Economics: Structure or Behavior

Economics: Structure or Behavior
Shyam Sunder, Yale University Santa Fe Institute Business Network Conference on Agent Models in Financial Economics New York, October 21, 2005

(c) Copyright Shyam Sunder
Overview Experimental Economics and asset markets Zero-intelligence agents Structure or behavior 12/1/2018 (c) Copyright Shyam Sunder

Experimental Economics
Why do we need even more data on financial markets? What have we learned so far from asset market experiments? What is next? 12/1/2018 (c) Copyright Shyam Sunder

Zero-Intelligence Agents
What have we learned? What are its implications 12/1/2018 (c) Copyright Shyam Sunder

Structure or Behavior Origin of experimental economics in examination of aggregate phenomena Gradual, steady shift towards micro-levels due to Analytical process and reasoning Incremental research questions Unlike assumption in theory, people do not optimize well by intuition Today, much experimental work has shifted to examination of individual behavior and of economies populated by artificial agents Shift to individual behavior has accentuated the ever-present dilemma of social sciences in trying to be a science on one hand, and handle humans at the same time What are the antecedents and consequences of this trend? Usefulness of organizing experimental economics into three streams: Structural: macro properties of social structures Behavioral: behavior of individuals, and Agent-based: exploration of links between the micro and macro phenomena At least the structural part of economics can be firmly rooted in the tradition of sciences, bypassing the free-will dilemma of social sciences 12/1/2018 (c) Copyright Shyam Sunder

Why Do We Need More Data from Experimental Markets?
Of all branches of economics, financial economics probably has the most detailed and up-to-date observational data available Consequently, this branch of economics is characterized by one of the strongest empirical traditions Why, then, spend time and money to conduct experiments with financial markets and gather more data? 12/1/2018 (c) Copyright Shyam Sunder

Transactions v. Expectations
Data from the stock exchanges include bids, asks, transaction prices, volume, etc. Data from information services on actions and events that may influence markets Neither does or can report on expectations Theory of financial markets (and economics of uncertainty) is built on expectations Need data on expectations to empirically distinguish among competing theories 12/1/2018 (c) Copyright Shyam Sunder

Relating Market Actions to Expectations and Parameters
In experimental markets, the researcher knows the expectations, and the underlying parameters With this knowledge, we know the price and other predictions of alternative theories We can therefore conduct powerful tests of theories which were not possible from the field data (because we know little about the parameters and expectations that generate the field data from the stock exchanges). Examples to follow 12/1/2018 (c) Copyright Shyam Sunder

What Have We Learned from Experiments?
Within the past two decades, asset market experiments have revealed some important findings by exploiting their advantages These findings were not, and could not have been, reached from the field data alone I shall summarize a few key findings in the time available 12/1/2018 (c) Copyright Shyam Sunder

Key Findings Security markets can aggregate and disseminate information (efficient markets) But they do not always do so (inefficiency) Information dissemination, when it occurs, is rarely instantaneous or perfect (learning takes time) Markets permit costly research to persist in equilibrium (why pay for research?) Price, as well as bids, offers, timing, etc., transmit information (many channels for information flow) 12/1/2018 (c) Copyright Shyam Sunder

More Findings Well-functioning derivative markets help improve the efficiency of primary markets Most important: statistical efficiency or inability to make money from past data does not mean informational efficiency (can’t make money does not imply the price is right) Bubbles arise when the path to backward induction of value from future payoffs is blocked, and investors are forced to use forward induction 12/1/2018 (c) Copyright Shyam Sunder

Efficient Markets: Information Dissemination
Can markets disseminate information from those who know to those who don’t? Could not be established from analysis of field data because we don’t know the distribution of information among investors Plott and Sunder (Journal of Political Economy, 1982) established the basic proposition through a simple experiment 12/1/2018 (c) Copyright Shyam Sunder

Equilibria in a Simple Asset Market
State X State Y Prob.=0.4 Prob.=0.6 Trader Type Expected Dividend I 400 100 220 II 300 150 210 125 175 155 PI Eq. Price Asset Holder I Informed Trader Type I Uninformed RE Eq. Price Trader Type I Trader Type III 12/1/2018 (c) Copyright Shyam Sunder

