ACE Research Area:
Business and Management Systems

Last Updated: 25 February 2024

Site Primarily Developed By: Peer-Olaf Siebers Ian F. Wilkinson Site Hosted By: Leigh Tesfatsion Professor Emerita of Economics Courtesy Research Professor of    Electrical & Computer Engineering Heady Hall 260 Iowa State University Ames, Iowa 50011-1054 https://faculty.sites.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 Some Early Individual Researchers

Site Disclaimer:

Unfortunately, links to many excellent on-line business and management resource sites originally posted here are now broken and had to be removed. Below are annotated pointers to remaining still-available online resources (plus journal-published materials) that visitors might find useful for their work or of historical interest.

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

• Agent-Based Modeling and Simulation for Business and Marketing

  In July 2011 the University of Sydney offered an intensive three-week summer course on agent-based modeling and simulation for business and marketing. Designed for advanced graduate students and faculty, the course combined lectures with hands-on experience in lab sessions, building simulations of business and social phenomena. The intention is to offer this course on a regular basis each summer. For further details about future summer courses, visit the course website.

• 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."

• Eric Bonabeau, "Agent-based modeling: Methods and techniques for simulating human systems" (pdf,265KB), Proceedings of the National Academy of Sciences, Supplement 3, May 14, 2002, 7280-7287.

  Abstract: "Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed."

• 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.

• William Rand and Roland T. Rust, "Agent-Based Modeling in Marketing: Guidelines for Rigor" (pdf preprint,1.4MB) International Journal of Research in Marketing 28 (2011), doi: 10.1016/j.ijresmar.2011.04.002

  Abstract: "Agent-based modeling can illuminate how complex marketing phenomena emerge from simple decision rules. Marketing phenomena that are too complex for conventional analytical or empirical approaches can often be modeled using this approach. ... Some useful examples of agent-based modeling have been published in marketing journals, but widespread acceptance ... has been slowed by the lack of widely accepted standards of how to do agent-based modeling rigorously. We address this need by proposing guidelines for rigorous agent-based modeling. We demonstrate these guidelines, and the value of agent-based modeling for marketing research, through the use of an example ... the Bass model of the diffusion of innovations."

• Peer-Olaf Siebers and Ian Wilkinson have guest-edits a special issue for the International Journal of Innovation and Technology Management titled "Multi-Agent Simulation as a Novel Decision Support Tool for Innovation and Technology Management", which reports on some real-world business and management applications of ABM. The lead editorial for this special issue is available here.

• Leigh Tesfatsion, "Agent-Based Computational Economics: A Constructive Approach to Economic Theory" (pdf preprint,253KB), Chapter 16 (pp. 831-880) 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, 2006.

  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

• P. Anderson, Meyer, A., Eisenhardt, K., Carley, K. and Pettigrew, A., "Introduction to the Special Issue: Applications of Complexity Theory to Organization Science", Organization Science 10 (3), 1999, 233-236.

• Eric Bonabeau, "Predicting the Unpredictable," Harvard Business Review, 2002.

  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.

  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.

  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."

• D. Debenham and Wilkinson, I. F., "Exploration versus Exploitation in Market Competition", Industry and Innovation 13 (3), 2006, 263-289.

  Abstract: "A general simulation model of market competition is developed to explore the effectiveness of and interactions between different types of product exploration and exploitation strategies, that is, innovation, imitation and process improvement. The model, like real markets, is highly non-linear such that analytical solutions are not possible. We use simulation experiments to examine firm survival and the effectiveness of different strategies under different market conditions including competitors’ strategies, how long it takes for each strategy to bear fruit and how costly it is, and the timing, growth and duration of product life cycles. The model is implemented on the Internet and provides the basis for further experiments to examine the impact of different combinations of firm strategies on survival and performance."

