Agent-Based Computational Economics (ACE)
Research Area: Business and Management

Last Updated: 26 January 2008

Original Site Development: Peer-Olaf Siebers Site Maintained By: Leigh Tesfatsion Department of Economics Iowa State University Ames, Iowa 50011-1070 http://www.econ.iastate.edu/tesfatsi/ tesfatsi AT iastate.edu ACE Website Online ACE Course (Self-Study eBook)

Table of Contents: Basic Issues Tutorials and Other Introductory Materials Readings Software, Toolkits, and Demos Resource Sites, Groups, and Individual Researchers

Basic Issues

• Impact of marketing strategies on consumer behaviour
• Impact of management practices on store productivity
• Comparing different marketing strategies for multi-channel (physical and electronic) stores

Tutorials and Other Introductory Materials

• Eric D. Beinhocker, Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics, Harvard Business School Press, 527pp., June 2006.
  Abstract: "Accounting for the creation of wealth has long challenged humanity's best minds. For business readers and academics, Beinhocker is a zealous and able guide to the emerging economic paradigm shift he calls the `Complexity Economics Revolution.' A fellow of the economic think tank McKinsey Global Institute, he rejects traditional economic theory, based on a physics model of closed systems, in which change is an external disruptive shock. Instead, he outlines an open,adaptive system with interlocking networks that change organically, reflecting the interaction of technological innovation, social development and business practice. Wealth is created to the degree that this interaction decreases entropy in favor of `fit order'that meets human needs, desires, and preferences."

  Michael J. North and Charles M. Macal, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford University Press, 2007, 328pp.

  Abstract: "Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. ... (This book) addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models."

  Michael J. North, MBA, Ph.D., and Charles M. Macal, Ph.D., P.E., are Deputy Directory and Director, respectively, of the Center for Complex Adaptive Agent Systems Simulation within the Decision and Information Sciences Division of Argonne and the University of Chicago.

  Leigh Tesfatsion, Agent-Based Computational Economics: A Constructive Approach to Economic Theory (pdf preprint,253K), in Leigh Tesfatsion and Kenneth L. Judd (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Table of Contents), Handbooks in Economics Series, North-Holland/Elsevier, the Netherlands, Spring 2006, to appear.

  Abstract: 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, behavior, and institutional arrangements.

Readings

• Eric Bonabeau, "Predicting the Unpredictable," Harvard Business Review, 2002. (pdf)
  Abstract: "The collective behaviour of people in crowds, markets, and organisations has long been a mystery. The author argues that now some companies are finding ways to analyze, and even foretell, such `emergent phenomena'."

  C. Buchta and J. Mazanec, "SIMSEG/ACM – A Simulation Environment for Artificial Consumer Markets," Working Paper Nr. 79, Vienna University of Economics, May 2001. (pdf)

  Abstract: "The Artificial Consumer Market (ACM) is part of the integrated simulation endeavor named the “Artificial Economy”. Complementing and extending the concepts developed in the SIMSEG simulation environment of Working Paper No. 60, this report proceeds in two steps: (1) it outlines the basic constructs and consumer behavior phenomena implemented in the ACM in a nontechnical manner; and (2) it elaborates the formal structure and relationships in full detail."

  B. Csik, "Simulation of Competitive Market Situations Using Intelligent Agents," Periodica Polytechnica Ser. Soc. Man. Sci, 11(1), 2003, pp. 83-93. (pdf preprint)

  Abstract: "This paper describes a way to use artificial-intelligence controlled simulation (AICS) methodology in the simulation of various marketing problems. The paper...explores the potential challenges in the construction of market models, describes a guideline for the solution, and points out aspects where the AICS methodology can be applied. Special (attention is paid) to the intelligent demon controlled simulation, especially the CASSANDRA system."

  V. Koritorov, "Real-World Market Representation with Agents," IEEE Power and Energy Magazine, 2004, pp. 39-46. (pdf preprint)

  Abstract: "The electric power industry around the world is undergoing an extensive restructuring process. In many countries the traditional vertically integrated power utilities are being unbundled and replaced with a number of separate business entities dealing with the generation, transmission, and distribution of electric power. One of the most significant features of the restructuring process is the introduction of electricity markets, aimed at providing competitive electricity service to consumers. As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial, and regulatory environments in which they operate. (This paper argues that a) new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation approach."

  M. A. Janssen and W. Jager, "Fashions, Habits and Changing Preferences: Simulation of Psychological Factors Affecting Market Dynamics," Journal of Economic Psychology, 22(6), 2001, pp. 745-772. (ScienceDirect) (requires subscription)

  Abstract: "Markets can show different types of dynamics, from quiet markets dominated by one or few products, to markets with constant penetration of new and reintroduced products. This paper explores the dynamics of markets from a psychological perspective using a multi-agent simulation model."