12/1/2018 (c) Copyright Shyam Sunder

Results Markets can disseminate information from the informed to the uninformed investors Dissemination can occur through trading, without exchange of verbal communication Such markets can achieve high levels of informational efficiency Securities end up in the hands of those who value them most 12/1/2018 (c) Copyright Shyam Sunder

Efficient Markets: Information Aggregation
Can markets behave as if diverse information in the hands of a few is aggregated so it is in the hands of all? Suppose there are three possible states of the world: X, Y and Z Some people know that the state is Not X, and some people know that it is Not Y Can the market behave as if everyone knows that the state is Z? Plott and Sunder (Econometrica, 1988) 12/1/2018 (c) Copyright Shyam Sunder

Equilibria in an Information Aggregation Experiment
Traders X-Asset Y-Asset Z-Asset I II III X Y Z 0 0 0 0 RE Eq. P Holder II - III - I 12/1/2018 (c) Copyright Shyam Sunder

Aggregation Results Complete markets can aggregate and disseminate diverse information Such markets can achieve high information and allocative efficiency Same happens when investors have homogenous preferences (which makes it easier for traders to infer information from actions of others) 12/1/2018 (c) Copyright Shyam Sunder

Inefficiency Just because markets can aggregate and disseminate information does not mean that all markets do so under all conditions Experiments show that market conditions must allow investors the opportunity to learn the information from what they can observe Even in these simple experimental markets, these conditions are not always satisfied (too many states, too few observations and repetitions to facilitate learning) For example, a complete market for three Arrow-Debreu securities is efficient, but an incomplete market for a single security is not 12/1/2018 (c) Copyright Shyam Sunder

Learning Takes Time Even in the best of circumstances, equilibrium outcomes are not achieved instantaneously Markets tend toward efficiency, but cannot achieve it instantaneously It takes time for investors to observe, form conjectures, test them, modify their strategies, etc. With repetition, investors get better at learning, but the environment changes continually, including the behavior of other investors; so the learning process never ends 12/1/2018 (c) Copyright Shyam Sunder

Why Pay for Research? This has been the conundrum of the efficient market theory: If the markets are efficient, why pay for research. This finite rate of learning makes it possible to support costly research, even in markets which tend toward efficient outcomes Enough people would conduct research so the average returns to research equal the average cost. Research users have higher gross profits, but their net profits are the same as the profits of the others Sunder (Econometrica, 1993) 12/1/2018 (c) Copyright Shyam Sunder

Difference between Gross and Net Profits of the Informed and the Uninformed Traders 12/1/2018 (c) Copyright Shyam Sunder

Results As investors learn (in a fixed environment), their value of information decreases because they can ride free on others’ information Market price of information falls If the supply of information can be sustained at the lower price, price drops to a level sustainable by learning frictions: get equilibrium If the supply of information also falls with its price, we get a noisy equilibrium Consequences of mandating provision of free research to investors 12/1/2018 (c) Copyright Shyam Sunder

Many Channels for Information Flow
In economic theory, transaction prices are considered the primary vehicle for the transmission of information in markets Experimental markets show that other observables (bids, asks, volume, timing, etc.) also transmit information in markets In deep markets, price can be the result of the information transmission through these other variables Example: see trading in period 8 on slide number 15 12/1/2018 (c) Copyright Shyam Sunder

Effect of Derivatives on Primary Markets
Derivative markets help increase the efficiency of primary markets Forsythe, Palfrey and Plott (Econometrica 1982), the first asset market experiment, showed that futures markets speed up convergence to equilibrium in the primary market Kluger and Wyatt found that the option markets increase the informational efficiency of the equity market 12/1/2018 (c) Copyright Shyam Sunder

Convergence with Two Spot Markets

Convergence with a Spot and a Futures Market

Error in Pricing Primary Security with and without Option Market

How Can We Tell If a Market is Efficient?
Traditional statistical approach: if you can’t make money from information (past data, public, or all information), the market is efficient Experiments: statistical efficiency is a necessary but not a sufficient condition for the informational efficiency of markets Example: Slide 19, second half is efficient by statistical criteria but is not informationally efficient Even when investors know that the price is not right, they may have no means of profiting from that knowledge 12/1/2018 (c) Copyright Shyam Sunder

