CAN SCIENCE HELP SOLVE THE ECONOMIC CRISIS?
By Mike Brown, Stuart Kauffman, Zoe-Vonna Palmrose and Lee Smolin
The economic crisis has to be stabilized immediately. This has to be carried out pragmatically, without undue ideology, and without reliance on the failed ideas and assumptions which led to the crisis. Complexity science can help here. For example, it is wrong to speak of “restoring the markets to equilibrium”, because the markets have never been in equilibrium. We are already way ahead if we speak of “restoring the markets to a stable, self-organized critical state.”
In the near-term, Eric Weinstein has spoken about an “economic Manhattan project”. This means getting a group of good scientists together, some who know a lot about economics and finance, and others, who have proved themselves in other areas of science but bring fresh minds and perspectives to the challenge, to focus on developing a scientific conceptualization of economic theory and modeling that is reliable enough to be called a science.
The Reality Club: Nassim Nicholas Taleb, Douglas Rushkoff, Larry Sanger, Mike Brown, George Dyson, Emanuel Derman, Michael Shermer, Paul Romer, Tor Nørretranders, Eric Weinstein, Brian Knutson
MIKE BROWN is Past Chairman of The Nasdaq Stock Market Board of Directors, past governor of the National Association of Securities Dealers, and past CFO of Microsoft Corporation, currently a director of EMC Corporation, VMware, Administaff Inc., Pipeline Financial Group Inc., and Thomas Weisel Partners.
STUART KAUFFMAN is Professor of biology, physics and astronomy and head of the Institute for Biocomplexity and Informatics, University of Calgary , also emeritus professor of biochemistry at the University of Pennsylvania, a MacArthur Fellow and an external professor at the Santa Fe Institute. Author of The Origins of Order, At Home in the Universe, Investigations and Reinventing the Sacred.
ZOE-VONNA PALMROSE is PricewaterhouseCoopers Professor of Auditing and Accounting, University of Southern California. Formerly served as Deputy Chief Accountant for Professional Practice in the Office of the Chief Accountant at the Securities and Exchange Commission. Co-author, with Mike Brown, of Thog’s Guide to Quantum Economics.
LEE SMOLIN is Founding and senior faculty, Perimeter Institute for Theoretical Physics. Author of Life of the Cosmos, Three Roads to Quantum Gravity and The Trouble with Physics.
CAN SCIENCE HELP SOLVE THE ECONOMIC CRISIS?
1. The crisis and regulation [*]
The main cause of the financial crisis is instability in the financial sector including the firms, institutions and markets which comprise it. To understand this instability, we have to begin with the legitimate primary purposes of the financial markets. One is to provide capital, as equity and debt, to the goods and services economy to allow it to thrive and grow. A second is to provide a stable repository for our collective savings. And a third is to responsibly provide appropriate credit to individuals. These legitimate functions have been hijacked by speculative behavior that was unchecked by regulatory structures. The consequences of this threaten to disrupt the productive efforts of millions of ordinary people who go to work every day to make stuff and provide services to one another.
In the decades leading to this crisis, the shift in our economic thinking from the long-term view on Main Street to short-term speculation and gratification on Wall Street have not only brought us to the brink of economic collapse, but have also compromised a sufficient flow of capital to important long-term initiatives—economic sustainability, renewal of infrastructure, abatement of climate change, and development of alternative energy sources—all important to a vibrant and sustainable economy.
This has happened before in history—in Rome, in Spain, Holland and England. More recently, in America, there were smaller crises before to the present one, perhaps early warnings—Black Monday, Long Term Capital Management, the dotcom bomb, and others. Now that we live in a global economy, we cannot afford the next crisis, an order of magnitude larger, in which the world’s governments themselves will have to be bailed out. Rather, we can only hope that these governments are collectively up to the task this time.
There is honor and service to society in inventing and building companies and products that make life better for people, which should be justly rewarded. There is honor in architecting balanced financial regulation, to which we should dedicate careful attention. There is honor for the important financial sector when it functions as it should for the collective good, and this too should also be justly rewarded. Reasoned risk-taking by knowledgeable investors plays an important role in capital markets in providing support for initially risk innovations. But there is no honor in abusing our regulatory and financial systems for reckless speculation (i.e. gambling) that has no productive value for our collective future and that of the generations who will follow.
Nonetheless, blame will not get us out of this situation. We need to understand how and why the crisis happened and why warnings over the last years were not understood or heeded. We need to use this knowledge to stop this crisis and get the economy functioning again. In the longer term, we need to redesign and reregulate the financial system so that it performs its necessary functions without leading to periodic crises of global scale.
Two basic assumptions must guide any thinking as we undertake these tasks. First, economies, financial institutions and markets cannot function without a context of rules and laws, which regulate them. In a market, each participant tries to do the best they can for themselves. In a properly architected and regulated market this contributes to the public good. There is simply no place for an ideological discussion about regulation. Stable systems in nature such as individual organisms and ecosystems are regulated. So must ours be. The only relevant question is do the regulations work or not, where work means that stable markets allow an orderly flow of capital to and from the goods and services economy and the people who comprise it.
Second, mathematics, physics and computers already play a major and necessary role in our economic affairs. People with training in mathematical sciences play a big role on Wall Street designing and valuing complex investment instruments, and running sophisticated trading strategies. There is no going back to the era before banks and funds depended on quantitative analysis and big computer programs, and the scientifically trained people to run them. Along with economists with whom they work, other scientists and computer scientists now have a profound responsibility to see that their skills, the principles which they have found effective, and the tools they have wrought, are all used well and wisely.
2. The crisis is at least partly due to shortcomings a certain theory of economics
“Those of us who have looked to the self-interest of lending institutions to protect shareholder’s equity (myself especially) are in a state of shocked disbelief. … It was the failure to properly price such risky assets that precipitated the crisis. In recent decades, a vast risk management and pricing system has evolved, combining the best insights of mathematicians and finance experts supported by major advances in computer and communications technology. A Nobel Prize was awarded for the discovery of the pricing model that underpins much of the advance in derivatives markets. This modern risk management paradigm held sway for decades. The whole intellectual edifice, however, collapsed in the summer of last year because the data inputted into the risk management models generally covered only the past two decades, a period of euphoria.”
— Testimony of Dr. Alan Greenspan, US House of Representatives Committee on Government Oversight and Reform, October 23, 2008
When economists and other scientists study a complex system they begin by asking about what assumptions have been used previously in understanding it, and how well they have done compared to data. So if we approach the crisis in this way, we have to begin by asking about the principles and assumptions that have been used to construct and justify the complex financial instruments whose use contributed to the present instability. We want to know how these theoretical ideas have been tested, and whether or not the present crisis is evidence that the ideas that the financial system have been built on may need to be improved.
In fact, there is an economic theory that shapes much of our thinking, as well as the practices of investment banks and the decisions of economic policy makers. This is called neoclassical economics. It is based on the following assumptions:
i. Most of the time markets are in or close to stable equilibrium.
ii. Participants in markets act rationally to maximize fixed and known preferences described by definite and time independent utility functions.
iii. Participants in markets have perfect knowledge of the information driving the markets as well as all other participants.
iv. Prices are set by a deterministic process of joint maximization of the preferences of all involved in a trade.
v. Fluctuations in prices are small, random and uncorrelated.
vi. There is perfect liquidity so all prices are well defined, and all markets clear.
vii. There is no important difference between markets comprising a few individuals and those comprising millions, so simple models suffice to elucidate the principles that govern markets.
