(c) 2005 shyam sunder, april 23, 2005 1 economic theory: structural abstraction or behavioral...

(c) 2005 Shyam Sunder, April 23, 2005

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Economic Theory: Structural Abstraction or Behavioral

ReductionShyam SunderYale University

History of Political Economy ConferenceDuke University, April 22-24, 2005


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Outline: Psychology and Reductionist Doubts

• Physics, optimization as an organizing principle of nature• Borrowed into economics, optimization was gradually

transformed from equilibrium abstraction into a behavioral principle

• Cognitive psychology: humans are not good intuitive optimizers: noisy, imprecise, learners

• A reductionist attitude to science expects derivation of aggregate outcomes from descriptively valid individual behavior; it is difficult, sometimes not possible

• The difficulty leads to doubts about the foundations of economic theory


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Outline: Computers Yield New Insights into Economics

• Computers seep into inaccessible parts of economics (Mirowski, 2002), transforming it in fundamental ways

• Discovery: the apparent mismatch between the cognitively bounded biological man and its optimizing cousin of econ texts is not necessarily a problem

• A good news-bad news story• The Good news: Economists can have their cake and psychologists

eat it too• The Bad news: Have to give up insistence on reducing economics

and psychology into a single science• The unity of science movement disbanded some 60 years ago, but

is alive and thriving in behavioral economics• Each science chooses its own level of abstraction; economic theory

chose one, just as psychologists choose another• A great deal of science would be impossible if we insist on

integrating all adjacent disciplines into a single logical structure


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Computation and Economics

• Each discipline is shaped by its tools

• Role of mechanical and electronic computers in shaping economics

• Computers may yet help shift the recent excessive preoccupation of economics with individual behavior, back to structural analysis


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Computers and Demand

• Lowered cost of transactions• Exploration of previously inaccessible possibilities

(computational chemistry and biology)• Fixed cost of discovery allows products to stay in the

market beyond the life of the discovery-enabling technology

• Speculative demand due to knowledge of demand with new communication technology

• New uses of older products and services• Life-style, migration and social organization-driven

demand changes• Endogeneity of demand and technology


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Computation and Markets

• Technology has given both greater specificity as well as greater ambiguity to definition of markets– Specificity through the salience of hardware and

software used to operate them– Ambiguity: when an investor in Boston buys a share

of stock of a firm with its headquarters in Cleveland, from another investor in Tokyo through a broker who lives in San Francisco and works for a Chicago brokerage house, in London Stock Exchange, with its hardware located in Frankfurt, software from Bangalore, and regulatory supervision of EU in Brussels, where is the market, and what is its extent?


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Computers Transforming Markets

• Organization and operation of markets

• As traders or traders’ assistants

• Science and engineering of markets


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Organization and Operation

• Reliable bookkeeping• Flexibility and accessibility of control information• Clearing and settlement• Reporting and dissemination of information• Monitoring and regulation (insider trading)• Risk management• Input of information to the markets• Demand for integration, legislation• Reorganization of markets


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Computer as a Trader

• Computational decision aids are ubquitous

• Substitution: exciting and controversial– Do human intelligence and world view define

the outer limits of what computers can “see” and do?

– Memory and speed are important components of intelligence. Computer programs for chess are already as good as the best human players. It is just a matter of time before …


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Tooling Up for Computational Economics

• William Norris, Control Data, PLATO for education and training in 1976

• Use of PLATO for economics experiments• PCs arrive• Modeling traders in order to

– Identify human algorithms from trading data– Design new, better algorithms– Replace paid subjects in econ experiments

• Economics and finance literature were of little help


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Stock Market Crash of 1987

• Blame on program trading• Proposal to teach a course on program

trading (with a background research agenda)

• Getting the platform, software, coding language and tools ready

• Market 2001 (turned out to be too optimistic)

• Show some data


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What Makes the Difference


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Class Experience

• Teaching becomes learning

• Efficiency as a measure of intellence

• Code length and performance

• Difficulties of identifying strategies from code– Not modular

• Does a superior strategy exist?

• Where is your strategy, Sir?


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This Makes No Sense

• Why did 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

• Intensive search for programming and system errors did not yield fruit

• Modeling and analysis supported simulation results

• Students versus ZI traders


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Our Inference

• Perhaps it is the structure, not behavior that accounts for the first order magnitude of outcomes in competitive settings

• Computers 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


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Time Dimension of Demand• Theory largely abstracts away from the time dimension of demand:

defined as quantity per unit of time, with discrete periodic market clearing through auction

• The difficulties of this abstraction became clear when Chamberlin (1948) and Smith (1962) tried to implement demand in lab and realized the difficulties

• Most markets require price making with continual trading (no discrete clearing)

• Should demand be interpreted as a steady rate of inflow of demand (and supply) units into the market with the hope that it will reach a (stochastic) steady state?– What would be the characteristics of market behavior?– Do the untraded (mostly extramarginal) units accumulate, or do they exit

the market? When?

•


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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”


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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• Increasing willingness among economists to

abandon the optimization principle


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Dropping the “Infinite Faculties” Assumption

• Conlisk: – Empricial 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 reasons focus on the “trees” not “forest”


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Equilibrium and Simon• Some time in 1993, Simon came to my office, we sat in front of my

computer and I showed him the pictures I showed you• His reaction: So, there is an answer after all! (to the apparent contradiction

between the discrepancy between cognitive man and its economic model). He was preparing the third edition of The Sciences of the Artificial and 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


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Economics: Behavioral or Structural

• 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


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Thank You

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(c) 2005 shyam sunder, april 23, 2005 1 economic theory: structural abstraction or behavioral...

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