Bubbles in Simple Asset Markets
Smith, Suchanek and Williams (Econometrica 1988) showed that bubbles can arise in simple asset markets with inexperienced subjects, and tend to diminish with experience Lei, Noussair and Plott (Econometrica 2001) showed that bubbles can arise even when investors cannot engage in speculative trades. They suggest that bubbles can arise from errors in decision making even in absence of a lack of common knowledge of rationality (“bigger fool” beliefs) 12/1/2018 (c) Copyright Shyam Sunder

Valuing Equity without Dividend Anchors
Fundamental economic model of valuation is DCF When security matures beyond investment horizon, personal DCF includes the sale price at horizon Sale price depends on other investors’ expectations of DCF beyond my own horizon Applying DCF involves backward inducting from the maturity of the security through the expectations and valuations of the future “generations” of investors 12/1/2018 (c) Copyright Shyam Sunder

Valuation be Equal to Fundamental Value if
All investors form rational expectations All investors form higher order expectations Common knowledge of rational expectations Common knowledge of higher order expectations Common knowledge of maturity These are very difficult conditions to meet in a market Bubbles arise, even with rational investors who make no errors, if they cannot backward induct the DCF 12/1/2018 (c) Copyright Shyam Sunder

Exogenous Terminal Payoff
Period 1 Period 15 15-period security, termination date is common knowledge Single terminal dividend (may vary across traders) A separate set of subjects record their price predictions at the beginning of each period, their mean is announced at the end of each period Hirota and Sunder (2004), Yale Working Paper 12/1/2018 (c) Copyright Shyam Sunder

Figure 4: Stock Prices and Efficiency of Allocations for Session 4
(Exogenous Terminal Payoff Session) 12/1/2018 (c) Copyright Shyam Sunder

Endogenous Terminal Payoff
Period 30 Period 1 Period 15-17 30-Period Security with a terminal dividend to be paid at the end of period 30 The security will be terminated earlier than 30 at a time written in the sealed envelope (in fact terminated at periods) Liquidation at average of the prices predicted by the predictors for the period following termination 12/1/2018 (c) Copyright Shyam Sunder

Figure 10: Stock Prices and Efficiency of Allocations for Session 8
(Endogenous Terminal Payoff Session) 12/1/2018 (c) Copyright Shyam Sunder

Understanding Bubbles
DCF valuation makes heroic assumptions about the knowledge necessary to do backward induction Even if investors are rational and make no mistakes, it is unlikely that they can have the common knowledge necessary for price to be equal to fundamental valuation in a market populated by limited horizon investors Not surprisingly, pricing of new technology, high growth, and high risk equities are more susceptible to bubbles In such circumstances, if we do not have common knowledge of higher order beliefs, how can we test valuation theories? 12/1/2018 (c) Copyright Shyam Sunder

Zero-Intelligence Traders
From experimental economics to agent economics Show simulation 12/1/2018 (c) Copyright Shyam Sunder

Smith (1962) Chart 1 12/1/2018 (c) Copyright Shyam Sunder

Shift Towards Micro Phenomena
Focus of experimental economics has gradually shifted from aggregate market level phenomena towards individual behavior Three factors seem to drive this shift The logic of analytical method Incremental research designs Empirical finding that people, acting by intuition alone, are not good at optimization as typically assumed in derivation of equilibria in economic theory 12/1/2018 (c) Copyright Shyam Sunder

Individual Behavior and the Dilemma of Social Sciences
This shift towards micro-behavior confronts economics with a fundamental dilemma shared among the social sciences As a science, we seek general laws that apply everywhere at all time, emulating physics, chemistry and biology Perfecting the scope and power of general laws of human behavior also implies squeezing out the essence of humanity—our free will What does it mean to have a science of individual human behavior? 12/1/2018 (c) Copyright Shyam Sunder

Free Will Free will, independent thinking, and ability to choose are essential to our concept of self We believe in our power and ability to do what we wish, beyond what is predictable on the basis of our circumstances, beliefs, and tendencies Ability to rise above our circumstances as the essence of human identity We can choose deliberately, in ways unpredictable to others Else, we would slip to the status we assign to animals, plants and inanimate objects 12/1/2018 (c) Copyright Shyam Sunder