The neoclassical paradigm based on these ideas has had some undisputed successes. At the same time, it appears to have led to the adoption of practices and recommendations, which are at least partly at the root of the present crisis. These included the ideas that,
i. Regulation is limited or unnecessary because markets find and stay close to stable equilibrium where they operate most efficiently, leading to maximally stable economic growth, whereas regulation only leads to slower growth. But we face a potentially precipitous decline in economic growth and prosperity in the wake of some deregulation.
ii. Everything has a value or price, at all times, that can be uniquely determined by some definite objective process. This includes contracts that refer to prices of fluctuating variables at future times. There is experience with futures contracts, which have prices which are set daily by their being actively traded. But we are now seeing these values evaporate.
iii. This trading experience may be generalized to a claim that complex financial instruments which oblige actions to be taken at future times based on conditions not known till then, still have definite values and prices even if they are never or rarely traded. But part of the crisis is due to the fact that the balance sheets of banks and companies holding these contracts cannot be computed because they include instruments whose prices have been revealed as simply hypothetical and are now proving to be indeterminate .
iv. Stability can be increased by inventing and trading abstract complex financial instruments rather than principal contracts like stocks and mortgages. Examples are derivatives. Although these predate the birth of Christ and have been a factor in every economy of scale since, our markets have recently been flooded with a host of new ones which cleverly combine functions of different prices at different times into financial instruments whose values are purported to fluctuate less than the values of stocks making them up. The theory behind the possibility of combining fluctuating variables into variables that fluctuate less is critically dependent on the above assumptions, especially that the fluctuations are small, random and uncorrelated. But these assumptions have been shown to be false.
v. It has been argued that these innovative instruments should not be regulated even as much as stock trading because they function as insurance to increase stability. This was based on another false assumption that any mathematical function of the values of stocks at different times has a fixed and determinate value at any time.
vi. Because price determination is a definite process of maximization of known preferences in an environment of perfect knowledge, and because all values are definite, it can be in some instances automated and carried out by computers programmed to trade under specified conditions. But some markets thus operated have failed to function or clear trades.
Before we look more deeply into possible difficulties with the neoclassical paradigm, we have to also emphasize that it has been so influential because it does give important insight into how markets work in some circumstances. Nor has it ignored the possibility that markets can have instabilities. For example, there is a long list of well known market failures (principal agent problems, moral hazard, public goods, menu costs, lemon markets, adverse selection, rent-seeking behavior, incomplete knowledge, incomplete markets, multiple equilibria etc. …) So we do not want to ignore the successes of this paradigm or overlook the role that these well known understandings may play in understanding the current crisis. But we also want to ask if there are alternative ideas, principles and methods of modeling economic systems which might also provide the basis for wise advice and policy.
As a result, in part, at least of belief in the neoclassical paradigm, a very technical approach to trading has come to dominate markets based on complex financial instruments and strategies that require mathematical scientists and computers to carry out. Beginning as small speculative efforts, these now dominate markets. In most markets including equities and credit, the value of derivative contracts now exceeds by an order of magnitude the total value of underlying contracts, which must be traded to fulfill those derived from them.
When physicists made the atomic bomb they realized what they had conceived and immediately felt a sober responsibility to help make the world safe from their invention. At this time there is a responsibility for those with the knowledge and skills to understand the financial instruments involved in this crisis to help first to resolve this crisis and to next turn their attention to the design and regulation of a stable market system. This will involve economists, mathematicians, physicists, biologists, computer scientists and others working together to make a more stable economic system.
In our view, the current crisis does suggest there are weakness in the paradigm of neoclassical economics. In particular:
i. The big markets in the economy appear not to be in equilibrium. Not now, and perhaps also not normally. The fluctuations in the values of stocks, currencies, and commodities are often not random and uncorrelated and, as we have seen recently, they need not be small. Some other paradigm is needed to describe the workings of real markets.
ii. More generally, the theory of competitive general equilibrium is based on assumptions that appear to be too idealized. These include the assumption that at equilibrium prices are set so that all markets will clear no matter how the future unfolds and the assumption that each agent has a view on the value of all possible dated contingent goods .
iii. Participants in markets do not have fixed preferences, but the theory of competitive general equilibrium assumes that they do. Preferences change in time unpredictably due to changing tastes and circumstances as well as in response to innovations which introduce new products and eliminate the needs for old products, and we should acknowledge this. The unforeseeable aspects of innovations renders risk assessments problematic.
iv. There has been an unjustified extrapolation from simple models of markets with two participants and two goods (or something similar) to real markets with millions of participants and thousands of goods, a mistake we should not repeat.
v. Participants in markets do not have perfect knowledge, indeed their knowledge and beliefs about the market conditions are sometimes false or unreliable and different participants have different knowledge and beliefs. We should acknowledge this freedom because it is not the case, as sometimes assumed, that the lack of perfect knowledge by traders averages out as noise.
vi. This has the effect that swings in belief can crash markets and hurt people even when much of the machinery of the goods and services economy is healthy and prepared to function well with an orderly availability of capital.
vii. Increasing returns can lead to path dependence in the economy so that the evolution of an economy will depend on historical contingencies. This makes prediction and risk assessment difficult.
viii. There appears to be a basic lack of appreciation of the importance of relative scales. This is because of the misapplication of the neoclassical paradigm that the markets operate near equilibrium. Financial instruments such as derivatives indeed can do little harm except to those who use them, so long as they represent only a fraction of a market. However, an extremely dangerous situation emerges when their use grows to the point where so much equity is pledged in the resulting contracts that a movement in the markets in a non-random direction can introduce an instability in which the contracts are called but cannot possibly be fulfilled. Any meaningful discussion about whether a novel financial instrument requires regulation must involve the scale of its use.
3) Does science, including economics, offer an alternative basis for conceptualizing a theory of economic markets?
The answer is yes. For one thing, a critique of the neoclassical paradigm has been developing from within economics as well as from the study of complex systems for the last twenty years. To this can be added other insights about how to describe markets which depart from neoclassical assumptions. These can be combined to yield a new scientific conceptualization of economic systems. This needs to be developed before it can yield precise detailed models of the economy of sufficient complexity to be reliable. But it offers a lot of promise.
Key components of this view and the methodologies that underlie it include the following.
1) The economy is a physical system, involving flows of goods, information and energy, hence it might be useful to model an economy as a system in physics. However, while there is a concept of equilibrium in neoclassical economic theory, the concept of equilibrium in physics is not applicable to economies because it applies only to particular kinds of systems called closed systems.
These are closed off from the outside world and have fixed unchanging amounts of the goods that compose them. They have fixed amounts of energy, which cannot be added to or subtracted from the outside. Markets are not describable as closed systems, so the notion of their being in physical equilibrium cannot be applied.
2) Instead, markets are examples of systems physicists call open systems. They do not have fixed amounts of goods or currencies. They are situated inside larger open systems including the biosphere and energy and materials flows through them from the larger system that contains it.
3) Flows of energy and goods through an open system can drive its selforganization to meta-stable states. These states are not like equilibrium in that fluctuations around them are neither small, nor random, nor uncorrelated.
4) Instead of being in equilibrium, markets can be understood as self-organized critical systems. This is a theoretical construct that can be usefully applied to real markets. It leads one to expect that in steady states markets are approximately scale invariant. This implies a prediction for how certain quantities will be distributed in an economy, which includes wealth, income, sizes of firms, populations of cities, and total values of currencies. The prediction is that these distributions are power laws and the prediction is observed in the real economy.
