoplos, and many more to be announced…
If you value our research and communication efforts, please subscribe to Complexity Podcast wherever you prefer to listen, rate and review us at Apple Podcasts, and consider making a donation — or finding other ways to engage with us — at santafe.edu/engage. You can find the complete show notes for every episode, with transcripts and links to cited works, at complexity.simplecast.com.
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Mentions and additional resources:
Emergent Political Economies and A Science of Possibility
by David Krakauer for SFI Parallax Newsletter, Spring 2022 Edition
Policing stabilizes construction of social niches in primates
by Jessica Flack, Michelle Girvan, Frans de Waal, and David Krakauer in Nature
Conflicts of interest improve collective computation of adaptive social structures
by Eleanor Brush, David Krakauer, and Jessica Flack in Science Advances
The Star Gazer and the Flesh Eater: Elements of a Theory of Metahistory
by David C. Krakauer in History, Big History, and Metahistory at SFI Press
The Cultural Evolution of National Constitutions
by Daniel Rockmore, Chen Fang, Nick Foti, Tom Ginsburg, & David Krakauer in SSRN
Scaling of Hunter-Gatherer Camp Size and Human Sociality
by José Lobo, Todd Whitelaw, Luís M. A. Bettencourt, Polly Wiessner, Michael E. Smith, & Scott Ortman in Current Anthropology
W. Brian Arthur on Complexity Podcast (eps. 13, 14, 68, 69)
Reflections on COVID-19 with David Krakauer & Geoffrey West (Complexity Podcast)
The Dawn of Everything
by David Graeber and David Wengrow at Macmillan Publishers
Mitch Waldrop speaks on the history of SFI (Twitter excerpts)
The Hedgehog and the Fox
by Isaiah Berlin
War and Peace
by Leo Tolstoy
On the Application of Mathematics to Political Economy
by F. Y. Edgeworth in Journal of the Royal Statistical Society
How Economics Became A Mathematical Science
by E. Roy Weintraub at Duke University Press
Machine Dreams
by Philip Mirowski at Cambridge University Press
All Watched Over by Machines of Loving Grace (TV series)
by Adam Curtis for BBC
Can’t Get You Out of My Head (TV series)
by Adam Curtis for BBC
The Collective Computation Group at SFI
Seeing Like A State
by James. C Scott at Yale Books
Uncertain times
by Jessica Flack and Melanie Mitchell at Aeon
At the limits of thought
by David Krakauer at Aeon
Preventative Citizen-Based Medicine
by David Krakauer for the SFI Transmissions: Reflections series
The uncertainty paradox. Can science make uncertainty optimistic?
by Stuart Firestein (SFI Seminar)
Editorial note: At one point DK mentions "John" Steuart but meant James Steuart, author of
An Inquiry Into the Principles of Political Economy
(a more thoroughly-indexed and searchable version can be found here)
David Krakauer (0s): There's a sociology of science here. As part of the reason we like one dimensions is we can visualize them so easily. So you can turn on the TV and see a secular trend in one dimension. But what happens if the reality is seventh dimension? What do you do then? Well, we shouldn't allow the expediency of plots, our need to visualize, diminish the richness of our theories. And I think so dimension interest me a lot. Causality, the history of economic metrics is projecting to dimensions.
So there is an independent variable and there's a dependent variable, and this is what we're going to explain. And we're going to do it with linear regressions or whatever. No, we're dealing with complex causality and there are two ways to do this. One is to say, well, we can't do that. So let's pretend it's one dimensional. Or wait a little bit like the machine learning crowd. No, we're going to embrace the dimensionality and we're going to forfeit comprehensibility. And there are people like Ricardo Hausmann, Cesar Hidalgo, and others who have created a much more subtle notion of product spaces to understand trade, which forfeits a little bit of the transparency but to integrate into the theory complex causality. That's something that we like to do.
SFI has been very willing to sacrifice elegance for the altar of truth.
Michael Garfield (1m 45s): The world is unfair, but how much of that unfairness is inevitable? How much is just contingency? After centuries of efforts to arrive at formal theories of history, society and economics, most of us still believe and act on what amounts to myth our predecessors can't be faulted for their lack of data. But in 2022, we have superior resources. We're only starting to appreciate and use. In honor of the Santa Fe Institute, new role as the hub of an international research network, exploring emergent political economies we dedicate this new sub series of complexity podcast to conversations on money, power, governance, and justice. Subscribe for a new stream of dialogues and trial logs between SFI’s own diverse scholastic community and other acclaimed political economists, historians and authors of speculative fiction. Welcome to Complexity, the official podcast of the Santa Fe Institute. I'm your host, Michael Garfield, and every other week, we'll bring you with us for far ranging conversations with our worldwide network of rigorous researchers developing new frameworks, to explain the deepest mysteries of the universe.
