Experiments with Minimally
Intelligent Agents and Minimal
Institutions: Structure and
Behavior
Shyam Sunder, Yale University
Barcelona LeeX Experimental Economics Summer
School in Macroeconomics
Universitat Pompeu Fabra
Barcelona, June 14, 2014
Humanities and Science
• Science does not know its debt to imagination.
Ralph Waldo Emerson
• Vivisection is a social evil because if it advances human knowledge,
it does so at the expense of human character.
George Bernard Shaw
• The theoretical broadening which comes from having many
humanities subjects on the campus is offset by the general dopiness
of the people who study these things.
Richard P. Feynman (Nobel Laureate in Physics)
• Economics has an amazing capacity to summarize staggeringly
complex phenomena by the application of only a handful of
principles
Charles R. Plott
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Overview
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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
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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
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At least the structural part of economics can be firmly rooted in the tradition
of sciences, bypassing the free-will dilemma of social sciences
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Examining Market Institutions
• Chamberlin (1948) examined the behavior of a market
institution under controlled conditions of his classroom
• Vernon Smith (1962), a subject of Chamberlin)
redesigned and systematically varied the market
conditions to examine price, allocation, and extraction of
surplus
• Both designs deviated significantly from Walrasian
tatonnement abstraction; they fleshed them out with
details, using stock market as a guide
– Economic environment (market demand and supply) and market
design as independent variables
– Market level outcomes as dependent variables
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Data from Experiments
• Experiments can yield a great deal of data
• Data are limited only by interest and imagination of the
experimenter, and ingenuity in capturing data without
distracting subjects from their task in a significant way
• Chamberlin gathered three pieces of data for each
transaction (price, seller cost and buyer value), and the
transaction sequence
• Examples of data he did not gather: the clock time of
transactions, details of the bargaining process (time
elapsed, price proposals, number of proposals, number
of counter-parties bargained with), etc.
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Data to Meet Experimental Goals
• Most experiments can yield a great deal of data
• We gather only what we need in order to address the
question(s) we wish to answer on the basis of the
experiment
• Constraints:
– Technology of data gathering, eased by development of
computer technology to conduct economics experiments)
– The possibility of interaction between data capture and subjects
• Given Chamberlin’s goals, asking subjects to report
their transactions immediately after they completed each
transaction served his purposes well, causing little
interference with subjects’ trading
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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
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Logic of Analytical Method
• It is rare for the correspondence between the predictions of
the relevant theory, and experimental data, to be nil or total
• If the experimenter has no or low expectation of
correspondence between the two, observation of even a
moderate relationship is seen as half full glass of water
• However, most experiments are designed to examine specific
theories that have some legitimate prior claim to predictive
power
• In such situations, any imperfections of correspondence
between data and theory are seen as half empty, not half full,
glass of water
• Seeking a fuller explanation to close the gap between data
and theory is a natural reaction of most investigators
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Search for Higher Explanatory
Power
• Following this logic, analysis and discussion of most
experiments ends in a search for ways to increase the
correspondence between data and theory
• Better prediction and explanation is the currency of
scientific progress
• We look for ways to modify the model to enhance its
explanatory power through analysis—breaking the
problem down into progressively smaller components
• This logical pursuit shifts research question(s) to the next
level of detail causing “micro-nization” of economics
• Discarding the details, to step back and see the big
picture, is a less common reaction
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Demand, Supply and Experiments
• Simple economic theory: point of intersection of
demand and supply determines price and
allocations
• Economists’ deep faith in theory
• Neither Chamberlin’s nor Smith’s data
corresponded precisely to the theory
• Smith saw half full glass of water, while
Chamberlin saw the half empty part and set out
to build a model to better explain the residual
variation left unexplained by the simple demandsupply model (instantaneous demand/supply)
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Chamberlin (1948), Figure 3
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Smith (1962) Chart 1
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Incremental Research Designs
• A good part of our research (including experimental) is
incremental, originating in proposals to gather data about
some additional aspect of behavior, or additional
analysis of existing data
• We make conjectures about how such data or analysis
might help explain residual variation
• Incremental work dominates graduate seminars focused
on critique and replication of extant work
• Easy to think of additional observations, motivations, and
information conditions associated with individual
participants to improve the fit between data and model
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Change in Models and Questions
• Both analytical logic and incremental
pursuits change the model used
• Additional variables use up some degrees
of freedom, but observations at micro-level
are far more numerous than at macro-level
• Shift to micro level also changes the
research question(s) being asked
– “Why is the price equal x?” might be replaced
by “why did trader y bid z?”
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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?
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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
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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
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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
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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
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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
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Neither Fish Nor Fowl
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This problem of social science is exemplified in the continuing attempts to
build a theory of choice
From science end: axiomatization of human choice as a function of innate
preferences. People choose what they prefer
How do we know what they prefer? Look at what they choose
The circularity between preferences and choice might be avoided if there
were permanency and consistency in preference-choice relationship across
diverse contexts
One could observe choice in one context, tentatively infer the preferences
from these observations, and assuming consistent preferences, predict
choice in other contexts
Unfortunately, half-a-century of research has yielded little predictability of
choice from inferred preferences across contexts (Friedman and Sunder
2004)
Individual human behavior appears to be unmanageably rowdy for scientists
to capture in a stable set of laws
While humanists may not take delight at such disappointments, but they can
hardly be surprised (if they pay any attention to choice theory)
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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?
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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
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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
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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
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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
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What Makes the Difference
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What Makes the Difference
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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
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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
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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
• Willingness among economists to abandon the
optimization principle
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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”
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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
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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)
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Thank You
Please send comments to
Shyam.sunder@yale.edu