ACE Research Area:
Agent-Based Industrial Organization

Last Updated: 16 January 2023

Site Maintained By: Leigh Tesfatsion Research Professor & Professor Emerita of Economics Courtesy Research Professor of Electrical & Computer Engineering Heady Hall 260 Iowa State University Ames, Iowa 50011-1054 https://www2.econ.iastate.edu/tesfatsi/ tesfatsi AT iastate.edu Online ACE Course (Self-Study eBook) ACE Website

Table of Contents: Basic Issues Surveys and Overviews Sample Research Software, Toolkits, and Demos Resource Sites, Groups, and Some Early Individual Researchers

Basic Issues

• How are goods produced in real-world markets? How are prices determined in real-world markets?
• Taking agent autonomy seriously in market modeling
• Agent-based monopoly, duopoly, and oligopoly studies
• Implications of market design for market performance
• Integration and coordination of market processes (production, pricing, and distribution)
• Modeling the co-evolution of market behaviors and market institutions

Surveys and Overviews

• Myong-Hun Chang, A Computational Model of Industry Dynamics, Advances in Experimental and Computable Economics Book Series, Routledge, 2015.

  Abstract: "This book endeavours to explain many well-documented aspects of the evolution of industries over time. It uses an agent-baed computational model in which artificial industries are created and grown to maturity in silico. ... (The model) is able to replicate many of the stylized facts from the empirical industrial organization literature, particularly as the facts pertain to the dynamics of firm entry and exit."

• Myong-Hun Chang, "Agent-Based Modeling and Computational Experiments in Industrial Organization: Growing Firms and Industries in silico" (pdf,88KB), Eastern Economic Journal 37 (2011), 28-34.

  Abstract: This paper discusses the need for, the mechanics of, and some potential application of agent-based modeling and computational analysis in industrial organization.

• Robert Clower and Peter Howitt, "Taking Markets Seriously: Groundwork for a Post Walrasian Macroeconomics", Chapter 2 (pp. 21-37) in David Colander (ed.), Beyond Microfoundations: Post Walrasian Macroeconomics, Cambridge University Press, Cambridge, UK, 1996.

• Herbert Dawid, "Agent-Based Models of Innovation and Technological Change", in Leigh Tesfatsion and Kenneth L. Judd (editors), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.

  Abstract: This chapter discusses the potential of the agent-based computational economics approach for the analysis of processes of innovation and technological change. It is argued that, on the one hand, several genuine properties of innovation processes make the possibilities offered by agent-based modelling particularly appealing in this field, and that, on the other hand, agent-based models have been quite successful in explaining sets of empirical stylized facts, which are not well accounted for by existing representative-agent equilibrium models. An extensive survey of agent-based computational research dealing with issues of innovation and technological change is given and the contribution of these studies is discussed. Furthermore a few pointers towards potential directions of future research are given.

• Guido Fioretti, "Agent-Based Models of Industrial Clusters and Districts" (pdf,171KB), in Albert Tavidze (ed.), Progress in Economics Research, Vol. IX Chapter VIII, 2006, pp. 125-142.

  Abstract: "Agent-based models, an instance of a wider class of connectionist models, allow bottom-up simulations of organizations constituted by a large number of interacting parts. Thus, geographical clusters of competing or complementary firms constitute an obvious field of application. This contribution explains what agent-based models are, reviews applications in the field of industrial clusters, and focuses on a simulator of infra- and inter-firm communication."

• John McMillan, Reinventing the Bazaar: A Natural History of Markets, W. W. Norton & Co., 2002.

  Abstract: "New ideas in economics, and some old ones, are used in the chapters that follow to dissect exotic, innovative, and everyday marketplaces -- some in physical space, others in cyberspace. How do markets work? What can they do? What can't they do? These are the questions I will address."

• John Sutton, Market Structure: Theory and Evidence (pdf,856KB), Latest Revision (2/09 download): February 2006.

  Abstract: Topics covered in this survey include: Introduction; Cross-Industry Literature; Size Distribution Literature; Dynamics of Market Structure; Caveats and Controversies; and Unanswered Questions and Current Research.

• Leigh Tesfatsion, "Testing Institutional Designs via Agent-Based Modeling: A U.S. Electricity Market Example" (pdf,2.5MB), Colloquium Talk, WICI, University of Waterloo, March 22, 2010.

• Paul Twomey and Richard Cadman, "Agent-Based Modelling of Customer Behavior in the Telecoms and Media Markets" (pdf,141KB), Info, Vol. 4(1), 2002, pp. 56-63.

  Abstract: The aim of this paper is to introduce the reader to some of the basic concepts and methods behind agent-based modelling and to present some recent business applications of these tools, including work in the telecoms and media markets.