Humanities: Eternal Truths
Humanities celebrate infinite variety of human behavior, but no laws of behavior In epics and literature: eternal verities, but no laws of behavior Epics (Mahabharata, Iliad) Duryodhana, Yudhishtira, Arjuna Literature (Dante’s Inferno, Shakespeare’s Hamlet) Human truths, questions, and tendencies repeated through history, always with a new twist People choose in ways unpredictable on the basis of their circumstances Celebration of infinite variation in human nature 12/1/2018 (c) Copyright Shyam Sunder

Science: Eternal Laws Identifying laws of nature valid everywhere and all the time Essence: regularities of nature captured in known and knowable relationships among observable elements (including stochastic) Helps understand, explain, and predict If I know X, can I form a better idea of whether Y was, is or will be? Objects of science have no free will A photon does not pause to enjoy the scenery A marble rolling down the side of a bowl does not wonder about how hot the oil at the bottom is 12/1/2018 (c) Copyright Shyam Sunder

Social Science: Irresistible Force Meets Immovable Object
Free will essential to our concept of self Without the freedom to act, we would be no different than a piece of rock Yet, the object of study in social science is us As a science, it must look for eternal laws that apply to humanity But stripped of freedom to act, and subject to such laws, there can be no humanity 12/1/2018 (c) Copyright Shyam Sunder

Mismatch of Science and Personal Responsibility
Objects of science can have no personal responsibility They do not choose to do anything They are merely driven by their circumstances, like a piece of paper blown by gusts of wind, or a piece of rock rolling down the hill under force of gravity in the path of an oncoming car Or, perhaps an abused child who grows up to be an abusive parent, sans personal responsibility Science and personal responsibility do not mix well 12/1/2018 (c) Copyright Shyam Sunder

Dilemma of Social Science
Do we abandon free will, personal responsibility, and special human identity; and treat humans like other objects of science? That is, drop the “social” and become a plain vanilla science Or, do we abandon the search for universal laws, embrace human free will and unending variation of behavior, and join the humanities Either way, there will be no social science left Is there a way to keep “social” and “science” together in social science? 12/1/2018 (c) Copyright Shyam Sunder

Isolating Three Streams of Work
Perhaps there is no general solution to this dilemma The dilemma does, however, point to the potential value of isolating streams of work where it may be more or less of a problem Significant parts of social sciences, and a large part of economics, are concerned with aggregate level outcomes of socio-economic institutions Institutions themselves do not need to be ascribed intentionality or free will Characteristics of the institutions can be analyzed by methods of science without running into these dilemmas This will leave analysis of individual behavior in the territory between science and humanities Agent-based models (in economics and elsewhere) could serve the bridging function between aggregate and individual phenomena Let us consider these possibilities 12/1/2018 (c) Copyright Shyam Sunder

Individuals I do not have much to add on the most complex problem of examining individual behavior It seems that we shall continue to examine ourselves and our behavior using both humanities as well as science perspectives, without ever reconciling the two into a single logical structure There seems to be no way out 12/1/2018 (c) Copyright Shyam Sunder

Institutions Experimental economics started out as investigation of aggregate level outcomes of market institutions using human subjects Attention has gradually shifted from aggregate outcomes to micro behavior Logic of analytical approach Incremental research designs A third reason is that predictions of aggregate outcomes (equilibrium analysis) are typically made assuming optimization by individuals Cognitive psychology showed that individuals are not very good at optimization by intuition This mismatch between the optimization assumption actual behavior at individual level has given additional impetus to “micro-nization” of experimental economics Thanks to recent findings using agent-based methods, we can conduct the study of social-economic institutions using methods of science 12/1/2018 (c) Copyright Shyam Sunder

Optimization and Equilibrium
The standard approach of economic analysis has been to assume that individuals choose actions by optimizing given their preferences, information and opportunity sets Interaction of individual actions in the context of institutional rules yield outcomes (e.g., prices and allocations), equilibrium outcomes being of special interest Equilibrium predictions derived from assuming individual rationality could be suspect when such rationality assumption is not valid Agent-based simulations suggest that individual rationality can be sufficient but not necessary for attaining equilibria in the context of specific market institutions 12/1/2018 (c) Copyright Shyam Sunder

What Makes the Difference Gode and Sunder (JPE 1993)