• K. J. Dooley and A. Van de Ven, "Explaining Complex Organizational Dynamics", Organization Science 10 (3), 1999, 359-372

  Abstract: "Studies of organizational processes can yield observations in the form of event time series that can be analyzed to determine whether they reflect periodic, chaotic, white noise, or pink noise dynamic patterns.… a causal system can be characterized by its dimensionality, and by the nature of interaction between causal factors. Low dimensional causal systems yield periodic and chaotic dynamics, while high dimensional causal systems yield white and pink noise dynamics. Periodic and white noise dynamics stem from systems where causal factors act independently, or in a linear fashion, while chaotic and pink noise systems stem from systems where causal factors act interdependently, in a nonlinear fashion. Thus, given a diagnosis of an observed event time series, we can hypothesize a particular story, or causal process theory, that might explain in organization-specific terms why such dynamics came about."

• G. Easton, R. Brooks, K. Georgieva, I. F. Wilkinson, "Understanding the Dynamics of Industrial Networks Using Kauffman Boolean Networks", Advances in Complex Systems 11(1), 2008, 139–164.

  Abstract: "Whilst one of the key themes in industrial networks research has been the dynamics of change, relatively little work, empirical or theoretical, has been devoted to the dynamics of stability. This paper presents a new approach to this problem by using the Boolean networks model that was originally devised by Stuart Kauffman as an abstract model of generic networks. The elements in the model are connected by rules of Boolean logic, and a novel aspect of this research is that the elements represent the industrial network exchanges rather than stock entities (the organizations). The model structure consists of interactions between the exchanges and the results represent the pattern of exchange episodes. A total of 42 networks were modeled and the dynamics analyzed in detail, and five of these cases are presented in this paper. The models produced realistic behavior and provided some insights into the reasons for stability in industrial networks."

• G. Easton, I. F. Wilkinson, and K. Georgieva, "On the Edge of Chaos: Towards Evolutionary Models of Industrial Networks", in Hans Georg Gemunden and Thomas Ritter, eds., Relationships and Networks in International Markets, Elsevier, 1997, 273-294

  Abstract: "A research program focusing on understanding and modelling the evolution of industrial networks is described which draws on recent theoretical and modeling advances in the area of artificial life and complex adaptive systems. The creation and evolution of the structure of industrial networks is seen to emerge through the on-going micro processes of action and interaction among and within firms. … we focus attention on a particular class of models develop by Stuart Kauffman called NK Models, which permit key elements of industrial structures to be modelled effectively. Using this approach we develop a model of the interaction between dyadic relations in industrial networks. Future developments of this type of research are discussed as well as its implications for understanding, predicting and coping with the dynamics and development of industrial structures."

• A. Følgesvold and F. Prenkert, "Magic pelagic — An agent-based simulation of 20 years of emergent value accumulation in the North Atlantic herring exchange system", Industrial Marketing Management 38 (5), 2009, 529-540

  Abstract: "Presents an empirically-based multi-agent simulation model of an industrial network modeled as an exchange system and tests for how variations in uncertainty of the input resource and how changing demand patterns affect the accumulation and distribution of exchange values in the system as a whole. The empirical material is a case study of an industrial network connecting herring supply with demand. The results indicate that certain types of internal organizing are more adaptive to changes in conditions and are better suited to absorb these changes. The paper contributes to both a theory and method emphasizing the modeling of emergent nonlinear patterns of networks."

• Jacob Goldenberg, Barak Libai, and Eitan Muller, "Using Complex Systems Analysis to Advance Marketing Theory Development", Academy of Marketing Science Review [Online] 2001.

  Abstract: "Stochastic Cellular Automata simulations are used to examine the process of new product growth. A computer-based Cellular Automata Diffusion Simulation (hereafter, CADS), developed in order to simulate the diffusion process within a social system is used, that creates a "virtual community" in which information flows and a probability exists of potential adopters being affected by it, and in turn adopting, consequently affecting the process in the following period."

• Jacob Goldenberg, Barak Libai, and Eitan Muller, "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth", Marketing Letters, 12 (3), 2001, 211-223.

  Abstract: "Stochastic Cellular Automata simulations are used to model the impact of word-of mouth communication processes, involving a mix of strong and weak personal ties, on product adoption processes. Results show that the impact of weak ties in total is just as strong as strong ties, external marketing efforts are effective only in the early periods and the effect of strong ties diminishes as personal network size decreases."