  Maite López-Sánchez, Noria Maite, Juan A. Rodríguez, and Nigel Gilbert, "Multi-Agent Based Simulation of News Digital Markets," International Journal of Computer Science & Applications, 2005. (pdf preprint)

  Abstract: "Over the past few years it has become clear that the Internet will play an ever greater role in the distribution of digital contents. Businesses have to start now understanding the dynamics of this new market and gaining insight into how to exploit the impending paradigm shift in contents, marketing, and distribution. Our main aim is to provide businesses in the digital contents sector with a tool which will enable them to take informed business strategy decisions and become more competitive by adapting their traditional business models to the new, demanding reality. To achieve this objective, we have implemented a first version of a news market model called SimwebAB that is based on multi-agent simulation technology."

  V. Petrushin, "eShopper Modeling and Simulation," Proceedings, SPIE 2001 Conference on Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, 2001, pp. 75-83. (pdf preprint)

  Abstract: "The advent of e-commerce gives an opportunity to shift the paradigm of customer communication into a highly interactive mode. The new generation of commercial Web servers, such as the Blue Martini’s server, combines the collection of data on customer behavior with real-time processing and dynamic tailoring of a feedback page. The new opportunities for direct product marketing and cross selling are arriving. The key problem is what kind of information do we need to achieve these goals, or in other words, how do we model the customer? This paper (focuses on) modeling an individual customer...based on the customer’s transaction data, click stream data, and demographics. ...(The model) is used for predicting the date of the next visit, overall spending, and spending for different types of products and brands."

  Jean-Philippe Rennard, Ed., Handbook of Research on Nature-Inspired Computing for Economics and Management, Two-Volume Set, Idea Group Inc., September 2006, 989pp.

  Abstract: "(This book) is the original, comprehensive reference work on research and applications of nature-inspired computing to economics and management. ... Gathering the work of over 100 internationally known contributors, this two-volume set explores how complexities found in nature can be modeled to simulate and optimize business situations. It provides practitioners a global view of the current and future applications of this ground-breaking technology, and also includes more than 1,900 references to existing literature in the field."

  L. B. Said, T. Bouron, and A. Drogoul, A., "Agent-based Interaction Analysis of Consumer Behavior", Proceedings, First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, Bologna, Italy, 2002. (ACM Portal) (requires subscription)

  Abstract: "Our goal is to create a virtual consumer population that can be used for simulating the effects of marketing strategies in a competing market context. ...This paper proposes a consumer behavioral model based on a set of behavioral primitives such as imitation, conditioning and innovativeness, which are founded on the new concept of behavioral attitude. It shows that this model provides an interpretation of the main concepts and cognitive features issuing from marketing research and psycho-sociology works on consumption. The paper presents also the CUstomer BEhavior Simulator (CUBES), which has been realized for implementing the customer model and leading multi-agents simulations. (G)enetic algorithms (GA), in addition to multi-agent systems, are used to fit the characteristics of the virtual consumer population (to achieve) global realistic market behavior."

  V. Saggau,"Agent-Based Modelling for Investigating Consumer Behaviour in Risky Markets: The Case of Food Scares," Dissertation, University of Kiel, 2005. (pdf)

  Abstract: "Agent-based modelling is a relatively new research method which offers rich possibilities for consumer research. In this thesis a multi-agent simulation was implemented for investigating consumer behaviour in case of food scares. The influence of information regarding the safety of food released by the media is in the centre of the investigations. An artificial consumer population receives (and discusses) this information...within their networks. Based on internal learning mechanisms in the sense of Bayesian updating each consumer agent revises its trust with respect to the safety of the food item under investigation. Different information scenarios are tested where the outcomes served as measures of effectiveness. In this way it was possible to test the influence of different information strategies regarding the trust recovery concerning the safety of the food item under investigation."

  Stephan Schuster and Nigel Gilbert, "Simulating online business models,", in H. Coleho, B. Espinasse, and M.-M. Seidel (Eds.), Proceedings, 5th Workshop on Agent-Based Simulation, Society for Modeling and Simulation International, Lisbon, Portugal, 2004, pp. 55-61. (pdf preprint)

  Abstract: "The online content market for news and music is changing rapidly with the spread of technology and innovative business models (e.g. the online delivery of music, specialised subscription news services). It is correspondingly hard for suppliers of online content to anticipate developments and the effects of their businesses. This paper describes a prototype multiagent simulation to model possible scenarios in this market. The simulation is intended for use by business strategists and has been developed using a participatory, rapid prototyping methodology. The implications of the method and the characteristics of the domain for the design are considered."

  A. Schwaiger and B. Stahmer, "SimMarket: Multiagent-Based Customer Simulation and Decision Support for Category Management," in Lecture Notes in Computer Science, 2003, pp. 74-84. (pdf, SpringerLink) (requires subscription)

  Abstract: "A key to an optimal assortment of goods and pricing of individual items in a store is the knowledge about potential customer’s behaviour. In this paper we present the simulation of individual customers based on a multiagent system which models the important elements and external influences as single agents. An agent can be a member of several agent groups, which are represented as holons. We model each individual customer as an agent that behaves according to the customer’s individual preferences. These preferences are extracted from real world data, such as customer cards, sales data and interviews. The customer’s shopping behaviour is represented in behaviour networks (Bayesian nets) which are stored in the customer agents’ knowledge bases. The behaviour of a representative group of customers induces the overall sales figures, which support decisions what to sell at which price. The presented concepts are based on ideas of Joachim Hertel from DACOS and Jörg Siekmann from the DFKI. They are implemented as a prototype, which provides, after further evaluation, the basis for a new and final system to be used by retailers."

