Agent-based computational economics (ACE)is the computational study of economies modelled as evolving systems of autonomous interacting agents. This short paper is a brief guide to recent ACE research. For more information, visit the ACE Web site at http://www.econ.iastate.edu/tesfatsi/ace.htm. Resources available at the ACE Web site include surveys, an annotated syllabus of readings, software, teaching materials, pointers to research on economic and social network formation, and pointers to individual researchers and research groups.Agent-based computational economics

Leigh Tesfatsion

English

Research Papers in Economics

16 January 2000
Agent-Based Computational Economics:
A Brief Guide to the Literature
Leigh Tesfatsion
Department of Economics
Iowa State University
Ames, Iowa 50011-1070
tesfatsi@iastate.edu
http://www.econ.iastate.edu/tesfatsi/
1 Introduction
The newly developing ¯eld of agent-based computational economics (ACE) is roughly de-
¯ned by its practitioners as the computational study of economies modelled as evolving systems of
autonomous interacting agents.1 A principal concern of ACE researchers is to understand the ap-
parently spontaneous formation of global regularities in economic processes, such as the unplanned
coordination of trading activities in decentralized market economies that economists associate with
Adam Smith's invisible hand. The challenge is to explain how these global regularities arise from
the bottom up, through the repeated local interactions of autonomous agents channeled through
socioeconomic institutions, rather than from the top-down imposition of ¯ctitious coordination
mechanisms such as market clearing constraints or an assumption of single representative agents.
The study of evolutionary economics is by no means new, of course. Even before Darwin,
attempts were made to apply evolutionary ideas to socioeconomic behavior.
Although this early work is now largely ignored by economists, economic textbooks still typically
include at least some mention of the ideas of J. Schumpeter regarding the evolution of economic
institutions. Moreover, Schumpeter's work, together with the seminal work by A. A. Alchian on
uncertainty and evolution in economic systems, appears to have strongly in°uenced the subsequent
well-known work by R. Nelson, S. Winter and various of their collaborators on evolutionary theories
of economic change. In addition, one has the work of W. B. Arthur on economies incorporating
positive feedbacks, the work by Richard Day on dynamic economies characterized by complex phase
transitions, the work by J. Foster on an evolutionary approach to macroeconomics, R. Heiner's work
1For extensive on-line ACE resources, including surveys, an annotated syllabus of readings, software, and pointers
to individual researchers and research groups, see the ACE Web site at http://www.econ.iastate.edu/tesfatsi/ace.htm.
1on the origins of predictable behavior, J. Hirshleifer's work on evolutionary models in economics
and law, and U. Witt's work on economic natural selection. These and numerous other interesting
studies on evolutionary economics are reviewed by Nelson (1995).
More recently, as detailed in Samuelson (1997), a number of researchers have been focusing on
the potential economic applicability of evolutionary game theory with replicator dynamics. In these
studies, game strategies are distributed over a ¯xed number of strategy types, and the strategies
reproduce over time in direct proportion to their relative ¯tness.
Exploiting the recent advent of more powerful computational tools, such as object-oriented pro-
gramming, ACE researchers such as Arifovic (1994), Arthur et al. (1997), Axelrod (1997), Epstein
& Axtell (1996), Kirman (1997), Kollman et al. (1997), Marks (1992), McFadzean and Tesfatsion
(1999), Miller (1996), Tesfatsion (1997a,b), Vriend (1995), and Young (1998) have been able to
extend this earlier work on evolutionary economics in four key ways.
First, agents in ACE frameworks are typically modelled as heterogeneous entities that determine
their interactions with other agents and with their environment on the basis of internalized data
and behavioral rules. These agents thus tend to have a great deal more internal cognitive structure
and autonomy than conventionally modelled economic agents. Second, a broader range of agent
interactions is typically permitted in ACE frameworks, with predatory and cooperative associations
taking center stage along with price and quantity relationships. Third, the evolutionary process is
generally represented as natural selection pressures acting directly on agent characteristics rather