In this episode, we talk with SFI President David Krakauer about the goals of this research theme and what SFI brings to the table. We discuss the legacy of long standing challenges to quantitative history and mathematical economics, how SFI thinks differently about these topics and a brief outline the major angles we'll explore in this sub series over the next year, plus including the roles of dimension, causality, algorithms, scaling, innovation, emergence, and more. Subscribed to Complexitypodcast for upcoming episodes with an acclaimed lineup of scholars, including Diane Coyle, Erik Beinhocker, Ricardo Hausmann, Doyne Farmer, Stephen Teles, Rajiv Sethi, Jenna Bednar, Tom Ginsburg, Niall Ferguson, Neal Stephenson, Paul Smalldino, C. Thi Nguyen, John Kay, John Geneakoplos, and many more to be announced.If you value our research and communication efforts, please subscribe to Complexity podcast wherever you prefer to listen. Rate and reviewus at applepodcasts or spotify and consider making a donation or finding other ways to engage with us at santafe.edu/engage. You can find the complete show notes for every episode with transcripts and links to sightedworks at complexity@simplecast.com. Thank you for listening. All right, David, here we are, introducing the new sub series for this podcast on emergent political economies.
So we're going to just do, as we did with the COVID series and hit the basis on this and talk a little bit about what this new side channel is going to be for Complexity podcast, how it ties in with the new program at SFI, give people a tour of the themes that we're going to touch over the next year on the show, and why don't you take it?
David Krakauer (4m 52s): So this is a new theme at SFI. It is funded by the Omidyar network. SFI is one of several organizations working on this constellation of problems. The other's largely funded by Hewlett that includes Harvard Kennedy School, Howard, MIT and John’s Hopkins. So it's sort of an interesting constellation of universities and institutes or one interested in this problem. And it's not surprising I think to the listeners. It's about emergence economics, but perhaps less familiar from SFI as history is politics.
And that's obviously an interesting and rather treacherous domain. So I want you to give a little bit of background on political economy and to place us in a chronology or a history. So the founding text in some ways is a text by John Stewart. It was published in 1767 and it's called An Inquiry into the Principles of Political Economy. And now John Stewart was a mercantilist. That's the sort of belief that you maximize national self-interest by maximizing exports and minimizing imports.
It's a fairly onerous actually rather disagreeable philosophy that's interested in colonialism and imperialism, the imposition of tariffs. But what John Stewart did in that founding text is realize that the essence of political economy is about what he called relations, relational problems, what we would call distributions or inequalities. And he says, and it's going to quote something from the book, “all actions and all things indeed are good or bad by relation only nothing is so complex as relations when considered with regard to a society.”
So he was saying, he actually nailed it. He said, how do we deal with inequalities? How do we deal with distributions in our societies? And one thing he also said, despite his mercantilism is the point of political economy is to provide food other necessaries and employment to everyone in society. So his ultimate objective was I think a very humanistic one. Now that position was taken up and significantly modified by Adam Smith in the Wealth of Nations. Adam Smith was vehemently anti-mercantilist. He thought it didn't solve the problem of relations.
And he thought the market would do that, that capitalism would solve the problem of inequality. And I think we all know that that isn't true, but it's interesting to point out that Adam Smith, again, made very clear that the ultimate goal of political economy was moral and that it was the responsibility of every citizen in a prosperous society to contribute to the collective wellbeing of society. So for him capitalism and free markets, weren't about selfishness necessarily. It was just a better solution than mercantilism.
And I think that's failed. And most recently, and I think we'll discuss this, this has been taken to another level by the uber text of our time, The Dawn of Everything by Graeber and Wengrow, who were also interested in this problem of relations, inequalities, but through a different lens, which is looking at our myths, if you like, or historical myths about political economy, which posited in some sense, a golden age or a dark age from which we've made progress.
And in the process, they essentially deconstruct a whole set of other narratives that we've taken for granted about the origin of the modern state with its focus on redistribution economics. So it's a very interesting history. It's very, very rich. And the question is for us, you know, why SFI? And if we get into a conversation, I just want to say, I think that why SFI, because the kinds of frameworks that we work on, collective intelligence, scaling, modeling through agent-based models, the whole panoply of if you like frameworks and tools of complex systems are necessary to answer the questions that Stewart and Adam Smith and Graeber and Wengrow are asking.
And so it's a necessary move to integrate those big epic questions with the kinds of rigorous methods that we've been developing.
Michael Garfield (9m 23s): So it seems to me to make sense to tie this in, not just because I want to stump the book from SFI Press, but because this question of how we think about political economies is very intimate with, and kind of rooted in the way that we think about history. And when you're talking about this tension between free market and mercantilism, it's akin to attention that you bring up in this essay in a history, big history and meta history on the stargazer in the flesh eater, as you put it, which actually bringing us back to episode one of this podcast, when you were talking about the difference as you see it, between the sciences and the arts and humanities.
On the other side of the spectrum, you've got the stargazers, which are the scientists who are searching for regularities in the historical record. And then you have the flesh eaters, which is the humanistic scholars that are trying to think about history in terms of narrative and thus end up sort of losing regularities in stories of unique historical events or historical great figures. And so you compare this to the question about levels of selection in evolutionary biology.