Sample Research

• Gary H. Anthes, "Agents of Change: Software Agents Tame Supply Chain Complexity and Optimize Performance" (html,Use Quicklink 35605 Search) ComputerWorld, January 27, 2003.

  Abstract: This news item relates how Proctor and Gamble's use of agent-based modeling helped them fundamentally (and profitably) transform their supply chain system connecting over 5 billion consumers in 140 countries into a "supply network."

• Robert Axtell, "Team Dynamics and the Empirical Structure of U.S. Firms" (pdf,3.7MB), Working Paper, Department of Computational Social Science, George Mason University, Fairfax, VA, 2013.

  Abstract: A model in which purposive agents self-organize into teams is demonstrated to closely reproduce empirical data on the population of U.S. firms. There are increasing returns within teams and agents move between teams or start new teams when it is in their self-interest. Nash equilibria of the team formation game exist but are unstable. Dynamics are studied using agent-based computing at full-scale with the U.S. private sector (120 million agents).

• Robert Axtell, "Zipf Distribution of U.S. Firm Sizes" (pdf,143KB), Science, 293, 2001, 1818-1820.

  Abstract: "Firm sizes have typically been described by lognormal distributions, with Pareto, Yule and related distributions accurately capturing the upper (large size) tail. Utilizing data on the entire population of U.S. firms, including small businesses, we find that the Pareto distribution well describes the entire firm size distribution. Furthermore, the exponent of this distribution is essentially unity, thus we have the special case of the Zipf distribution. Data on self-employment, not normally included in small firm data, are consistent with the Zipf characterization. These results are shown to be robust to alternative definitions of firm size."

• Jason Barr and Francesco Saraceno, "A Computational Theory of the Firm" (pdf,315KB), Journal of Economic Behavior and Organization 49 (2002), 345-361.

  Abstract: "This paper proposes using computational learning theory (CLT) as a framework for analyzing the information processing behavior of firms; we argue that firms can be viewed as learning algorithms. The costs and benefits of processing information are linked to the structure of the firm and its relationship with the environment. We model the firm as a type of artificial neural network (ANN). By a simulation experiment, we show which types of networks maximize the net return to computation given different environments."

• Jason Barr and Francesco Saraceno, "Cournot Competition, Organization, and Learning" (pdf,296KB), Journal of Economic Dynamics and Control 29 (2005), 277-295.

  Abstract: "We model firms' output decisions in a repeated duopoly framework focusing on three related issues: (1) the role of learning in the adjustment process toward equilibrium; (2) the role of organization structure in organizational decision making; and (3) the role of changing environmental conditions on learning and output decisions."

• Jason Barr and Francesco Saraceno, "Organization, Learning, and Cooperation" (pdf,379KB), Newark Working Paper No. 2004-001, Rutgers University, March 2004, to appear in the Journal of Economic Behavior and Organization.

  Abstract: "We model the organization of the firm as a type of artificial neural network in a duopoly framework. The firms plays a repeated Prisoner's Dilemma type game, but also must learn to map environmental signals to demand parameters. We study the prospects for cooperation given the need for the firm to learn the environment and its rival's output. We show how a firm's profit and cooperation rates are affected by its size, its rival's size and willingness to cooperate, and environmental complexity."

• Lucio Biggiero and Enrico Sevi, "Opportunism by Cheating and its Effects on Industry Profitability: The CIOPS Model" (pdf,1.1MB), Comput. Math. Organization Theory 15 (2009), 191-236.

  Abstract: The CIOPS (Cognitive Inter-organizational Production SysteMB) model is an agent-based model that integrates industry structural aspects and agents’ cognitive characteristics. A demand-driven industry, whose profitability depends on the quality of suppliers’ products, is represented by a three-stage vertically integrated industry. Four types of decision-making methods are analyzed and compared ranging from random choice to a complex method involving direct and indirect experience and reputation aspects.

• Myong-Hun Chang, "Industry Dynamics with Knowledge-Based Competition: A Computational Study of Entry and Exit Patterns" (pdf,1.4MB), Journal of Economic Interaction and Coordination, 4 (2009), 73-114.

  Abstract: This study develops a computational model of industry evolution capable of matching many stylized facts. It views the firm as a myopic but adaptive entity whose survival depends on its ability to perform various activities with greater efficiency than its rivals. In this model, the shakeout pattern arises naturally in the early stage of industrial development. The author provides a full comparative dynamics analysis of how various industry-specific factors determine the numbers and the rates of entries and exits over time as well as the ages of the exiting firms.