What Makes the Difference

Why Equilibrium without Individual Optimization
Why do the markets populated with simple budget-constrained random bid/ask strategies converge close to Walrasian prediction in price and allocative efficiency No memory, learning, adaptation, maximization, even bounded rationality Search for programming and system errors did not yield fruit Modeling and analysis supported simulation results 12/1/2018 (c) Copyright Shyam Sunder

Inference Perhaps it is the structure, not behavior, that accounts for the first order magnitude of outcomes in competitive settings Computers and experiments with simple agents opened a new window into a previously inaccessible aspect of economics Ironically, it was not through computers’ celebrated optimization capability Instead, through deconstruction of human behavior Isolating the market level consequences of simple or arbitrarily chosen classes of individual behavior modeled as software agents 12/1/2018 (c) Copyright Shyam Sunder

Optimization Principle
In physics: marbles and photons “behave” but are not attributed any intention or purpose Yet, optimization principle has proved to be an excellent guide to how physical and biological systems as a whole behave At multiple hierarchical levels--brain, ganglion, and individual cell—physical placement of neural components appears consistent with a single, simple goal: minimize cost of connections among the components. The most dramatic instance of this "save wire" organizing principle is reported for adjacencies among ganglia in the nematode nervous system; among about 40,000,000 alternative layout orderings, the actual ganglion placement in fact requires the least total connection length. In addition, evidence supports a component placement optimization hypothesis for positioning of individual neurons in the nematode, and also for positioning of mammalian cortical areas. (Makes you wonder what went wrong with human design when you see all the biases and incompetence of human cognition. Could it be just the wrong benchmark?) Questions about “forests” and questions about “trees” 12/1/2018 (c) Copyright Shyam Sunder

Optimization Principle Imported into Economics
Humans and human systems as objects of economic analysis Conflict between mechanical application of optimization principle and our self-esteem (free will) Optimization principle interpreted as a behavioral principle, shifting focus from aggregate to individual behavior Cognitive science: we are not good at optimizing Willingness among economists to abandon the optimization principle 12/1/2018 (c) Copyright Shyam Sunder

Dropping the “Infinite Faculties” Assumption
Conlisk: Empirical evidence in favor of bounded rationality Empirical evidence on importance of bounded rationality Proven track record of bounded rationality models (in explaining individual behavior) Unconvincing logic of unbounded rationality All these reasons focus on the “trees” not “forest” 12/1/2018 (c) Copyright Shyam Sunder

Equilibrium and Simon Simon in the third edition of The Sciences of the Artificial wrote: “This skyhook-skyscraper construction of science from the roof down to the yet unconstructed foundations was possible because the behavior of the system at each level depended on only a very approximate, simplified, abstracted characterization of the system at the level next beneath. This is lucky, else the safety of bridges and airplanes might depend on the correctness of the ‘Eightfold Way’ of looking at elementary particles.” Indeed, the powerful results of economic theory were derived from “a very approximate, simplified, abstracted characterization of the system at the level next beneath,”—the economic man so maligned, and its scientific purpose and role so misunderstood, by many who claim to be followers of Simon 12/1/2018 (c) Copyright Shyam Sunder

Economics: Structural or Behavioral
Economics can be usefully thought of as a behavioral science in the sense physicists study the “behavior” of marbles and photons Given the pride we take in attributing the endowment of free will to ourselves, this interpretation of behavior is a hard sell in social sciences To build on the achievements of theory, it may be better if we think of optimization in economics as a structural principle, Just as physicists (and many biologists) do This will allow us to focus on structural stream of economics in the tradition of sciences Individual behavior is likely to remain as a shared domain of humanities and sciences Modeling specific behaviors as software agents in the context of specific economic institutions allows us to make conditional statements about the links between individual and aggregate level phenomena (as in the case of ZI agents) 12/1/2018 (c) Copyright Shyam Sunder

Implications Should we design robots/agents to mimic humans? What kind of humanss? What do we assume about their behavior? Does it matter? Or do we modify human behavior using models or agents To what end? Understanding individuals Understanding markets Looking for competitive advantage Each winning strategies carries seed of its own demise Search for competitive advantage has no end point 12/1/2018 (c) Copyright Shyam Sunder

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