• D. B. Hibbert and I. F. Wilkinson, "Chaos in the dynamics of markets", Journal of the Academy of Marketing Science 22(3), 1994, 218-233.

  Abstract: "A brief introduction to chaos theory is given. Its relevance to marketing is then illustrated through a discussion of different types of marketing models that can result in complex dynamics, including chaos. Next, techniques for detecting the presence of chaos in the behavior of real systems are reviewed. Finally, the implications of chaos theory for marketing theory and practice are discussed."

• 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.

  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."

• W.-S. Kim, "Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Simulation Study", Journal of Artificial Societies and Social Simulation, 12, 2009.

  Abstract: "This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. The main purposes are to consider a social factor, trust, in modeling the agents' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The results reveal symmetrical trust levels between two trading agents emerge, as well as the resulting collaboration patterns in the entire supply network. Also, agents' decision-making behavior based on trust can contribute to the reduction in the variability of inventory levels because mutual trust based on past experiences of trading diminish an agent's uncertainties about the trustworthiness of its trading partners."

• 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."

• 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.

  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."

• R. E. Marks, Midgley, D. F., and Cooper, L. G., "Adaptive behavior in an oligopoly", Evolutionary Algorithms in Management Applications, ed. by J. Biethahn and V. Nissen, (Berlin: Springer-Verlag), 1995, 225-239. (available here).

  Abstract: "An application of repeated games and machine learning (GAs) to adaptive behaviour in a retail market for ground coffee. Using historical data on the weekly prices and promotional instruments of the four largest and several smaller coffee sellers in a U.S. regional retail market, and using an estimated market model to predict each seller’s market share and profits in response to others’ actions in any week, we examine the adaptive strategic behaviour of the three largest brands of coffee over time."

• Bill McKelvey, "Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes", Organization Science 10 (3), 1999, 294-321.

  Abstract: "Can firms and coevolutionary groups suffer from too much interdependent complexity? Is complexity theory an alternative explanation to competitive selection for the emergent order apparent in coevolutionary industry groups? … Based on complexity theory, four kinds of complexity are identified. Kauffman’s “NK[C] model” is positioned “at the edge of chaos” between complexity driven by “Newtonian” simple rules and rule-driven deterministic chaos. Kauffman’s insight, which is the basis of the findings in this paper, is that complexity is both a consequence and a cause. Multicoevolutionary complexity in firms is defined by moving natural selection processes inside firms and down to a “parts” level of analysis, in this instance Porter’s value chain level, to focus on microstate activities by agents. … This allows a translation of Kauffman’s theory to firms, paying particular attention to (1) how value chain landscapes might be modeled, (2) assumptions underlying Kauffman’s models making them amenable to firms, and (3) a delineation of seven of Kauffman’s computational experiments."

• D. F. Midgley, Marks, R.E., and Cooper, L.G., "Breeding competitive strategies", Management Science, 43(3), 1997, 257–275.

  Abstract: "Genetic algorithms are used to evolve strategies in oligopolistic markets characterized by asymmetric competition. The approach is illustrated using scanner tracking data of brand actions in a real market. An asymmetric market-share model and a category-volume model are combined to represent market response to the actions of brand managers. The actions available to each artificial brand manager are constrained to four typical marketing actions of each from the historical data. Each brand's strategies evolve through simulations of repeated interactions in a virtual market, using the estimated weekly profits of each brand as measures of its fitness for the genetic algorithm. The artificial agents bred in this environment outperform the historical actions of brand managers in the real market."

• D. F. Midgley, Marks, R. E. and Kunchamwar D., "The building and assurance of agent-based models: an example and challenge to the field", Journal of Business Research, Special Issue: Complexities in Markets, Vol. 60, 2007, 884-893.

  Abstract: "An agent-based model of the complex interactions among consumers, retailers, and manufacturers is used to explore issues of model assurance. Explorations indicate two challenges for the agent-based models field. The first challenge is to address the critical issue of software verification. The second challenge is to overcome the many methodological challenges that exist in empirically validating these models. A method is proposed based on the Genetic Algorithm to address both challenges, but our experiments, and the lack of good data for many kinds of agents, suggest a minimalist approach to building and assuring agent-based models in general."