than as population-level laws of motion. These natural selection pressures result in the continual
creation of new modes of agent behavior and an ever-changing network of agent interactions. Fourth,
ACE frameworks are computer implemented as virtual economic worlds that grow themselves along
a real time-line, much like a culture grows in a petri dish. In principle, once initial conditions are
set, all subsequent events in these virtual economic worlds are initiated and driven by agent-agent
and agent-environment interactions; no further outside interventions by the modeler (e.g., o®-line
¯xed point calculations) are permitted.
In brief, then, ACE is a blend of concepts and tools from evolutionary economics, cognitive
science, and computer science. It represents a methodological approach that may ultimately per-
mit two important developments: (a) the rigorous testing, re¯nement, and extension of theories
developed in the earlier literature on evolutionary economics that were found to be analytically
intractable; and (b) the rigorous formulation and testing of conceptually integrated socioeconomic
2int main () f
Init(); // Construct the initial trader generation
// with random trade strategies.
For (G = 1,...,GMax)f // Enter the generation cycle loop.
// Generation Cycle:
InitGen(); // Con¯gure traders with user-supplied
// parameter values (initial expected
// utility levels, capacity quotas,...).
For (I = 1,...,IMax) f // Enter the trade cycle loop.
// Trade Cycle:
MatchTraders(); // Determine trade partners,
// given expected utilities,
// and record job search and
// inactivity costs.
Trade(); // Implement trades and
// record trade payo®s.
UpdateExp(); // Update expected utilities using
g // newly recorded costs and payo®s.
// Environment Step:
AssessFitness(); // Assess trader ¯tness scores.
Output(); // Output trader information.
// Evolution Step:
EvolveGen(); // Evolve a new trader generation.
g
Return 0;
g
Table I: Pseudo-Code for the Trade Network Game (TNG)
theories compatible with theory and data from many di®erent relevant ¯elds currently separated
by arti¯cial disciplinary boundaries.
2 Illustration: An ACE Trade Network Framework
To illustrate the ACE approach in more concrete terms, consider the Trade Network Game
(TNG) developed by Tesfatsion (1997b) and McFadzean and Tesfatsion (1999) for studying the
formation and evolution of trade networks. As depicted in Table I, the TNG consists of successive
generations of traders who choose and refuse trade partners on the basis of continually updated
expected payo®s, engage in risky trades modelled as two person games, and evolve their trade
strategies over time.
The TNG framework facilitates the computational study of markets from an agent-based per-
spective in three key ways.
First, as seen in Table I, the TNG framework is modular in design. This means that experi-
mentation with alternative speci¯cations for market structure, search and matching among traders,
trade site interactions, expectation formation and updating, and evolution of trade site strategies
3class TradeBot
f
Internalized Social Norms:
Market protocols for communicating with other traders;
Market protocols for trade partner search and matching;
Market protocols for trade interactions.
Internally Stored State Information:
My attributes;
My endowments;
My beliefs and preferences;
Addresses I have for myself and for other traders;
Additional data I have about other traders.
Internal Behavioral Rules:
My rules for gathering and processing new information;
My rules for determining my trade behavior;
My rules for updating my beliefs and preferences;
My rules for measuring my utility (¯tness) level;
My rules for modifying my rules.
g;
Table II: A TNG Trader as a Software Agent
can easily be undertaken | much like changing a lightbulb in a multi-bulb lamp | as long as
the interfaces (inputs and outputs) for the modules implementing these speci¯cations remain un-
changed. Moreover, each of these modules can potentially be grounded in trader-initiated actions
in the sense that the module is implemented via behavioral rules internal to the traders.
Second, as seen in Table II, each TNG trader is an autonomous software agent (\tradebot") with
internalized social norms, internally stored state information, and internal behavioral rules. The
traders can therefore engage in anticipatory behavior. Moreover, using stored addresses together
with internalized communication protocols, the traders can communicate with each other at event-
triggered times, a feature not present in standard economic models.