  P.O. Siebers, U. Aickelin, H. Celia, and C. Clegg, "A Multi-Agent Simulation of Retail Management Practices" (pdf,466), Proceedings, 2007 Summer Computer Simulation Conference, pp. 959-966, San Diego, CA, July 15-18, 2007.

  Abstract: "We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multidisciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development."

  P. Twomey and R. Cadman, "Agent-Based Modelling of Customer Behaviour in the Telecoms and Media Markets,", Information, 4(1), 2002, pp. 56-63. (pdf preprint)

  Abstract: "Agent-based modelling is a bottom-up approach to understanding systems which provides a powerful tool for analysing complex, non-linear markets. The method involves creating artificial agents designed to mimic the attributes and behaviours of their real-world counterparts. The system’s macro-observable properties emerge as a consequence of these attributes and behaviours and the interactions between them. The simulation output may be potentially used for explanatory, exploratory and predictive purposes. 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."

  Ian Wilkinson, Business Relating Business: Managing Organisational Relations and Networks, Edward Elgar Publishing, May 2008.

  Publisher's Abstract: "(This book) argues that business performance depends on the way a firm is connected to other firms and organisations and not just its own skill and resources. The book synthesises thinking from marketing, management, economics and international business with evolutionary biology and complexity theory, as well as integrating many years research on interfirm relations and networks. It develops the management and policy implications of adopting relationship and network perspectives and sets out an agenda for future research."

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 Demonstration Software

• AnyLogic is a commercial simulation tool that supports process-centric (also called discrete event), system dynamics, and agent-based modeling approaches. According to the developers, the unique flexibility of the modeling language enables the user to capture the complexity and heterogeneity of business, economy and social systems at any desirable level of detail. The AnyLogic set of primitives and library objects allows the user to model manufacturing and logistics, business processes, human resources, consumers' and patients' behavior, as well as the environment (the "background") in their natural interaction. The object-oriented model design paradigm supported by AnyLogic provides for modular and incremental construction of large models. More detailed information about AnyLogic, including documentation, utilities, and pointers to business research papers implemented with AnyLogic, can be found at the AnyLogic home page (above).

• MarketSim is a commercial tool that creates real-world simulations of consumer behaviors. It combines relevant market, customer, and competitive data into an interactive reusable model that captures a market's "DNA."

• ShopSim is a commercial decision-support tool for retail and shopping centre management. It evaluates the shop mix attractiveness and pedestrian friendly design of a shopping centre. The software uses an agent-based approach, where the behaviour of agents depends on poll data.

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

Research Groups and Individual Researchers

• ACE-Related Research Groups

• Argonne National Laboratory (Argonne, Illinois), the Santa Fe Institute, and the University of Chicago annually host a course titled Capturing Business Complexity with Agent-Based Modeling (ABM) and Simulation. This short course (typically six days) offers an intensive introduction to agent-based modeling and simulation with a focus on business applications. The course is divided into two sessions. The first stresses ABM concepts from the perspective of company managers and analysts and has no prerequisites. The second focuses on ABM implementation from the perspective of company software developers and includes extensive hands-on exercises. Prerequisites for the second session are a good knowledge of general ABM concepts and basic familiarity with a high-level programming language.

• Eric Bonabeau (Management, Icosystem Inc., Cambridge, MA): Complex systems and distributed adaptive problem solving; Information science; Telecommunications.

• Bo Carlsson (Economics, Case Western Reserve University, Cleveland, Ohio): Industrial dynamics; Role of entrepreneurship in economic growth; Clustering of economic activity in industries and regions (international comparison); Intellectual property management; Innovation systems.

• Nigel Gilbert (Sociology, University of Surrey,Guildford, UK): Computer simulation; Sociology of science and science policy; Innovation; Consumer behaviour; Sociology of the environment.

• Wander Jager (Marketing, University of Groningen, the Netherlands): Social simulation and behavioural dynamics; Consumer behaviour and decision-making; Sustainable behaviour.

• Stan Metcalfe, (CRIC - Centre for Research on Innovation and Competition, University of Manchester, UK): Evolutionary economics and the modeling of evolutionary processes in relation to innovation, competition, and economic growth.

• Bart Nooteboom (Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands): Organizational dynamics; Agent-based transaction cost economics; New institutional economics.

• Peer-Olaf Siebers (School of Computer Science, ASAP Group, University of Nottingham, UK): Application of agent-based modeling and simulation to study human-centric complex adaptive systems.

• Ian F. Wilkinson (School of Marketing, University of New South Wales, Australia): Evolution of institutional and network structures; Structural dynamics of industrial networks; the Kauffman NK model.

Copyright © 2007 Leigh Tesfatsion. All Rights Reserved.