5) Complexity matters. In neoclassical economics many conclusions are drawn from studying situations with two traders and two goods (or similar simplifications). The conclusions from modeling these and other simple systems are then applied to real economies with millions of participants and thousands of goods. The new paradigm finds that the qualitative features of real economies cannot be correctly captured by such simple models, because basic features of how they work depend on their size and complexity.
6) Heterogeneity matters. In the real world participants have very incomplete knowledge of markets and different players know different things. Different players also have different strategies which persistently co-evolve and change as the market, partially engendered by those changing strategies, itself changes. This renders risk assessment in financial instruments difficult. This diversity cannot be modeled by neoclassical economics, but it can be modeled by new techniques such as complex adaptive systems.
7) Economic growth is driven by the development of cycles of materials, goods, energy and money, which are analogous to cycles which comprise ecological systems. These cycles are understood as basic components of self-organized systems that are far from equilibrium. Neoclassical economics studies flows that do not generally close into cycles and hence miss the key issues regarding stability and instability of an economy.
8) There is a lot of experience modeling ecological systems, which are similar to economies, in that they are open systems which self-organize due to flows of energy and material. This gives us a methodological basis for theorizing an economy in a way in which the role of energy is intrinsic and issues such as what we do with the inevitable waste products of industrial processes necessarily arise.
9) Time scales matter. Different functional cash to cash cycles have very different time scales. Instabilities can be easily introduced by too strongly coupling processes on different time scales.
10) Complex systems can function in different phases, analogous to the different phases of matter: solid, liquid, and gas. Some of these are more hospitable to us than others. Transitions between phases can be abrupt and disruptive. Regulators of economic systems would do well to keep track of measures that indicate proximity to phase transitions and act to avoid them.
11) There is a natural language for describing and quantifying departures from the conditions of equilibrium described by neoclassical theory. These are important because these are conditions in which the assumptions that go into the design of many complex financial instruments such as derivatives cease to be reliable. This physics, but their general importance to economics was discovered only recently by Pia Malaney and Eric Weinstein . This gives us a concept called curvature that measures how far a market is from equilibrium, how much markets fail to clear, how large arbitrage opportunities are, and the effects of changing preferences.
12) Economic markets can be described as networks of traders and transactions evolving in time. There is a well developed theory of such networks, which provides a useful methodology and language for economic modeling. For example, the notion of a small worlds network has been found to apply to economic systems as well as other networks in present use such as the internet. Computer scientists have a lot of experience developing and using models of complex systems based on such networks.
13) The number of distinct goods and services, the kinds of companies, and the numbers of ways to make a living, have all grown dramatically over time. This growth is driven by innovation and it in turn is a major cause of real economic growth. Models such as neoclassical economics work with fixed baskets of goods and services and thereby miss the key driver of growth.
14) There is a limit to the accuracy of future predictions, because the innovations that drive the increasing diversity of the economy and economic growth generally cannot be reliably predicted. But one can make an economy more or less hospitable to them.
These critiques of neoclassical economics point to the possibility of a new paradigm in economic theory and modeling. Given that some of these points have been argued and studied for decades, by economists and others, it is pertinent to ask whether they have led to economic models, which have proved accurate in predicting the behavior of real markets. There have been some successes, such as a model of the NASDAQ stock market . At the same time it cannot be said that the complexity point of view has led to a well developed economic theory that presently stands as a full alternative to the neoclassical paradigm. For example, there has not been, to our knowledge, a large scale economic model built with these ideas which is presently up to the challenge of modeling the current complexities of the worlds financial markets. So there remains much to be done to develop and test models based on these ideas and see how well they do applied to the real economy.
Nor do these new ideas necessarily invalidate the successes of neoclassical economics. The conclusion we draw from this is that much more work has to be done. The good news is that both the successful aspects of the neoclassical paradigm and the newer ideas based on complex systems offer much scope for development of economics as a science. What is needed is an open-minded development of economic theory, as in any area of science, based on the development of detailed models, through which the applicability of different principles and hypothesis can be compared with real world data.
The real success of the American economy has been in its functioning well as an incubator for innovation in real goods and services. This has given us expertise and technology, which has now led to the invention of financial instruments whose use, in a failed theoretical context, largely unregulated or comprehended, threatens to undo all the economic progress of the last decades. The question in front of us is whether the same spirit of innovation can be applied to the principles of economic theory that governs financial markets so as to base the design and regulation of those markets on correct and verifiable principles and models.
4) What is to be done?
“… since the early 1980s …the way we train people to think … in main-stream economics…has left them almost unable anymore to distinguish the surface from the underlying reality. Not only was it the age of Reagan and the beginning of market fundamentalism that came in the early 80s, and the rational expectations revolution and all the rest, but a fundamental break in how we actually train our students to think. …Because the new kind of economic modeling, that won all the Nobel prizes said: you don’t have to understand the deep picture…You don’t really have to know underlying mechanisms in the economy because the prices reflect the underlying mechanism.”
—Jeffrey Sacks, remarks at Earth Institute (Columbia University), October 20, 2008.
Classical economic theory was a product of the enlightenment, invented by philosophers who wanted to contribute to the growth of liberal democracy. They taught us how to construct societies conducive to human dignity based on a balance between cooperation and the freedom to pursue life, liberty and happiness. The highpoint of the enlightenment was the mutual influence of Newton, Locke and Montesquieu, who in turn influenced the founding fathers to adopt principles of government they believed came from observations of nature. It is in the spirit of their shared values and idealism that we today call for a renewal of the enlightenment approach to rationally understanding and governing human societies.
In the last century, science has developed new tools with which to understand complex and evolving systems such as the economy. We ask economists, other scientists and policy makers to work together to develop a new approach to conceptualizing and modeling the economy that is reliable enough to serve as a guide for building and regulating stable markets.
How are we to bring about such a transformation in economic theory? Let us separate the discussion into the immediate, near and long-terms.
The economic crisis has to be stabilized immediately. This has to be carried out pragmatically, without undue ideology, and without reliance on the failed ideas and assumptions which led to the crisis. Complexity science can help here. For example, it is wrong to speak of “restoring the markets to equilibrium”, because the markets have never been in equilibrium. We are already way ahead if we speak of “restoring the markets to a stable, self-organized critical state.”
In the near-term, Eric Weinstein has spoken about an “economic Manhattan project”. This means getting a group of good scientists together, some who know a lot about economics and finance, and others, who have proved themselves in other areas of science but bring fresh minds and perspectives to the challenge, to focus on developing a scientific conceptualization of economic theory and modeling that is increasingly reliable.
To accomplish this, research has to be done to develop a new paradigm for economic theory and modeling markets. This research has to be carried out as the science it is, which is to say that assumptions must be tested against the real world, alternative theories must be developed and refined, and these must be compared with one another and tested again.
In all of this work economists, accountants and financial mathematicians should join forces with complexity theorists and other scientists with the goal of remaking economic theory and modeling so that it can offer reliable guidance for the organization and regulation of stable financial markets. The research has to be carried out in an interdisciplinary and open spirit.
The goal of this research is a new scientific conceptualization of economics and economic modeling which can provide reliable advice in constructing, running and regulating financial institutions and markets so that they serve the purpose of growth and stability in the standard of living of all people. Financial firms and markets must innovate to serve their larger purpose in providing efficient capital flows for the growth and innovation of real goods and services as well as a safe repository for our collective savings. Innovation in methods of speculation that are unrelated to stable production of goods and services and the efficient flow of capital within the system must be recognized and discouraged.