Third, the TNG permits the rigorous and routine experimental study of non-steady state mar-
ket dynamics at three di®erent levels: individual characteristics of traders; interactions among
traders (network formation); and social welfare as measured by descriptive statistics such as av-
erage trader welfare. For example, the TNG framework is currently being applied to the study of
worker-employer market power relationships in evolutionary labor markets with adaptive search;
see Tesfatsion (1999).
3 Conclusion
In summary, the hallmark of the ACE approach to the study of economic processes is a
4bottom up perspective, in the sense that global behavior is grounded in local agent interactions. The
TNG framework brie°y outlined in Section 2 illustrates how the ACE approach might be specialized
to the study of evolutionary trade networks. More generally, however, the ACE approach permits
economists to begin the di±cult task of studying the self-organizing capabilities of decentralized
market economies.
References
Arifovic, Jasmina (1994), \Genetic Algorithm Learning and the Cobweb Model," Jour-
nal of Economic Dynamics and Control 18, 3{28.
Arthur, W. Brian, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Tay-
lor (1997), \Asset Pricing Under Endogenous Expectations in an Arti¯cial Stock
Market", in The Economy as an Evolving Complex System, II, edited by W. Brian
Arthur, Steven Durlauf, and David Lane, Santa Fe Institute Studies in the Sciences
of Complexity, Proceedings Volume XXVII, Reading, MA: Addison-Wesley.
Axelrod, Robert (1997), The Complexity of Cooperation: Agent-Based Models of Com-
petition and Collaboration, Princeton: Princeton University Press.
Epstein, Joshua, and Robert Axtell (1996), Growing Arti¯cial Societies: Social Science
from the Bottom Up, Cambridge, MA: The MIT Press.
Kirman, Alan (1997), \The Economy as an Interactive System", in The Economy as an
Evolving Complex System, II, op. cit..
Kollman, Ken, John H. Miller, and Scott E. Page (1997), \Computational Political
Economy", in The Economy as an Evolving Complex System, II, op. cit..
Marks, Robert (1992), \Breeding Hybrid Strategies: Optimal Behavior for Oligopolists",
Journal of Evolutionary Economics 2, 17{38.
McFadzean, David, and Leigh Tesfatsion (1999), \A C++ Platform for the Evolution
of Trade Networks,"Computational Economics 14, 109{134.
Miller, John(1996), \The Coevolutionof Automata in the RepeatedPrisoner's Dilemma",
Journal of Economic Behavior and Organization 29 (1996), 87{112.
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bridge, MA: The MIT Press.
Tesfatsion, Leigh (1997a), \How Economists Can Get Alife", in The Economy as an
Evolving Complex System, II, op. cit..
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tion", pp. 249{269 in Computational Approaches to Economic Problems, edited by
Hans Amman, Berc Rustem, and Andrew Whinston, Dordrecht, the Netherlands:
Kluwer Academic Publishers.
Tesfatsion, Leigh (1999), \Structure, Behavior, and Market Power an in Evolutionary
Labor Market with Adaptive Search", Economic Report 51, Iowa State University,
to appear in the Journal of Economic Dynamics and Control. [A preprint of this
paper is available online at http://www.econ.iastate.edu/tesfatsi/mpevlab.ps]
Vriend, Nicolaas (1995), \Self-Organization of Markets: An Example of a Computational
Approach", Computational Economics 8, 205{231.
Young, Peyton, Individual and Social Structure: An Evolutionary Theory of Institutions,
Princeton: Princeton University Press, Princeton, 1998.
Copyright c ° 2000 Leigh Tesfatsion. All Rights Reserved.
6

A Trade Network Game with Endogenous Partner Selection&quot;,

Asset Pricing Under Endogenous Expectations in an Arti¯cial Stock Market&quot;, in The Economy as an Evolving Complex System,

Breeding Hybrid Strategies: Optimal Behavior for Oligopolists&quot;,

Computational Political Economy&quot;,

Copyright c °

Evolutionary Games and Equilibrium Selection,

Genetic Algorithm Learning and the Cobweb Model,&quot;

Growing Arti¯cial Societies: Social Science from the Bottom Up,

How Economists Can Get Alife&quot;,

Individual and Social Structure: An Evolutionary Theory of Institutions,

Recent Evolutionary Theorizing About Economic Change&quot;,

The Coevolutionof Automata in the RepeatedPrisoner's Dilemma&quot;,

The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration,

The Economy as an Interactive System&quot;,

Agent-Based Computational Economics: A Brief Guide to the Literature

http://128.118.178.162/eps/comp/papers/0004/0004001.pdf

Agent-Based Computational Economics: A Brief Guide to the Literature

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