And to what degree can we tease out these causal relationships? You critique Richard Dawkins, as somebody saying the gene is the appropriate level at which to look at these things. And so it seems that these tensions in the history of thought around political economy tend to sift out in a very similar way, which is people not agreeing on the appropriate methods by which to understand these things and therefore, and then also within that, not being able to agree on what forces actually matter in history and therefore what forces we can emphasize or isolate for study that are going to help us understand how to actually operate on historical patterns in a way that gives us instruments, that we can actually apply in these areas.
So, and then just one more thing to stack on that is John Lewis Gaddis in his contribution to this volume on war, peace and everything, thoughts on Tolstoy has this great passage from a warrant piece about Boradino and how a Napoleon sends these people out in battle. This is a cliche in military strategy that by the time the message gets to the front lines, it's stale. And so there's these questions of to bring it kind of closer into the ambit of SFI, these questions about networks and latencies and networks, and also a tension between the way that top-down and bottom-up forces work in regulatory structures and then processes of collective intelligence.
And so maybe that's a bit too much to bite off all at once, but it seems like that's a good place to anchor into questions of how do we think through not only the question of regularities and history, but then also this question of, as you just sort of laid it out, this tension between agency at different levels of society and economy and the way that these things are perhaps innately at odds with each other, not only in the way that we think about them, but in the way that they actually play out in history.
David Krakauer (12m 44s): So there's a bunch of things to say. It's interesting. So one thing to say right at the start is that in some sense, all of these projects, political economy in particular are trying to reconcile our beliefs about individual morality with the mechanics of the institutions that we live in and Stuart, Adam Smith, Graeber/Wengrow disagree about the mechanics of the institutions. That's where I think they disagree. And it's interesting that you mentioned John's article or essay in our book on big history and meta history.
I mean, John is referring there to epilogue to of in War and Peace where Tolstoy does this strange thing where he rates a narrative history of statistical mechanics in history. It was made famous in fact, that particular epilogue by Isaiah Berlin in his book, the Fox and the Hedgehog where he actually delved into it deeply, which has that same character which is a multiplicity of causes, the multiplicity of ideas versus a single big idea, which is bled shock versus the fox. And in a sense you're right.
I mean, this is like a fractal that all of complexity science in the end is trying to reconcile single factors, which have a disproportionate causal role versus this complex causality where it's distributed over many, many, many agents and Tolstoy comes to this conclusion that we lie to ourselves, that the Napoleonic complex of a single individual in control is not actually tenable by virtue of the kinds of mechanical and obstacles that you just described, like latencies.
So he says, it can't work. Interestingly Graeber and Wengrow channeling Todd's story. I mean, very much that the right way to think about history is in terms of this de-centralized, co-operative, discursive experimental playful model. It's not all about people coming in and imposing their will from the top. One thing that we've talked about, which I find striking is that this particular view of history was shared by the left and the right. We would all say, right, that Graeber is clearly of the left is incredibly important role in occupy Wall Street, these movements and their story is a story of distributed emergent co-operative polities largely through an indigenous lens, which they argue rightly I think have been ignored to our peril. But so does Neil Ferguson make this point? So in Neil's book, The Square and the Tower, he makes a contrast between the tower from which you command above or the town square, where people negotiate solutions to problems. And his argument is very much epilogue to one piece. We've told ourselves if fictitious history. So the right and the left share more in common.
And I think this is a part of, to be honest, a reconsideration of history through a more complex lens, because we clearly oscillate between these two states and historians perhaps have overemphasized the role of the rational leader and diminished the importance of a more democratic means of arriving at solutions. So that's very interesting and I think you're right, and it's sort of interesting that the debate will go back to Tolstoy.
Michael Garfield (15m 59s): That also gets to something that you've spoken about plenty elsewhere, which is actually just talking about this the other day when Mitch Waldrop was here for the postdoc conference and people who are visiting here for that conference were asking Waldrop about how SFI has changed over the last few decades and whether it was still the case here that what SFI is after is sort of what it became known for in the nineties was tilting its spear after this windmill of a unifying theory.
And, I've heard you say on plenty of occasions that it's still a nice goal, but often what we actually find here are not in some sense, like positive certitudes about that so much as bounds, limits and constraints. And so, you know, I'm thinking about again, when we talk about top down, bottom up, one of the ways that we can think about that is in talking about not what can be done from the tower or necessarily even strategies that can be implemented from the square, but bounds to the efficacy of either strategy.
And, and so, you know, you say here and as does Murray Gell-Mann and his contribution to this piece, that if Pareto's power laws really a robust regularity, then it may not be so wise to try and fight the power law itself by means of public policy. Rather one might try to reduce the exponent, thus flattening the curve, which is a kind of a funny way to put it, cause this, you know, this is before COVID and flattening the curve became such, but this is again, this question of what, what can we, what are we trying to do with this research?