If this research succeeds then a discussion of whether a given financial instrument should be allowed or how it should be regulated should not be a matter of opinion or ideology. It should be based on detailed modeling and data taken from real world experiment and treated with the scrutiny brought to the introduction of a new airplane.
In the longterm, there needs to be an independent, non-partisan methodology for economic and financial modeling which involves globally agreed upon standards, as in the world of climate modeling. As in that world, one can imagine an international commission of economic scientists who develop, test and benchmark economic models against each other, and against past data, so that there is a reliable understanding of what the best models are and how reliable they are for studying different kinds of problems and predicting the impacts of proposed new economic and financial regulation. This will allow new proposals for innovative financial instruments or changes in trading rules or accounting rules to be tested in an open environment using best practices to understand their results.
An economy involves finding balance between long- and short-term objectives, acceptable distributions of wealth, and rewards for innovation and risk taking. Different governments may embrace different social philosophies and may seek to establish economic and financial regulations to obtain somewhat different desired results. The role of an independent, non-partisan scientific conceptualization of economics should be to provide these policy makers with a notion as to the likelihood that new economic and financial regulations they are considering will have the results they desire and that these will not involve unintended consequences to others.
One can also imagine that in the long term the software used to model markets and economies should be expected to be open, so that it can be critiqued and improved on by experts, whether they work in business, academia or government, and irrespective of the country in which they work.
Perhaps at this uncertain point it is good to end with a vision of what a broader scientific approach to modeling economic markets might lead to. As a result of increases in airplane safety and rigorous application of risk management procedures there have been fewer and fewer crashes per mile flown over the last decades. This has not damped innovation—new airplanes are regularly introduced incorporating improvements in engines, constructions and instrumentation. What we can aim for is a financial system similarly designed and regulated according to sound and tested scientific principles so that crashes and crises once a decade or so become a thing of the past, and in which innovation of goods and services thrives in a stable economic environment.
[*] This essay is based on ongoing collaborations and discussions with Jim Herriot, Jaron Lanier, Bruce Sawhill and Eric Weinstein.
 This is because the markets for such instruments are inactive as well as because of uncertainties around otherwise estimating the amount and timing of expected future cash flows from the rights embodied in such instruments.
 Such as a bushel of wheat to be delivered in May if it rains in Nebraska for a week in April.
 The role of gauge theories in financial mathematics was developed also by K. Ilinksi, Physics of Finance: Gauge Modeling in Non-Equilibrium Pricing).
[4 ] V. Darley and A.V. Outkin. “A NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems.” Singapore: World Scientific Publishing Co. Pte. Ltd., 2007.
Director, EMC Corporation; Past Chairman of The Nasdaq Stock Market Board of Directors
Past Chairman, Nasdaq Stock Market Board of Directors; Past Governor, National Association of Securities Dealers; Past CFO, Microsoft Corporation
A Response to Nassim Taleb
I am an admirer of yours and not a scientist, just a simple old CFO with very little formal education. I have listened to countless “scientized” proposals for who knows what all market gymnastics over the years and am largely in agreement with what you say about scientism and its contribution to our crisis.
However, over the last forty years, I have worked primarily in the information technology business, and in this experience have come to revere our scientists for practical purposes like building and delivering info-tech products that do real stuff and are used by practical people, including traders. Working with these guys on a specific issue of concern can seem a little like rounding up cats at times, but I have always admired their willingness to say, “oops” when they are wrong and frankly, cannot imagine how we could have this particular industry without them.
Over ten years on the NASDAQ Board, I grew increasingly disappointed in our approach to studying the consequences of proposed market regulations before launching them, and towards the end of this time turned to Stu Kauffman, Vince Darley and Sasha Outkin to model the consequences of regulatory proposals during the legendary debate over teenies and decimalized tick sizes back just before the dot.com bomb. This work is well documented (A NASDAQ MARKET SIMULATION, Insights on a Major Market from the Science of Complex Adaptive Systems by Darley and Outkin), and was one of several sober warnings that were ignored by our regulators as we launched into the bomb. They too suggested we should take this kind of science where it might work and leave them in the real world without more problems. With the benefit of hindsight, they were wrong and Darley and Outkin were right.
I think the main thing science has to offer in this crisis right now is a little dose of its traditional empirical humility, and when things have gotten pretty screwed up, that is usually a good place to start, if only just for good form. It would also, of course, be wise to remain skeptically mindful of where science may have contributed to the mess.
Co-founder of Wikipedia and Citizendium
I am not an economist, but I was trained in philosophy and I have spent a lot of time managing arguments between ideological foes in open community projects online. So I think I know a few things about ideology, and I just wanted to say a few things about the nature of ideology and science, and the puzzling suggestion of an ideology-free science of economics.
The article begins by saying, “The economic crisis has to be stabilized immediately. This has to be carried out pragmatically, without undue ideology, and without reliance on the failed ideas and assumptions which led to the crisis.”
That the current economic crisis must be stabilized immediately and that free market “ideas and assumptions” led to the crisis—these suggestions are themselves all deeply ideological claims and, I would have thought, obviously so. But maybe not. So let me explain.
One of the better-known conceits of conservative ideology, associated with Edmund Burke, is that conservatism is supremely pragmatic and not based on grand theories and generalizations. Conservatism is instead supposed to promote traditions that are sometimes inconsistent but that also work. More recently, people whose views are far to the left have taken to describing themselves as “centrists” and “pragmatists.” So both left and right want to appear to be pragmatic. On both sides, however, I suspect there are many posturing ideologues whose protestations of pragmatism are little more than ploys to disarm their opponents. Who, after all, wants to be branded an “ideologue” by disagreeing on principled grounds with someone who claims to be pragmatic and ideology-free and hence more reasonable? Just as such protestations of lacking ideology are often deceptive, adversions to the allegedly objective, ideology-free conclusions of science sound just as deceptive to me.
The ambition of scientism—to make unrigorous disciplines rigorous by modelling them after science—is perennial. If we are deeply impressed with the hard sciences and mathematics and disappointed with an inexact social science like economics, we might seek to reduce the inexact disciplines to careful, value-free observation and the rigor of scientific argument.
I associate this ambition with Rudolf Carnap and Vienna Circle (the logical positivists) and its project of the Unity of Science from 80 years ago. The possibility of the Unity of Science was and remains a core question within the philosophy of science, and many philosophers remain very skeptical of it. One of the problems with the project is that the phenomena studied outside the exact and hard sciences—including economic phenomena like market crises—are not obviously amenable to the same sort of analysis. They are, of course, more complex, as Nassim Taleb and Mike Brown pointed out. A failure to understand that deep complexity is precisely a failure to understand the phenomena.
Any scientific project to take on economics and boldly transform it into a hard science will run into that problem of a complexity that is not amenable to rigorous scientific model-building. The other trouble with an “economic Manhattan project” suggestion is the fact that work in the social sciences is inherently ideological. I suppose that the title of the article’s section 4, “What is to be done?” was chosen ironically—being the title of Lenin’s most famous tract and all.
In the social sciences, methods and fundamental assumptions reflect deep ideological commitments, and any group that aspired to make a science of economics and produce a scientific solution of our economic woes would instantly run into this problem. If the group’s members were ideologically diverse, they would find they could not agree on how to proceed; if they were chosen to be ideologically consistent, they would ipso facto be an ideological group, incapable of acting merely “pragmatically” and “without undue ideology.” Many other economists, not in the group and not sharing their ideological assumptions, would inevitably disagree with their recommendations.
