Leigh Tesfatsion

Agent-Based Computational Economics: Growing Economies From the Bottom Up

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.

Agent-Based Computational Economics: A Constructive Approach to Economic Theory

Economies are complicated systems encompassing micro behaviors, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning, and the possibility of multiple equilibria. Recent advances in analytical and computational tools are permitting new approaches to the quantitative study of these aspects. One such approach is Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. This chapter explores the potential advantages and disadvantages of ACE for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, institutional arrangements, and behavioral dispositions.

Market power and efficiency in a computational electricity market with discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

Introduction to the special issue on agent-based computational economics

A brief overview of agent-based computational economics (ACE) is given, followed by a synopsis of the articles included in this special issue on ACE and in a companion special issue on ACE scheduled to appear in the Journal of Economic Dynamics and Control.

Agent-based computational economics: modeling economies as complex adaptive systems

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This paper outlines the main objectives and defining characteristics of the ACE methodology, and discusses several active research areas.

Preferential partner selection in an evolutionary study of Prisoner's Dilemma.

Partner selection is an important process in many social interactions, permitting individuals to decrease the risks associated with cooperation. In large populations, defectors may escape punishment by roving from partner to partner, but defectors in smaller populations risk social isolation. We investigate these possibilities for an evolutionary Prisoners Dilemma in which agents use expected payoffs to choose and refuse partners. In comparison to random or round-robin partner matching, we find that the average payoffs attained with preferential partner selection tend to be more narrowly confined to a few isolated payoff regions. Most ecologies evolve to essentially full cooperative behavior, but when agents are intolerant of defections, or when the costs of refusal and social isolation are small, we also see the emergence of wallflower ecologies in which all agents are socially isolated. Between these two extremes, we see the emergence of ecologies whose agents tend to engage in a small number of defections followed by cooperation thereafter. The latter ecologies exhibit a plethora of interesting social interaction patterns.

Time-varying linear regression via flexible least squares☆

Abstract Suppose noisy observations obtained on a process are assumed to have been generated by a linear regression model with coefficients which evolve only slowly over time, if at all. Do the estimated time-paths for the coefficients display any systematic time-variation, or is time-constancy a reasonably satisfactory approximation? A “flexible least squares” (FLS) solution is proposed for this problem, consisting of all coefficient sequence estimates which yield vector-minimal sums of squared residual measurement and dynamic errors conditional on the given observations. A procedure with FORTRAN implementation is developed for the exact sequential updating of the FLS estimates as the process length increases and new observations are obtained. Simulation experiments demonstrating the ability of FLS to track linear, quadratic, sinusoidal, and regime shift motions in the true coefficients, despite noisy observations, are reported. An empirical money demand application is also summarized.

Structure, behavior, and market power in an evolutionary labor market with adaptive search ☆

This study uses an agent-based computational labor market framework to experimentally study the relationship between job capacity, job concentration, and market power. Job capacity is measured by the ratio of potential job openings to potential work offers, and job concentration is measured by the ratio of work suppliers to employers. For each experimental treatment, work suppliers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modelled as prisoners dilemma games, and evolve their worksite behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power.

An agent-based computational laboratory for testing the economic reliability of wholesale power market designs

In April 2003 the U.S. Federal Energy Regulatory Commission proposed the wholesale power market platform (WPMP) for adoption by all U.S. wholesale power markets. The WPMP market design envisions day-ahead, real-time, and ancillary service markets maintained and operated by an independent system operator or regional transmission organization. Previous work reports on the development of an agent-based model for testing the economic reliability of the WPMP market design. This paper reports on the implementation of this model as an agent-based computational laboratory. Initial experiments focusing on optimal power flow solution methods for the day-ahead and real-time markets are also discussed.

A C++ Platform for the Evolution of Trade Networks

This paper presents a general C++ platform for the implementation of a trade network game (TNG) that combines evolutionary game play with preferential partner selection. In the TNG, successive generations of resource constrained traders choose and refuse trade partners on the basis of continually updated expected payoffs, engage in risky trades modelled as two-person games, and evolve their trade strategies over time. The modular design of the TNG platform facilitates experimentation with alternative specifications for market structure, trade partner matching, trading, expectation formation, and trade strategy evolution. The TNG platform can be used to study the evolutionary implications of these specifications at three different levels: individual trader attributes, trade network formation, and social welfare.