A first workshop on Agent-based models of market dynamics and consumer behaviour was organised at the University of Surrey by the Centre for Research in Social Simulation at the University of Surrey on the 17th and 18th of January 2006. Information on this meeting can be found at:

http://www.ias.surrey.ac.uk/reports/conmod-report.html

A selected number of papers presented during this workshop has been published in a special issue 'Complexities in Markets' of the Journal of Business Research, Vol. 60, Issue 8 pp. 813-924 (August 2007). This issue can be downloaded as a zip file: http://www.essa.eu.org/simulation-wiki/FirstWorkshopOnMarketDynamics2006/JBR_specialissue_ABM.zip

The special issue includes the following papers:

Deffuant and Huet describe a model of attitude formation focusing on how processes of information filtering may generate counter-intuitive effects, such as the emergence of an overall negative product attitude within a population, despite initial favorable views. These results are relevant in understanding how processes of word-of-mouth may affect consumers' evaluations of products, and are hence important in understanding the promotional effects on consumers' product evaluations.

Delre, Jager, Bijmolt, and Janssen explore how the timing and targeting of a promotional strategy affect the diffusion of a new product within different types of markets. This study indicates that targeting a number of clustered consumers is effective in generating positive recommendations and successful product diffusion. The timing of such a promotion strategy also appears to be a critical variable, and is related to the relevance of social aspects to markets.

Frenzel Baudisch explains how heterogeneity among consumers may emerge from social comparison processes. Using data on the market for shoes in Germany, the author clearly illustrates how new reference groups may emerge from social comparison processes, leading to the establishment of new submarkets and the evolution of aggregate consumer heterogeneity. These results are relevant to understanding how new consumer segments develop.

Garcia,Rummel, and Hauser focus on how agent-based-models can be validated against empirical data. Following the history friendly model approach, the authors study a case from the wine industry by first describing the industry background, then delineating the main theoretical issues to be explored, and finally developing a computational model. The tentative simulation results provide insights into how an alliance of wine-makers is able to make the diffusion of screw-caps a success, and thereby contribute to the understanding of how collaboration among competing companies may benefit all parties.

Izquierdo and Izquierdo use an agent-based model to demonstrate how consumers' uncertainty about product quality may cause market failure. Assuming that buyers assess product quality by using their past experiences, and considering quality estimation rules that are individually sensible and unbiased, the authors show that market interaction results in an underestimation of product quality, which produces systematic drops in prices and losses in market efficiency.

Jager presents a general framework for developing agent rules to simulate artificial markets. Jager's argument is that for testing marketing strategies one should equip agents with rules that capture how consumers respond to these strategies. The author's general framework is organized along the main marketing strategies of product characteristics, pricing, placement and promotion. The paper concludes with suggestions for the construction of experimental designs and the use of different types of empirical data.

Kuenzel and Musters focus on involvement and social interaction processes in the purchase of everyday food products. In an empirical study the authors have found considerable differences in consumer involvement in different food products. Moreover, Kuenzel, and Musters observe differences in consumers' susceptibility to informative social influence as well as in the size of consumer networks. These results are relevant to modeling consumer behavior, in particular with respect to how involvement and social interaction play a role in different types of product markets.

Midgley, Marks, and Kunchamwar use an agent-based model depicting the complex interactions among consumers, retailers and manufacturers to explore issues of model assurance. The two challenges these authors address relate to software verification and the empirical validation of models. The paper proposes a method based on the Genetic Algorithm to confront both these challenges, generally suggesting a minimalist approach to building and assuring agent-based models.

Schenk, Löffler, and Rauh focus on the spatial dimension of consumer behavior. This paper examines the applicability of multi-agent modeling to simulate spatial choice in shopping behavior at a regional level. Based on individual population and store data gathered in northern Sweden, the authors have developed a model for grocery shopping. The authors validate this model on the basis of empirical data.

Vag presents an integration of two modeling philosophies: conjoint analysis and multi-agent simulation. The conceptual integration into dynamic conjoint modeling accommodates notions of social network analysis, consumer behavior modeling, and word-of-mouth marketing. An important point is that these methods are complimentary; for example, conjoint analysis may serve as a tool to supply multi-agent models with behavioral data, and multi-agent simulation may offer dynamics to the static results of conjoint analysis. Vag presents a market model to illustrate how the association between consumers' communication and sales can be studied by using this approach.

Zhang and Zhang focus on the decoy effect, which denotes how adding a third product may alter the preferences for two competing products. With the aid of an agent-based model, the authors formalize consumer motivation as a function that combines personality traits with consumer interaction. The paper demonstrates that this decoy effect is an emergent market dynamic phenomenon originating from the individual behavior of heterogeneous consumers and their interactions in a real world complex market. This approach offers a perspective on how to cope with such dynamic changes.