An economic Manhattan project would be much more plausible to me, at least, if its members were to admit their ideological commitments up front. But they would have to come to grips with their commitments in the first place, and in doing so, they would no doubt have to become actual economists.
Science Historian ; Author, Turing’s Cathedral; Darwin Among the Machines
Brown, Kauffman, Palmrose, and Smolin have hit the nail on the head. But is it the right nail? When the patient needs first aid, do you ask “is there a modeler in the house?”
Financial systems exhibit the Gödelian incompleteness characteristic of all (sufficiently powerful) formal systems: within the given system it is possible to construct statements (or financial instruments) whose value appears to be sound, but cannot be proved within the system itself. The same limitations apply to models of financial systems.
There is good news and bad news in this. No financial system (or model of such a system) can ever be completely secure and closed. On the other hand, there is no limit to the level of concepts that an economy (or a model of that economy) is able to comprehend.
So, what should we do? Assigning an international team of experts to formulate a global economic model is a worthy undertaking, but can the rest of us afford to hold our breath and wait? We also need Plan B, just in case. Plan B is to nurture new, grassroots economic systems that directly (and honestly) couple the flow of currencies to the flow of goods, services, and information—down to the last bit, and the last dollar, from the bottom up.
“Ten years ago I started a company based on the assumption that people are basically good,” argued E-Bay founder Pierre Omidyar (at the Santa Fe Institute) in 2004. “And now I have the data to prove it.” Instead of putting a dozen scientists in a room to come up with a better model of the existing global financial system, we should put a dozen Pierre Omidyars, Elon Musks, Salar Kamangars, and Jeff Bezoses in a room (with Danny Hillis) and let them actually build one (a new financial system, not another model).
Financial systems are the oldest and most centrally-regulated information-processing systems we have, and that’s one reason they have been so resistant to change. At the beginning of the 21st century (five years after “Toy Story”) 20th-Century Fox was still running their internal payroll system on punched cards. Why change now?
The present crisis is an opportunity to reboot the malfunctioning system from the bottom up. Computational tools such as AdWords allow tracking the value of everything, down to the word “Dog,” in real money in real time. Digital rights management tracks the migration of a 99-cent MP3 file from one iPod to the next; PageRank gauges the credibility of even the most obscure internet site; cell phones have more processing (and encryption) power than automatic teller machines owned by banks. We deserve an open, transparent, efficient, and secure financial system where you know exactly where your money really is, and what it is really worth, in real time. If data were disappearing the way money is disappearing, we would find a different service provider, fast.
It is time to abandon some of our legacy systems, not bail them out. And if we have to start over with less money, is this really so bad? “Is not a Country the Poorer for having less Money?” asked Sir William Petty in 1682. “Not always,” he answered, “For as the most thriving Men keep little or no Money by them, but turn and wind it into various Commodities to their great Profit, so may the whole Nation also.”
Instead of attempting to prop up failed institutions with money that does not exist, we should be launching new institutions with money that does.
Professor, Financial Engineering, Columbia University; Author, Models.Behaving.Badly
This is a noble proposal, but I remain a bit of a skeptic with respect to the ability of a cohort of scientists and economists to find a scientific solution to the problems of our economy. Economies are living organisms, about as old as the oldest profession, and rebuilding the economic system from scratch is a problem in engineering and social engineering, not in science. Human’s and scientists don’t have a good history as regards social engineering.
Science is reductive, and seeks to establish the laws which govern physical systems. To do so, scientists carry out repeatable experiments. For an experiment to be approximately repeatable, history has to be unimportant, and so the system has to couple very weakly to the rest of the universe. You can figure out the statistics of a coin flip because initial conditions of the coin are more or less irrelevant. The hand of the coin flipper, the temperature of the room, the location on earth, don’t matter too much because the coin is coupled weakly to the rest of the world, and hence to history.
Engineering is constructive, and tries to build little almost-isolated universes (usually called machines) which obey the laws of physics and chemistry and more or less do what we want them to do, in certain regimes. You can do engineering without knowing too much science but by having a good empirical understanding of how matter behaves (building a hammer or chisel or lever or bow and arrow), or you can engineer it using scientific laws (using quantum electrodynamics and solid state physics to build transistor radios).
Now let’s think about economics and the behavior of markets and the people who comprise them. First of all, we don’t have the scientific basis for engineering economics. Second, it will be very difficult to find the scientific laws governing the behavior of economies, because there are very few isolated economic machines. There is one large economy, more or less. Since economies aren’t isolated, you can’t carry out the repeated experiments that science requires. History is important in economics, and in human behavior in general. You cannot replicate the initial conditions over and over again, as you can with a coin flip. Credit markets tomorrow won’t behave like credit markets last year because we have learned what happened last year, and you cannot get back to the initial conditions of a year ago. Human beings and societies learn; physical systems by and large don’t.
Though I came to finance as a scientist, my experience working in the financial arena has taught me to be very humble of scientific claims and of big universal ambitions. Whenever we make a model of something involving human beings, we are trying to force the ugly stepsister’s foot into Cinderella’s pretty glass slipper. It doesn’t fit without cutting off some of the essential parts. You have to understand that you do need models—you can’t think about finance and economics without math and modelsbut you have to understand too that models are not the world, at least not in the social sciences, and so the models have to be simple, shallow even.
I would add that the economy we have been part of has not been treated as an efficient market, even by the economists who claim that it is. Every time the economy has suffered a threat, the Fed has eased credit to reignite it, but whenever it has boomed they have never tried to quench it very hard. It is an oddity that the only solution people can think of now to the credit crisis, which was in large part caused by too much easy credit rather than by scientism or mathematics, is to ease credit again and have the government spend money on infrastructure and public works. This is probably the right thing to do, but it shows you how counterintuitive and complex the system is, and how important history in fact is: first time around easy credit is the problem, second time around it’s the hoped-for solution.
So, let’s try to figure out how to do things better, with words and thoughts and with math too. More uniform regulation, more transparency, less leverage are going to be critical. I’d guess that in the short run the robust solution will lie in a proposal written with words rather than with equations, and be more like a bow and arrow than like a transistor.
Publisher, Skeptic magazine; Monthly Columnist, Scientific American; Presidential Fellow, Chapman University; Author, Heavens on Earth
WHY AN ECONOMIC MANHATAN PROJECT CANNOT WORK
Life is intricate, complex, and looks intelligently designed, so our folk biology leads us to infer that there must be an intelligent designer. Analogously, economies are intricate, complex, and look intelligently designed, so our folk economics is to infer that we need an economic designer. This is why instinctually we look to top-down artificial solutions to problems that naturally arose from this bottom-up complex adaptive system.
The temptation to establish an economic Manhattan Project is compelling, but it won’t work because we now know that economies, like ecologies, are not intelligently designed from the top down; they spontaneously arise out of simpler systems from the bottom up. Life and economies, like language, writing, the law, civilizations, and cultures, arise spontaneously as self-organized emergent properties from within systems themselves and without the aid of a blueprint design by a clever engineer. Neither God nor Government are needed to explain such phenomena. In their stead, natural selection and the invisible hand explain precisely how individual organisms and people, pursuing their own self-interest in their struggle to survive and make a living, generate the emergent property of complex ecologies and economies. Both are Complex Adaptive Systems in which individual particles, parts, or agents interact, process information, learn, and adapt their behavior to changing conditions.