In what way do we see this as informing rather than perhaps like shaping or dictating policy? And then how does that have to do with this sobering realization over the last few years that what, one of the things that this place seems to be good at is not so much arriving at a final statement that brings everything together so much as offering a boutique of different perspectives from which to consider a problem.
David Krakauer (18m 18s): So two, three maybe points there. One is about Mitch's point about SFI has transitioned into the empirical world, moving away a little bit from these grand unified theories that are indebted to physics envy, and then what, why are we, why are we playing in this arena at all? And I think you raise a really interesting question that Murray articulated with, which is in this domain of political economy, what is law,like in other words, what is hard to change versus what can be changed?
And again, Graeber/Wengrow, their whole premise is the world is much more malleable than we have described it as. We like these theories that make it look more like it's worth bearing. I want to introduce some facts here because I'm in this sort of style of pickety why are we worried? And I just want you to go through some fats and get to this point, which you're raising, which is, which of these facts do we think are if you like contingent and which of these are more closer to universal regularities?
I should say, read it ahead of time. I believe many of these things are rather contingent. So let's just go through the things that motivate I think the institutions on the immersion political economies project. Well look here we are considering wealth. So half of the world's net wealth belongs to the top. 1% has been said many times. Top 10% of adults on the planet hold 85% of the wealth. North America holds about 30% of the world's total wealth. And it's less than 5% of the world's population. Europe, about 25 it's 10% of the population. So the Western world alone accounts for over 50% of the total wealth. Now that's just one example. Is that inevitable, does that come out of some law or is this some historical accident that could be undone? So you look at Africa, counts for about 17% of the world's population, but less than 1% of the world's wealth. Clearly that's a consequence of historical colonial policies in large part. What could we do to change it? China accounts for about 30% of the world's CO2 emissions, but less than 20% of the global population.
Income disparities on average globally, women make about 75% of what men make. In the USA it's about 83 and you can keep going on and on, access to healthcare patent production, trademark production, racial disparities. The USA has the highest incarceration rate in the world. 650 people per 100,000. Canada by contrast is like Europe about a hundred. And then of course the racial disparity are huge. One in 17 white men, I'd likely to experience imprisonment as suppose one in three black men and women much lower and so on.
So you look through all of this data that people they pick. It you're very fond of providing us with, and you have to ask that deep question, what is it about the system that leads to these outcomes and how much of it can be changed? And I happened to be of the opinion that much of it both can be changed and should be changed, stressing the moral side of the political economy equation, but we have to get the institutional mechanics right. And what we don't want to do in the process is discouraged individual creativity and innovation. We don't want to impinge on individual freedoms, but we do want to respect as Smith did the well-being of society as a whole.
And so I think those figures whilst they feel sterile establish the context of the problem that we're facing. And the role of science is to ask, is this expected under some null hypothesis or are there irregularities in our history that we could modify to lead to more just outcomes? So I think that's establishing the boundaries.
Michael Garfield (22m 9s): So there's another piece in this volume that Geoffrey Harpham from the National Humanities Center writes in critique of your contribution and talks about the history of a once promising, but ultimately doomed effort to try and bring a narrative and a quantitative approach together. And try to understand this stuff. It talks about philology and philology as a discipline erased from the pantheon of natural sciences originally looked for explanatory power in understanding the history and the relationships of languages, and was ultimately appropriated by racists and fascists and became kind of the opposite of its promise.
So part of this question of where we sit with this program today is in all of the inequalities that you're talking about are in part due to the way that the search for these regularities and this understanding was abused in the past. And so I would be curious to hear again, in situating ourselves in this history, how you see as SFI's approach is distinguished from earlier attempts at the same goal, and also to connect that, not just the study of history, but to the study of economics and earlier attempts to mathematize economics,
David Krakauer (23m 45s): That's all good stuff. And so I should say it's Geoffrey who contributed to that but that. The book we're talking about is a book that I edited with John Gaddis and Ken Pomerantz. And I asked Geoffrey to contribute that article because Geoff is a critic of scientific reasoning in the human domain. And he gives the very good example. I think of the misappropriation of Darwinian thinking in the social sciences, leading to eugenics and race theory. And as you exactly, as you say, this was a utterly failed project based on completely fallacious premises that try to explain current inequality in terms of a deeper biological principle.
And of course we know it's completely bogus, but the question is, are we doing that again? In other words, in our efforts to be scientific about social phenomena, are we making assumptions which are not justified and that will prove to be over the course of time as, as flawed as, as those projects that Geoffrey described. I think it's extremely important cautionary principle. I do think that we are now in a position to be much more evidence based. There never was any evidence in support of race theory.
