Economic%20Theory:%20Structural%20Abstraction%20or%20Behavioral%20Reduction

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Economic Theory Structural Abstraction or
Behavioral Reduction

• Shyam Sunder
• Yale University
• History of Political Economy Conference
• Duke 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 cellphysical 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

• Please send comments to
• Shyam.sunder_at_yale.edu