Just as the environment is the designspace of evolution, the market is the designspace of economics. Just as nature selects the variations best suited to survive in a particular environment, so too do people select the goods and services that are best suited to meet their unique needs and desires in a particular market. In nature, random genetic mutations and the mixing of parental genes in offspring produce variation, and the selection of this genetic variation through the survival of their hosts is what drives evolution.
The evolution of our material economy proceeds in an analogous manner through the production and selection of numerous permutations of countless products. The ten billion different products traded in our modern economy represent only those variations that made it to market, so there is already a selection process by the manufacturers themselves as they attempt to correctly predict what the market will prefer. Once these choices are brought to market, there is a cumulative selection for those deemed most useful, with the selection conducted by consumers in the marketplace who vote with their dollars which products will survive—VHS over Betamax, DVDs over VHS, CDs over records, flip phones over brick phones, computers over typewriters, Google over Altavista, SUVs over station wagons, paper books over e-books (still), and Internet news over network news (soon). Those that are purchased “survive” and “reproduce” into the future through repetitive use and remanufacturing.
The Austrian economist Ludwig von Mises spelled out the reasons why top-down controls of economies do not work, noting especially the problem of “economic calculation” in a planned economy, such as the one an economic Manhattan Project would oversee. In capitalism, prices are in constant and rapid flux and are determined from below by individuals freely exchanging in the marketplace; in socialism, prices are slow to change and are determined from above by government fiat. Money is a means of exchange and prices are the information people use to guide their choices. Mises demonstrated that socialist economies depend on capitalist economies to determine what prices should be assigned to goods and services. And they do so cumbersomely and inefficiently. Ultimately, free markets are the only way to find out what buyers are willing to pay and what sellers are willing to accept.
For example, studies show that Internet airfares change thousands of times an hour as people search for the best price they can find to reach their destinations. Airlines have sophisticated software programs that adjust the prices according to supply and demand for particular routes, the number of seats available at any given moment, and other variables that go into what has become known as “dynamic pricing.” Imagine a centralized bureaucratic airline price committee—a division, perhaps, of the economic Manhattan Project—meeting each morning to work out how much it is going to cost someone to fly, say, from Greensboro, North Carolina to Wichita, Kansas, on each of a dozen different airline carriers, factoring in not only the real-time supply and demand parameters and number of available seats, but also the time of day, type of aircraft, class of travel, cost of aviation fuel, number of frequent flyer mileage seats already taken, discount coupons, and dozens of other variables, and doing so for hundreds of thousands of people. It is not an impossible feat to attempt, and planned economies have tried to do it, but like the proverbial bipedal dog, it is exceedingly rare, awkwardly clumsy, and painfully humorous to watch. Unfortunately for those forced to live in planned economies in the twentieth century, the economic disasters that were the inevitable result were neither rare nor humorous.
Or imagine the futility of government bureaucrats trying to find the right price for each of the approximately 170,000 different books published each year, factoring in hardback versus paperback prices, special discounts for multiple purchases of bundled books, plus shipping specials for minimum sales and factoring in, of course, the discriminatory pricing now used in the same way the airlines price their tickets, and then imagine multiplying that process by the hundreds of thousands of different markets, industries, and businesses and it becomes crashingly clear why no top-down system could ever match the real-time sensitivity to prices provided by the bottom-up complex adaptive pricing system currently in place.
Expand the problem by many orders of magnitude and we get a sense of the breathtaking inanity of trying to control an entire economy, no matter how smart the experts in our hypothetical economic Manhattan Project may be. The economy is a product of human action, not of human design. Trying to redesign something that was never designed in the first place is futile. I vote no on an economic Manhattan Project.
Senior Fellow, Stanford Institute for Economic Policy Research
Imagine that fires were devastating the world’s forests and you came across this manifesto:
The forest crisis has to be stabilized immediately. This has to be carried out pragmatically, without undue ideology, and without reliance on the failed ideas and assumptions that led to the crisis. Complexity science can help here. For example, it is wrong to speak of “restoring the forests to equilibrium,” because forests have never been in equilibrium. We are way ahead if we speak of “restoring forests to a stable, self-organizing critical state.”
Would this convince you that only complexity science can prevent forest fires?
With one tweak, this is the first paragraph from the pull-quote for this piece. All I’ve done is change “market” to “forest.” The forest version sounds pretty implausible to me, but after a financial crisis, people seem to be drawn to the version with “markets.” For example, after Citibank made some bets that turned out badly in the Latin American debt crisis of the 1980s, its CEO John Reed helped mid-wife the Economics Program at the Santa Fe Institute. He wanted the new theoretical insights about financial crises that the new complexity scientists promised. Ever since, the complexity scientists have been telling us that markets are self-organizing systems. For the life of me, I can’t see how this puts us way ahead. Didn’t seem to help Citibank either, which I’ve noticed is back in the headlines.
Then as now, a key recommendation is to recruit some “good” scientists (their modifier, not mine; see the second paragraph from the pull quote) from other fields. I guess that these outsiders are supposed to purge economics and finance of the aforementioned ideology and failed assumptions. But before we put up money for an “economic Manhattan Project,” wouldn’t it make sense to ask if there is any evidence to support the basic claim here–that more theory, developed by people who don’t have domain experience, is the key to scientific progress in macroeconomics and finance?
Oh, yes, speaking of evidence. Andrew Lo from MIT, someone who actually studies financial markets as his day job, suggested in testimony before Congress that we create a system that would do for each financial crisis what the National Transportation Safety Board (NTSB) does for each plane crash: collect evidence on what actually happened.
To be successful, a Capital Markets Safety Board (CMSB) would require both funding and careful attention to incentives. Like the NTSB, a CMSB should be truly independent from the government agencies that are responsible for crisis prevention and crisis management. It should also be protected from influence by firms in the financial sector. In its data collection efforts, it should not rely on university researchers who are themselves susceptible to influence by the interested government agencies or the private sector players. Nor should it use academics who have a personal or professional stake in any particular view about what caused a crisis. It’s the soft corruption of lobbying and regulatory capture that should worry us, not ideology. Institutionalized transparency is the best antidote.
A CMSB could have its own staff of talented, neutral researchers. It could have the power to compel testimony and to inspect and summarize (but not reveal) confidential data from private and public sources. It might even be able to mandate data collection systems analogous to the black boxes required in commercial aircraft. To the extent possible, its evidence about individual actions should not be accessible to the authorities responsible for regulatory, criminal or civil proceedings, or God help us, members of Congress looking for headlines. A CMSB should have no responsibility for proposing laws, regulations, or institutional reforms. Like the NTSB, its only job should be to establish a reliable body of evidence about what happened during each crisis and make it available for others to use.
I agree with the closing suggestion, that air transport is a good place to look for lessons about avoiding crashes. No matter what we do, we will still have financial crises, just as we still have plane crashes. But judging from our success in making air travel safer, it seems reasonable to bet on an institutionalized commitment to systematic data collection as a way to reduce their frequency and severity. As a traveler and an investor, I feel much safer in the hands of experts with good data than tourists from other scientific disciplines bearing new models.
A comeback for the commons is what we need. Most great leaps forward in economic development happened because people started working together and trusting each other. It was often the result of a crisis or the advent of a new technological possibility. The science of economics has assumed the opposite: that we are all members of another species, homo economicus, consisting of selfish and entirely rational agents acting to optimize individual wealth.