• 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.

  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."

• 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."

• 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."

• 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.

  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.

  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), 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."

• N. S. P. Tay and R. F. Lusch, "A preliminary test of Hunt’s General Theory of Competition: using artificial adaptive agents to study complex ill-defined environments", Journal of Business Research 58, 2005, 1155-1168.

  Abstract: "Evolutionary computing tools are used to build a competitive market that mimics Hunt’s General Theory of Competition (HGTC), in which competition is disequilibrium provoking and both innovation and organizational learning are endogenous. A discussion illustrates how this form of simulation can be valuable to the business strategist and also serve as an alternative method for competitive market strategy research."

• 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 F. 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."

• Ian F. Wilkinson, "Toward a Theory of Structural Change and Evolution in Marketing Channels", Journal of Macromarketing, Fall 1990, 18-46.

  Abstract: "At a given time, the organizational structure of a channel shapes the flow of marketing activity. Over time, the flow of marketing activity shapes the organizational structure of the channel. This article explains how activity shapes organizational structure. It draws on recent advances in analyzing the dynamics of systems and research in urban dynamics. Different types of dynamic models are described and illustrated, and research implications are outlined."

• Ian F. Wilkinson, Wiley, James B., and Lin, Aizhong, "Modelling the Structural Dynamics of Industrial Networks", Interjournal of Complex Systems, Article #409, 2001, www.interjournal.org

  Abstract: "Business relations and networks may be (possibly unintentional) consequences of the ongoing process of interaction in which network structure emerges in a bottom up self-organizing way rather then in a top down organized by firms. NK Models developed by Stuart Kauffman are used to develop computer models that are capable of mimicking the evolutionary processes of industrial networks."

• (John) L. Zhiang, Zhao, X., Ismail, Kiran M. and Karley, Kathleen M., “Organizational Design and Restructuring in Response to Crises: Lessons from Computational Modeling and Real-World Cases", Organization Science 17 (5) (2006), 598–618

  Abstract: "A matched analysis of 80 real organizational cases and 80 computer-simulated organizations is used to that a crisis can present critical challenges to organizational performance both externally and internally, and that there is no design guarantee that a high-performing organization will continue to perform well during a crisis situation. In addition, when organizations restructure to adapt to crisis situations, they often face the serious challenges of having to understand not only the external environment, but also organizational design traps."

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).

• GoldSim is commercial Monte Carlo simulation software for dynamically modeling complex systems in business, engineering, and science. GoldSim supports decision and risk analysis by simulating future performance while quantiatively representing the uncertainty and risks inherent in all complex systems.

• NetLogo is the most commonly used ABM/S tool in academia. While it does not support UML (and is not object-oriented) it has the advantage that it is easy to learn, as it uses a simple programming language based on Logo. Furthermore many sample models and tutorials exist for this tool, much more than for the other tools mentioned above.

• Repast Simphony is a free and open source ABM/S platform. In its latest version, it has been equipped with a new agent state chart framework. Repast Simphony is a java-based modelling system that is designed for use on workstations and small computing clusters. A lean and expert-focused C++ based modelling system that is designed for use on large computing clusters and supercomputers is also available but does not support graphical agent modelling (however, it supports a Logo-like programming language which is designed to be simpler than C++).

Research Groups and Some Early Individual Researchers

• ACE-Related Research Groups

• Petra Ahrweiler, (School of Business, University College Dublin, Ireland): Artificial social systems; Innovation networks; Computer simulation; Policy research; Science and technology studies; Economic sociology; Innovation and technology management.

• 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, (Emeritus Professor, University of Manchester, UK): Evolutionary economics and the modeling of evolutionary processes in relation to innovation, competition, and economic growth.

• Bart Nooteboom (Tilburg University, 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.

• Kermit Wies (Deputy Executive Director for Research and Analysis, Chicago Metropolitan Agency for Planning (CMAP), Chicago, IL) is exploring agent-based applications to regional transportation planning.

• 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.

Copyright © Leigh Tesfatsion. All Rights Reserved.