It was a highly selective argument and the kinds of data that Pickotine and others are talking about are the real world statistics that we have to reckon with. I think that's the most important thing that we keep ourselves honest by virtue of the evidence. It doesn't solve the problem. I think the second one to your point about the sort of plurality of models, as opposed to the grand unifying theory, I think that in this particular project, SFI is value. I think to the debate, given that we aren't, some of our faculty are, but as an Institute experts necessarily in political economy is to introduce into the debate subtleties and complexities of reasoning that could be useful to people who are wiser than us when it comes to application. Let me give you three areas, which I think define SFI’S contribution to this project. One is about distributions of relations, distributions of income, energy, food, education, disparities of race, gender climate vulnerabilities. The shift in our thinking from the parametric normal distribution world, the bell curve to as you described power laws of various kinds are more sophisticated attitude towards random processes that generate distributions of outcome, something that we've worked on a lot in a whole range of different topics from biology through to cities. There are insights there I think that could be valuable. The other one is invention and discovery. What are the conditions that favor the emergence of new solutions both in deep time as we've studied them, as you have, for example, in paleontology and biology, but also in cultural evolution and issues of levels of selection, as you say, conflicts between parts and wholes and the underlying configuration space of invention something we've worked on a lot. And many of our faculty, including people like, you know, Andreas Wagner and Douglas Irwin have done a huge amount of work to try and formalize what we've mean by invention. So that I think could be quite useful. And finally emergence. I mean, much of what we're talking about here are functions of the sun contributions of many, many factors. And it's a little futile to imagine that we could dissect them into their single dominant causes.
How should we then think about intervening in a system that has thousands, if not tens of thousands of variables or parameters. That is what we work on. That's in some sense, constitutive of what we mean by a complex system and the methods that we've developed, like network theory, are efforts to remain true to that fact. So I think it's a kind of methodological theoretical model based approach where we're just trying to contribute modern styles of rigorous thinking that we hope will be useful.
I mean, they might not be, I think we should be candid about that. And it motivated, I think by our individual moral concerns, which I think are fair that the world is not as just a place as it might be, and that we're leaving a planet to future generations that could be in crisis. I think there's no denying the our belief in the urgency of a rethink or a reboot.
Michael Garfield (28m 13s): Indeed. So now seems like a decent time to take a turn. And, and we've talked about this on the show before with Brian Arthur, with Doyne Farmer and others about the history and the theoretical successes of complexity thinking in economics. But I would like to go a little deeper with you on this particular piece, just to give us a little bit more solid footing in the way that SFI thinks differently about these ideas compared to the way that they have been historically attempted with any economics specifically, to bring a kind of a statistical lens to this.
And again, like you just said to understand the interplay of all of these variables in their relations with one another and emergent phenomenon that occur and the nature of invention and innovation.
David Krakauer (29m 1s): Let me talk about that a bit. And of course people would have different narratives here, but I'll give you mine. I think one of the key papers, that's not as well-known as it should be, was written by Francis Edgeworth in 1889. And the paper’s got a wonderful title. It's called Points at Which Mathematical Reasoning is Applicable to Political Economy. So it couldn't be more apropos and Edgeworth makes three points in this paper. He says, first of all, at the time he was writing it, which is in the late 19th century, there's not enough data to develop a real theory.
He meets this very explicit. He also makes the point, and this is his language. It's not clear what the prime factors are. And he makes a third point that economics should be about models, not general theories. It's kind of a market prescient piece. He sounds an awful lot like us. You know, he also goes on to talk about non-numeric mathematics. He has this wonderful phrase. He says, mathematical political economy. It's not only about political arithmetic, but economic algebra, so logic and not only quantification.
And that brings us to this very interesting fact about modern economic thinking. And I think Deirdre McCloskey does a brilliant job in a book that she wrote called Knowledge and Persuasion in Economics, where she talks about this influence of rigorous mathematics and economic thinking that derived largely from logic rather than the more empirical basis of the natural sciences. And this is a path that it went down, which culminated in one of our founding faculties work and arrows work, the so-called Arrow and Debreu, competitive economics, competitive market theory published in 1954, which they won the Nobel prize.
Now this is very interesting. This is a body of work, which is entirely based in mathematics, rigorous, pure mathematics. There's no dynamics in this work. There's no demonstration of the stability of equilibria. It's almost set theoretic. It's very indebted to the movement called bull barky, which was an effort to really actually monetize all of mathematics. Something that Murray has actually attacked impressed many times. And so economics became rather decadent because people talk about physics envy, but it's not really true.
It was really pure mathematics envy. And I wanted to mention two books. I think that are worth mentioning here in this regard. One is a book by Roy Weintraub called How Economics Became a Mathematical Science that looks through this emergence of a powerful, pure mathematics that was a counterbalance to Marshall's mathematical economics based at Cambridge, which wants to be Newtonian and approximate. And it's the path in some sense that modern economics took.
So if you pick up an economics journal, you think, am I reading about the real world or some sort of computer game idealization where human beings don't resemble the sort of noisy, messy, imperfect things they really are. Or another book, which was the path of not taken. And this is really the preamble to my answer by Phillip Mirowski called Machine Dreams. And what Mirowski does is he said, why didn't Shannon Wiener, John Von Neumann who were developing, I think in some sense, the seeds of what became mature complexity science in the forties and fifties, why didn't that become the basis of economic theory, not pure mathematics.