We now know from experimental economics, game theory and the anthropology of gift giving that this creature exists only in the mind of economists, not in the real world. Humans (and other primates) treat each other with empathy and a striving for fair play (often through the punishment of free riders). Therefore the laudable new discussion of models of the economic system fail to discuss the real issue: Our model of human beings. And it fails to discuss the crucial externality to the economic process: Sometimes we decide to do great things that will lift each and everyone up where we belong. These great things can be nice creations like the welfare state, general education systems, infrastructure, going to the Moon, saving the climate or creating Web 2.0. They can also be not so nice stuff such as wars or other destructive and wasteful shows of power.
Cooperation is what important. Scientific study can do a lot here: Study the historical cases, dig out all the reasons for the origin of wealth. Complex systems behavior also plays a role, obviously, but the essence is that the true center of gravity for the trends in the economic system is society, not economics. Therefore, there is an immediate implication of all this: Society must act to get the economy working. It is not the other way around. Hence scientists should study society and follow the human behavior, not the money.
Mathematician and Economist; Managing Director of Thiel Capital
As the person who originated the proposal of an “economic Manhattan project” mentioned in the Brown, Kauffman, Palmrose, & Smolin essay, I should probably explain why I am not a co-author. What is happening in this discussion is that the proposal is, as I feared, being treated as a kind of economic Rorschach test in which people imagine for themselves what an “economic Manhattan project” might mean. Does it mean a soviet style planned economy? A priestly cabal of researchers taking sabbaticals, far away from their core competencies, to play ‘mad scientist’ with the economy? A secret group searching for a simple unified field equation that can be proven to predict security price movement?
Happily the answer is “no” several times over, so let me clarify and remove some of the confusion.
The idea for a Manhattan project was born in the wake of a number of email exchanges and conversations with some of the people I most respect in the financial world, in which I tried to find out if the people who showed by their research, public speaking and trading that they anticipated crisis, were now being called in to help. Prominent among the people I thought should be called in to help were in fact, Emanuel Derman and Nassim Taleb.
Typical of these investigations was a call to a mathematical colleague and co-author Adil Abdulali who had prominently lectured and published on the danger and fraud inherent in the valuation of mortgage backed securities. He told me that he too had not been contacted by anyone and claimed that that corporate types within the banks were finding an eager media audience for the theory that ‘rocket scientists’ and PhDs had caused the financial world to collapse because their models had supposedly treated human beings like protons (or some other such nonsense).
In short, a system of selective pressures was, to my way of thinking, being installed to short circuit the natural reordering of the universe of ‘experts’ by this disaster.
At least to my knowledge, not one of the constructive scientifically minded people who had earned our attention and trust by speaking up before the upheaval, were being called in on these secret meetings where the fate of the world was being decided. It was in this light that I began mentioning to people the idea of an economic Manhattan project, away from the corridors of power, staffed by credible people who as scientists were comfortable with their own ignorance and unintimidatated by those with titles or authoritative baritones.
To be clear, the world’s markets are going to be analyzed, modeled, and regulated by panels of “experts”. That is not at issue. What is at issue is whether the scientific community has the moral luxury, as some commenters here heartily recommend, to sit this one out and complain from the sidelines when most of the skills needed to debunk seemingly sophisticated failed market theory are scientific in origin. But to believe in one’s own ability to improve a theory and make contributions across disciplines require taking serious risk and I well understand that some may find such risk frightening. I would be happy enough for those who feel sure they have nothing to contribute to avoid such an undertaking.
But there are plenty of constructive and imaginative risk takers who know just how flawed this system really was. The original Manhattan project was never a sure thing. It was born of a recognition that we needed to risk succeeding at the price of possible failure. Yet, had it failed it is not clear that we would have been much worse off than had we never dared to try. By any definition of ‘scientific community’, those who study systems and demonstrate some measure of predictive or explanatory power have to be considered scientists. To our way of thinking, the biologist traveling to the Amazon to meet with a tribal medicine man may be seen as one scientist visiting the pharmacological laboratory of another. Therefore Taleb’s argument that markets should be left to practioners is based on a misunderstanding, as the practioners with predictive power are arguably the most important scientists of this system.
We have had a massive failure of a dominant model. We are trying to find the right people to deal with this failure. And, at least some of those people are likely to be unfamiliar voices. It is as simple as that.
Here is the basic idea to first approximation which will be spelled out in detail elsewhere. First there needs to be a great debunking of bad models as well as an elevation of those models which are either already mature or readily salvageable. This could turn out to be a bit more subtle than some scientists appreciate, for while many of the holes in economic and financial theory are wallpapered over with obvious error, not all of the defects are irreparable.
Some salvageable models have been kept on life support by adding epicycle after epicycle to deal with their obvious incompatibilities with markets and observed human behavior (e.g. the inexplicable neoclassical insistence on fixed preferences) while common risk measures like Value-at-Risk are probably flawed beyond hope. And yet, insights from fields as diverse as gauge theory, and evolutionary psychology may have a positive role to play in healing what ails the cartoonish homo economicus model to create a more realistic scientific theory of market agency. But the best way to do this is too subject the models to a marketplace of ideas somewhat more vigorous than modern day economics.
Next we need to get financial economists to become more comfortable with their new found ignorance in the wake of this failure. This likely entails a profound move away from ‘risk management’ towards the “management of uncertainty”. Uncertainty is effectively a second order effect, which deals with the risk that the risk assessment is wrong. It should now be clear to all that it is high time to bring this long neglected 1920’s theory of economist Frank Knight back from the periphery and treat it with the respect it has just earned and the modern mathematics it requires. Additionally there needs to be a reckoning with the ignorance and incentive incompatibility, exposed by the debunking.
To put it simply, there is something of a conservation equation: the smaller our knowledge of markets, the greater our financial reserves need to be to withstand the uncertainty in the system. What we have learned is that our communal financial ignorance is far greater than many of our financial elders would have lead us to believe.
If we had embraced uncertainty management rather than risk management and done even a rudimentary job of estimating the uncertainty in the system, the mathematics would not have been nearly as courteous to the preposterous positions taken in the markets, which doomed our last financial system. This is to say nothing of the myriad principal-agent problems with incentives rampant throughout the system.
To sum up, most all of the criticisms of the financial Manhattan project mentioned have been valid critiques but appear addressed to utopian projects that are not under discussion so far as I am aware. There was however a valid criticism about ideology which should be addressed.
We in favor of an EMP are possessed of an ideology as Larry Sanger correctly points out, if a somewhat minimal one given the extraordinary circumstances. Let’s spell it out. Many of us who work in finance are even more horrified by what we see than the lay public appears to be. Some of us spoke publically for years about the dangers posed. Others published papers or books to spread the word. Curiously, however, our country’s laws would not even permit average families to voluntarily invest in those hedge funds that profited from this crisis by, for example, shorting subprime mortgages.
Accordingly, we don’t believe that citizenship in the United States should now hurriedly be converted into forced participation in an unaccountable secretive national hedge fund which buys lousy assets at inflated prices from banks mismanaged for personal profit by multi-millionaires, and makes non-consensual capital calls on uninformed, captive, financially unsophisticated families.
Oddly, that’s not hyperbole. That’s a description of what has taken place. It’s the reality that’s objectively outrageous.
We think that just like high net worth individuals are represented by ‘family offices’, those average families deserve now to be represented at the table by financially and scientifically sophisticated individuals with the character necessary to question cronyism and the scientific background to debunk financial mischief dressed in Greek letters and accessorized with stochastic differential equations.