• Myong-Hun Chang, "Entry, Exit, and the Endogenous Market Structure in Technologically Turbulent Industries" (pdf,538KB), Eastern Economic Journal 37 (2011), 51-84

  Abstract: "Empirical studies have found high correlation between entry and exit across industries, indicating that industries differ substantially in their degree of firm turnover. I propose a computational model of dynamic oligopoly with entry and exit in a turbulent technological environment. I examine how industry-specific factors give rise to across industries differences in turnover. An analysis of the endogenous relationships between firm turnover,industry concentration, and the performance variables shows: 1) the rate of turnover and industry concentration are positively related; 2) industry concentration and market price are positively related; 3) no general relationship exists between industry concentration and price-cost margin."

• Shu-Heng Chen and Bin-Tzong Chie, "Agent-Based Simulation of Product Innovation: Modularity, Complexity, and Diversity" (pdf,446KB), Proceedings, Agent 2007 Conference on Complex Interaction and Social Emergence (Agent 2007), Northwestern University, Evanston, Illinois, Nov. 15-17, 2007, pp. 295-305.

  Abstract: This work is a continuation of earlier work by the authors in which they develop an agent-based model to simulate the evolution of product innovation. The earlier work introduced a new representation of commodities, production processes, and preferences via the use of genetic programming (GP). However, in this earlier work the authors only considered a simple version of genetic programming that could not suitably express functional modularities. The result was that their simulated economy only rarely advanced to a mature state where consumers’ desires could be met to a sophisticated degree. In this paper the authors remedy this problem by replacing simple GP with automatically defined terminals (ADTs), which are very similar in spirit to the automatically defined functions (ADFs) invented by John Koza. The authors then demonstrate how their approach permits them to model product innovation as the incremental development of products "from the bottom up".

• Evan J. D. Gee, Agent-Based Modeling of Non-Walrasian Markets with Entrance and Exit of Agents (pdf,597KB), Work Submitted in Fulfillment of Requirements for a Degree with Honor, Williams College, Spring 2004.

  Abstract: The author uses agent-based modeling to explore not only the effects of bounded rationality but also the effects of asymmetric information on markets with entry and exit of agents. An example of such a market would be the housing market in a college town filled with students looking for housing and landlords willing to provide it. The author finds that even small departures from perfect information cause large changes in both the total surplus and the distribution of that surplus. Given the magnitudes of these effects, he conjectures that very few actual markets are likely to have outcomes anywhere near the competitive equilibrium.

• Peter Howitt and Robert Clower, "The Emergence of Economic Organization" (pdf,947KB), Journal of Economic Behavior and Organization, Vol. 41, No. 1, January 2000, 55-84.

  Abstract: "This paper studies the mechanism by which exchange activities are coordinated in a decentralized market economy. Our topic is not amenable to conventional equilibrium theory, which assumes that exchanges plans are coordinated perfectly by an external agent (usually unspecified but sometimes referred to as `the auctioneer') with no identifiable real-world counterpart. In contrast, we depict transactors as acting on the basis of trial and error rather than pre-reconciled calculation, and we start by noting that in reality most transactions are coordinated by an easily identified set of agents; namely, specialist trading enterprises."

• Erich Kutschinski, Thomas Uthmann, and Daniel Polani, "Learning Competitive Pricing Strategies by Multi-Agent Reinforcement Learning", Journal of Economic Dynamics and Control 27(11-12), September 2003, pp. 2207-2218, available from Science Direct.

  Abstract: The authors study several adaptive pricing strategies and learning behaviors in a co-learning market scenario with different levels of competition."

• Jean Pierre Nadal, Denis Phan, Mirta B. Gordan, and Jean Vannimenus, "Monopoly Market with Externality: An Analysis with Statistical Physics and ACE" (html), Economics Working Paper Archive, Washington University at St. Louis, 2002.

  Abstract: "In this paper we explore the effects of localised externalities introduced through interaction structures upon the properties of the simplest market model: the discrete choice model with a single homogeneous product and a single seller (the monopoly case)."

• Junjie Sun and Leigh Tesfatsion, "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework" [ (pdf,2.2MB), (ppt,3.86MB)], Computational Economics, Volume 30, Number 3, 2007, pp. 291-327. This article is an abridged version of ISU Economics Working Paper No. 06025

  Abstract: In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) - for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and adopted for implementation in California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration.

• Katarzyna Sznajd-Weron and Rafal Weron, "How Effective is Advertising in Duopoly Markets?" (html), Economics Working Paper Archive at Washington University, St. Louis, 2003. (Also published in Physica A 324 (2003), 437-444.)

  Abstract: "A simple Ising spin model which can describe the mechanism of advertising in a duopoly model is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes the spread of opinions among customers. It is shown via standard Monte Carlo simulations that very simple rules and inclusion of an external field -- an advertising campaign -- lead to phase transitions, i.e., extreme and fast changes in market share."