And I think complexity economics is the power of economics didn't take. It said where's information, where's computation, whereas many body formalisms et cetera, et cetera. It's not that it isn't in part of economics. So that is the path that we've taken in some sense beholden to early cybernetics in part, but it's new and it's not a part of the mainstream literature and not well-represented in economics journals. And so people who try to publish in this domain are often marginalized.
And there's another dimension to this, which I think we should touch on, which is the ideological dimension of this. I mean, I know that you're a fan of Adam Curtis' films, All Watched Over by Machines of Love and Grace.
David Krakauer (33m 23s): But those series of films that come out of this counterfactual history, the Mirowski history, and whose if you like tutelary demi-gods at the Austrian school, right. Hayak and Schumpeter, who argued very much in favor of this alternative market as information gathering machine. And he traces it as you know, up through the influence of Stewart Brand on west coast investors in tech thinking. But I actually think where Curtis gets it wrong, is that the history that actually never took place in academia, it took place in the free market.
The academic world has up until now really largely ignored to be honest, these kinds of styles of thinking that could enrich the field. So it's an interesting, slightly perplexing circumstance.
Michael Garfield (34m 9s): You've been giving me so many points with which to enter this particular question, which is about when I spoke with Brian Arthur back actually in 13 and 14, but then also I think in 68 and 69, he's known in Silicon Valley for his work on the evolution of technology. But the more recent conversation that we had with him was his piece on the difference between algebraic and algorithmic thinking in economics and what we've mentioned a few times in this conversation, which could be articulated as the conflict between intrinsic and extrinsic causes in understanding the situation.
So like when you're talking about distributions, inventions and emergence, all of these things are lens through Brian's love for Schumpeter and the way that, that, and his love for Alfred North Whitehead and process philosophy brings him into these questions about what can be prefigured in our understanding of technology and socio-economic systems. And when you're talking about even the people that were thinking in this way before the ascendancy of complex computational technologies, one of the things that has really stepped forward is the importance of the algorithm and of a more process-based approach to this, but also the tension that as you put it again in your piece on stargazers and flesh eaters, we say in this piece, intrinsic theories include social upheavals, such as peasant revolt.
The ecological theory presents itself as a rather general process capable of explaining a large number of unrelated collapses, potentially in a large range of circumstances. The social theory involves a larger number of historical contingencies peculiar to, you give the example of Mayan society, in all likelihood, these factors interacted in multiple ways to accelerate the collapse, making a Sherlock Holmesian elucidation of one guilty party, rather difficult. And you know, Brian Arthur says in his piece on economics announced in verbs that again, this is just an inroad to talk about how SFI is doing these things differently, the use of simulation in an agent-based models and algorithmic thinking and so on. And this is one of a bouquet of core theoretical challenges that we're tackling with this research theme.
David Krakauer (36m 29s): I'm very much in the Edgeworth school. I'm a pluralist. The algorithmic worldview is just one of many and very powerful ones. And we have to be careful about overextending metaphors. Of course, I think about SFI in complexity economics sort of break it down into one, you know, out of equilibrium behavior. The core concepts in economics are by and large solution concepts based on an equilibrium, best known of course, is the Nash equilibrium.
And it's of a low dimensional game where the strategies are known and the equilibrium is established and much of early complexity economics are saying that's not true which means that if you're trying to address some of these statistics that we opened with in political economy, assuming stationarity that we're somehow at a fixed point is probably not correct. These could be transients. What kinds of frameworks would be appropriate if we're not at equilibrium? So there's that sort of thing. There's realistic psychology agent psychology, how we think.
And that's really changed in the last several decades. I mean, huge amounts of work by people like Kinnaman on diversity and others on bias and noise and imperfect decision-making economists are very hip to that. But how you put psychology and agency into our models is very, very far from obvious in colon camera, of course is one of our community. And Colin Cameron who's been thinking very carefully about that, collective dynamics. I mean the Dobro models has their households and their affirms households.
Try to minimize their costs firms try to maximize their profits. That's a two-party game. And you know, that's very far from how the world is configured. Is that an acceptable, what we would call core screening? Can you really project from millions of dimensions onto two? Is that okay or not? And I think without recourse to the empirical data, I think the answer is absolutely. You cannot know. So that's another. That’s one of the most important and most challenging. And this is something that Murray articulated earlier at SFI with integration across systems, you know, economics is not about economics alone.
It's about behavior, but it's also about ecology, et cetera. I mean, it's also about science and technology and invention and patents, all of that. How do you do that? I mean, is there a principle way of integrating all that without just creating spaghetti. It's unknown. And I think part of what defines as a fight is the desire or the aspiration to integrate in a principled way those systems without losing the plot. And that's been a big part of how we think about it. So those are just some of the things that SFI has been concerned with and all the methods that you mentioned, network theory, agent-based modeling, scaling theory of in scaling, et cetera, collective intelligence.