Sometimes, it takes a good physicist to keep another honest. Or an economist. Or trader.
Professor of Psychology and Neuroscience; Stanford University
WHEN WAITING IS THE HARDEST PART
How might the psychology of individuals matter in today’s market?
Herbert Simon famously proposed that individuals suffer from “bounded” rationality— they can’t attend to or remember everything all the time. Thus, far from optimizing, people muddle through decisions and “satisfice” instead. However, bounded rationality might only add noise, and needn’t systematically bias people’s risk preferences. On the other hand, recent theorists have argued that anticipatory emotional states (e.g., fear, excitement) can bias peoples’ risk preferences, and have begun to develop tools that allow them to test their claims.
For instance, neuroeconomists—including a motley crew of economists, psychologists, neuroscientists, and others—study how the brain makes decisions. Neuroimaging techniques have advanced over the past ten years such that they now allow scientists to track second-to-second changes in the activity of deep subcortical regions. This means we can examine activity in regions of the brain implicated in emotion not only after a decision has been made, but also during and even prior to the decision.
What have these fancy techniques revealed so far? First, the brain responds to uncertain future outcomes in a specific region (i.e., the anterior insula), and ambiguity (not knowing the probabilities of uncertain outcomes) provokes even greater activation in this same region. Further, insular activation precedes risk avoidance in investment tasks, and is even more pronounced before people “irrationally” avoid risks (i.e., or violate the choices of a risk-neutral, Bayesian-updating “rational” actor). Inflict enough ambiguity on enough people and you can immediately sense that they might lean towards risk aversion.
What are some implications of these findings for the current crisis? Presently, we need to put a price on ambiguous derivatives (a job for the economists). As long as the value of these contracts remains unresolved, this could generate ultra-uncertainty, which will promote fear, which will keep money in peoples’ mattresses and out of the market. In the future, we should regulate (or incentivize) against contracts that resist pricing.
Paradoxically (and inconsistent with expected value), ambiguity preceding a terrible outcome might be worse than the outcome itself. This could apply to derivatives or any dangerous scenario. Imagine teetering on the edge of a cliff in broad daylight. You look down to see a river running over rocks 100 meters below. Now imagine teetering on the edge of another cliff in the dead of the night. You peer down into a void. Which do you prefer?
Distinguished Professor of Risk Engineering, New York University School of Engineering ; Author, Incerto (Antifragile, The Black Swan…)
Essayist and Mathematical Trader; Author, The Black Swan
I spent close to 21 years in finance facing “scientists” in some field who show up in finance and economics, realize that economists and practitioners are not as smart as they are (they are not as “rigorous” and did not score as high in math), then think they can figure it all out.
Nice, commendable impulse, but I blame the banking crisis (and other blowups) on such “scientism”.
We’ve seen them from all fields of study—particularly that finance pays (used to pay) a multiple of what someone can earn in the more respectable sciences. We’ve seen people from astrophysics, statistical mechanics, mathematical biology, pure mathematics, applied mathematics, semi-applied mathematics, probability theory, engineering, solid state physics, turbulence … literally ever single field.
Meanwhile the most robust understanding is present among practitioners who do not have the instinct to reduce ambiguity and uncertainty that scientists have. I urge all you scientists to go take your “science” where it may work—and leave us in the real world without more problems. Please, please, enough of this “science”. We have enough problems without you.
Media Analyst; Documentary Writer; Author, Throwing Rocks at the Google Bus
Media Analyst; Documentary Writer; Author, Get Back in the Box: Innovation from the Inside Out
What an important conversation. This is precisely what I’ve been working on the for the past eight years, since the first leg of the dot.com crash (we’re now in the second one).
So far, the relationship of science to economics has been a corrupt marriage of self-interested parties. Scientists and mathematicians with models or discoveries compatible with free market ideology are plucked selectively from academia, stationed in well-funded institutes, and then promoted relentlessly with the very same dollars funding Cato Institute and the Heritage Foundation. Thus, the main contributions of science to economics are passed through a neo-libertarian filter, resulting in a skewed and oversimplified set of game theory principles, an at-best metaphorical relationship to systems theory, and a rather contemptuous perspective on human behavior as impulsive and only significant as an emergent, collective phenomenon.
The greatest danger of this compromised and arbitrary “scientific-style” approach to economics is that it implies an equivalence of the economy with nature. The sense is that the economy is really an ecology in which the laws of physics and nature actually apply. Sure they apply, but only as much as they apply to any utterly synthetic and manufactured environment. It’s like applying the laws of nature to a Monopoly board or poker table. It ignores the fact that there are invented rules—often unacknowledged or utterly forgotten and internalized—that supersede the physics or nature beneath them.
Concepts such as interest, currency, debt, and LIBOR may have corollaries in nature and measurements in physics, but so do other human-created values such as harmony, Amazon rankings, and foul shot percentage. We get in the most trouble of all when we mistake these abstracted game metrics and their interrelationship for what’s going on in the natural world. For these measures were developed with very specific biases in mind—biases whose very intent was to prevent natural cycles and changes from taking place.
In just one of many examples, centralized currency—the kind we use today—was invented during the Renaissance as a way of curbing the creation of wealth from the bottom up. The people of Late Middle Ages Europe enjoyed the use of multiple local currencies, biased towards re-investment and transaction. They became so wealthy, they invested in their local futures in the form of cathedrals and preventative maintenance on their infrastructure. People ate and lived well: women in England were taller in the Late Middle Ages than at any time up to the 1970s. A waning monarchy with limited means of generating value to compete with the rising merchant class saw this immense and distributed wealth as an opportunity for extraction. Kings outlawed local currencies and forced subjects to use “coin of the realm,” lent into existence at interest.
My point is less to expose the rather unkind origins of central money systems than to reveal the unacknowledged biases of these currencies themselves. Our money isn’t water—it is a programmed currency, heavily biased towards wealth extraction from the periphery to the center. So any scientific analysis of the current economy can not be undertaken without acknowledgment of the fact that its many emergent currency systems have been outlawed and repressed in favor of just one highly managed one. We would like to treat the economy as the circulatory system of an organism, but the components of the bloodstream have been quite artificially limited to those that favor cell depletion and value extraction.
The very best thing science can offer economics is a fuller understanding of what it means to work with a model. While most economists are more than willing to admit that their equations and theories are mere models, they seem unprepared to acknowledge that the economy on which those models are based is itself a model. Economics is at best a modeling of a model—a systemic approach to a game. And that game isn’t even being played particularly well or intentionally by most of us; we have better things to do with our time than calculate the efficiency of our transactions, and more pressing emotional needs than value maximization.
An economics Manhattan project would have to begin by parsing human need and transactional principles from the game theory and programmed biases of the economy. It would require an open-minded approach to the transactional requirements of human populations—one that transcended the orthodoxies of over-simplified single currency schemes, and acknowledged the historical and political processes through which they were created. Most importantly, adherence to the scientific method would help economists remember the underlying assumptions of their theories and models. (The presumption underlying the law of competitive advantage, for example, is that both nations exporting their goods and services are operating at full employment—an assumption conveniently forgotten in most IMF white papers.)
So while I’m extremely wary of falsely bolstering the artificial models of economists with the language and seeming certainty of the hard sciences, I do welcome, encourage, and hope to participate in any application of the scientific method to principles of the market.
In short, science could help economists by restoring their memory of which is the map, and which is the territory.