• Xavier Vila and Francesc Rocher, "A Note on Agent-Based Imperfect Competition" (html), Presented at the First World Congress of the Game Theory Society (Games 2000), July 24-28, 2000.

  Abstract: "The model we discuss in this note is a re-examination of the classical Bertrand model of imperfect competition. The main difference is that consumers are allowed to have some strategic behavior when deciding which one of the two sellers they will buy from."

• Junfu Zhang, "Growing Silicon Valley on a Landscape", Journal of Evolutionary Economics 13 (2003), 529-548.

  Abstract: "We propose a Nelson-Winter model with an explicitly defined landscape to study the formation of high-tech industrial clusters such as those in Silicon Valley. ... We argue that the emergence of clusters can be explained by the social effect through which the appearance of one or a few entrepreneurs inspires many followers locally. Agent-based simulation is employed to show the dynamics of the model. Data from the simulation and the properties of the model are discussed in light of empirical regularities."

Software, Toolkits, and Demos

• Annotated list of pointers to ACE/CAS General Software and Toolkits

• Annotated list of pointers to ACE/CAS Computational Laboratories and Demonstratino Software

• Peter Haddawy, Khaimook Dhananaiyapergse, Yongyos Kaewpitakkun, and Thai Bui, "Data-Driven Agent-Based Simulation of Commercial Barter Trade" (pdf,271KB), Report, CSIM Program, Asian Institute of Technology, downloaded 5/12/05.

  Abstract: This paper presents TRADES, a data-driven agent-based simulator for barter trade exchanges. This simulator is built by learning proabilistic models of company purchase behavior using transaction history data from an operating trade exchange. The authors evaluate the accuracy of their simulator by comparing simulated trade to the transaction data, showing a high degree of agreement between the two. They also use the simulator to evaluate the effectiveness of a particular trade brokering strategy.

• The Alliance for Innovative Manufacturing (AIMB) at Stanford University maintains an interesting site titled How Everyday Things Are Made (html).

  The site provides manufacturing video (virtual factory tours) covering the manufacturing processes for over forty types of common products (cars, planes, chocolate, glass bottles, etc.). These videos stress the extraordinary degree of coordination among input suppliers, producers, and distributors required to bring to market even seemingly simple products such as a jelly bean.

Resource Sites, Groups, and Some Early Individual Researchers

• ACE/CAS Research Sites and Groups

• Workshop on Agent-Based Models of Market Dynamics and Consumer Behavior

• Myong-Hun Chang (Economics, Cleveland State University, Ohio): Industrial organization; Computational organization theory; Multi-agent adaptive systems; Applied game theory.

  Herbert Dawid (Economics, University of Bielefeld, Germany): Economics of innovation; Industry Dynamics, Market Design, Agent-based Computational Economics , Evolutionary Game Theory.

• Wander Jager (University of Groningen, the Netherlands): Social simulation of consumer behaviour and market dynamics; Sustainable consumption and diffusion of sustainable technology; Network effects on market dynamics.

• Marco A. Janssen (School of Human Evolution and Social Change, Arizona State University, Tempe, AZ): The consumat approach (multi-agent modeling of consumer behavior).

• Bart Nooteboom (Economics and Business, Tilburg University, the Netherlands): Organizational dynamics; Agent-based transaction cost economics; New institutional economics.

• Andreas Pyka (Economics, University of Augsburg, Germany): Decay innovation theory; Evolutionary economics; Schumpeterian economics; Innovation networks; Innovation and employment.

• Leigh Tesfatsion (Economics, Iowa State University, Ames, Iowa): Electricity restructuring; Multi-market coordination with learning traders; Modeling decentralized market economies as distributed local-interaction systems; Construction of computational laboratories for the systematic study of decentralized market processes.

• Gérard Weisbuch (Département de Physique, Ecole Normale Supérieure, Paris): Complex system dynamics; Bounded rationality and socio-economic institutions; Market organization; Information contagion; Sustainable development; Mutualism.

• Ian F. Wilkinson (Discipline of Marketing, The University of Sydney Business School, University of Sydney, NSW, Australia): Evolution of institutional and network structures; Structural dynamics of industrial networks; the Kauffman NK model.

• Ulrich Witt (Evolutionary Economics Unit, Max Planck Institute for Research into Economic Systems, Jena, Germany): Evolutionary economics; Economic behaviour, cognition, and social learning; Institutions and public choice; Market process and industry dynamics; Long-term economic development and growth; Austrian approach to economics.

• Murat Yildizoglu (Université Montesquieu Bordeaux IV, Pessac, France): Evolutionary modelling and economic dynamics; Industry dynamics; Economics of innovation; Economic growth; Industrial organization; Decision theory and the theory of the firm.

Copyright © Leigh Tesfatsion. All Rights Reserved.