These are all what we consider the necessary tools to solve this class of systemic problems. And I'm not saying they will, but I'm pretty certain that to agent equilibrium assumptions are definitely not, you know, so let me give some examples then of core problems that I think the world should take on, honestly, and do the best that we can. And I'll mention some SFI people who will be a part of this project, and this is what they would be doing.
So one of them is we've alluded to the problem of dimension. There is a persistent allure of one dimension as you know, in my own work. Cause this is not really my field in my own work on intelligence, migrate battle with IQ that somehow you can capture all of the subtlety of someone's reasoning with a single number that is to me, patently absurd, but to others rather appealing, but it's no different in economics. Income, interest rates, exchange rates. We love these one dimensions, GDP. I mean, we just proliferate them and someone like John Geneakoploshas been saying enough is enough.
We need to look at high dimensional manifolds or what we would surfaces, let's say in particular, in his interest in credit cycles and leverage cycles. So interestingly Michael, there's a sociology of science here as part of the reason we like one dimensions is we can visualize them so easily. So you can turn on the TV and see a secular trend in one dimension. But what happens if the reality is seventh dimension? What do you do then? Well, we shouldn't allow the expediency of plots, our need to visualize, diminish the richness of our theories.
And I think so dimension interest me a lot, causality. The history of economic metrics is projecting internal low dimensions. So there is an independent variable and there's a dependent variable, and this is what we're going to explain. And we're going to do it with linear regressions or whatever. No, we're dealing with complex causality and there are two ways to do this. One is to say, well, we can't do that. So let's pretend it's one dimensional or wait a little bit like the machine learning crowd. No, we're going to embrace the dimentionality.
And we're going to forfeit comprehensibility. We've talked about this before with Geoffrey and there are people like Ricardo Hausmann, and Cesar Hidalgo and others who have created a much more subtle notion of product spaces to understand trade, which forfeits a little bit of the transparency, but to integrate into the theory complex causality, that's something that we like to do. Irrationality and belief, I mean, this is the whole history of agent based modeling, John Miller, Scott Page, Baxtell, and Josh Epstein, all of whom have been saying, look, the algebra.
Brian talks about cannot be a ball and chain. If belief matters, then we need a framework that can incorporate it. Even if that framework is not elegant and SFI has been very willing to sacrifice elegance at the altar of truth. So there's that bit. You talked about algorithms. Absolutely. I'll get into technology. Very interesting. You know, to what extent is the economy a sort of neo-Hayekian computer?
Well, we don't know it's most definitely not a computer along the lines of computers we build. Is it an information processing system? No doubt. But I think that, to be honest, Michael, we, at this point we don't really have adequate theories of computation in complex systems. So we're constantly borrowing the model from computer science, which probably has to stop. And then scaling, this beautiful work that Geoffrey West and others have been really pioneering that gives us a novel expectation for what we might observe, what is law like and what is a deviation from the law like.
And so it gets us right back to our opening remarks about what might we expect to be malleable. And I think scaling theory is one of those theories that gives us a window into that. So that's just an example of some of the work we've done that I think would inform the project.
Michael Garfield (43m 30s): So just to branch this out again, and then let you decide which branch decline before we cinch this up. A lot of what you said there, you've got this very rich history of publications with Jessica Flack and others in the collective computation group on the way that, and this sort of circles back to some of the first points that we were discussing, the way that violence on the individual is actually mitigating violence at the level of an entire group or society.
And it would be remiss, I think, to leave out James C. Scott and seeing like a state in this and the way that detention and the theory that you're talking about here of explanatory levels or causal levels is something that we also see in the tension between different levels of a complex system, as it knows itself. And as it encodes stable features about its environment. So when you're talking about the violence committed by this effort to try and reduce the dimensionality of our complex world in order to form these parsimonious models, I'm thinking about, I think the next episode in this series is the one that I just recorded with Eric Beinhocker and Diane Coyle.
And Eric has this theory that complexity economics should be serving eudaimonic approach to reality, which is, you know, an approach that is not concerned with these simple metrics. And so this is related also to when you're talking about ecological concerns and the impact of our economic theory on the degree to which we undermine our own material supports this question of ecosystem services and are the metrics that we're coming up with actually in service of the goals that we're hoping that they achieve for us. Jessica Flack and Melanie Mitchell wrote a great piece at Aeon about this and about how the whole system shifts, once you assign a target metric and suddenly the students are trying to game the test rather than actually learn the material.
So I'd love to, again, bring this back to when we're asking these questions about the destined versus potential levels of inequality, these kinds of questions, just to lever into the next episode in this series about how these theoretical failures are actually making us miserable or killing us and how a complex systems approach to sociopolitical thinking and economic thinking gets us closer to what Beinhocker is calling eudemonic strategy.
David Krakauer (46m 1s): Yeah. I mean, there were two points that, and you've made this point several times today, drawing on the Geoffrey Harpham thing. It's not clear to me, to what extent the academic theory is to blame versus a much more intuitive application of sophomoric economic logic is in the real world. In other words, I doubt many CEOs come in and write down mathematical models when they make their decision. So this is an interesting issue. What is responsible for the state of the world?
And I have no doubt that we are as complicit in not communicating our objection. So that's a big, interesting question, or is this sort of a free-wheeling set of folkloric beliefs that are hurling the planet forward.
Michael Garfield (46m 48s): And you address this again and again, in your work that there's a trade-off between model simplicity and the cost. You've shown this graph of complex system size, living in a sweet spot, kind of between how accurate can we get and how much time can we actually devote to this. And when you know, we've talked about this on the show a few times, so much of this science now lives as a crisis discipline where you address this in your piece in the COVID reflection series, we're at a point now where you have to act on fundamental uncertainties.
We don't necessarily have time to know these things with the confidence that we would like.
David Krakauer (47m 28s): So again, through back to the statistics, back to the evidence that we face, I mean, it's clear to everyone, regardless of political persuasion, that something should be done. My philosophy is a little bit like the Graeber Wengrow philosophy that we need to be more experimental in our thinking. I'm not sure I completely subscribe to their view of history, but this idea that we've somehow reached the omega point of political economy and let's call it whatever you want to call it. You could call it neo-liberalism if you're on one side or you could say state-based capitalism on another side, and there are various typologies of the states of the world.
I mean, my view is why is it that we expect scientific revolutions? I mean, there was a time when we didn't have general relativity. There was a time when we didn't have evolutionary theory. These came along and then we learned from them and incorporated them. There was a time when we didn't have iPhone, believe it or not. Well that, there's no reason why that shouldn't be different in society. I mean, there will be reconfigurations. There is an ongoing reconfiguration that's alarming everyone because it seems so divisive and so polarizing, but that idea that society is a place in which socially responsible experiments are being played out all the time in households and in markets and in cultures I think very rich one. And I think we now have some ideas and some methods which allow us to analyze what's going on in a way that's less doctrinaire, less based in idealizations of mathematics than we had in the past. That comes partly from advances in statistics, machine learning, and partly from advances in the kinds of science we do. So I think this more pluralistic, open-minded use of methods on the one side and the second, a kind of comfort with allowing that society could be configured differently.
I always liked this idea of satisficing. It's not about optimizing, we're not living in a kind of live in a universe of best of all possible worlds. We know that that since Voltaire took it apart. So if we're not, we're dealing with this constantly shifting frontier of the satisfactory, I think that's a philosophy that I would like more people to hold and it would make people feel comfortable with the possibility of living in a radically different world. Now, does complexity science you'd have? No, but, but the tools of complex systems can help us understand where we are sort of a navigational device.
The wild world is changing.
Michael Garfield (50m 1s): To call in a Stuart Firestein and the talk that he gave here recently, I'm hoping to get him on the show soon too, because he's dancing around what you're talking about here in his argument, that in the awkward uncertainty, we're forced to eat our humble pie now epistemically. We don't have the same promise of certitude that it once seemed in the modern project that we would. He finds that as the basis for a philosophical optimism. So it seems like what you're saying is kind of akin to that, that actually it's in not knowing that there is some kind of hope or promise to be found.
David Krakauer (50m 38s): Yeah. I mean, that again, just going back to this essential scientific method that says, what are the expectations under a different mechanism and can they be changed or are there invisible constraints that we can't modify? And I think what's unfortunate in this area and what's going to generate, I imagine some debate after this show is aired is, oh, what is their political persuasion? Are they of the right or the left?
What are the hidden ideologies that they don't even realize that they're skating over? You know, and these are all totally reasonable, but the most important point is a kind of humility that the methods that we have developed thus far have been insufficient, that political economy can evolve as a discipline. And that the current dominant configuration of political economy is far from perfect. And not only that, grossly unfair. And so what can we do from all sides to arrive at a more satisfactory, not more optimal state of affairs?
I think that's the idea and what fascinating new science actually will emerge from this when we actually in a reasonable way, integrate human psychology, collective intelligence, scaling theory, and so forth. I mean, I think we're going to make incredible discoveries along the way.
Michael Garfield (51m 60s): Wonderful. I mean, just in closing, do you any other statements, invitations?
David Krakauer (52m 5s): Yeah. I mean, the invitation would be to engage with this domain in a non-partisan way. We don't know the answers and we're not pretending to. We don't have a hidden agenda. Here's an opportunity to rethink a little bit the structure of the world and do so with a new kind of science. And so I think that's an exciting moment and hopefully it's an inclusive moment.
Michael Garfield (52m 28s): Awesome. Thanks. Thank you for listening. Complexity is produced by the Santa Fe Institute, a nonprofit hub for complex systems science located in the high desert of New Mexico. For more information, including transcripts research links and educational resources, or to support our science and communication efforts. Visit Santafe.edu/podcast.