Market design for standardization problems with agent-based social simulation

This paper provides a computational market model with technological competitions among standards and presents simulations of various scenarios concerning standardization problems. The market model has three features: (1) economic entities such as consumers and firms are regarded as autonomous agents; (2) micro interactions among consumer agents or firm agents have essential mechanisms interpretable in real markets; and (3) consumers’ preferences and firms’ technologies co-affect their evolutionary behavior. In recent years, consumers have experienced various inconveniences from de facto competition based on a market mechanism. Standardization communities or committees such as the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) need to design a compatible standard or a de jure standard in a market. However, it is difficult for market designers to decide a method or timing for the standardization. Here, by introducing a novel technique used in agent-based social simulation (ABSS), which we call “scenario analysis,” we aim to support such decision making. Scenario analysis provides the possible market changes that can occur following implementation of a design policy under a specific market situation and the market mechanisms that generate these market changes.

[1]  Mauro Gallegati,et al.  Validating and Calibrating Agent-Based Models: A Case Study , 2007 .

[2]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[3]  Beom Jun Kim,et al.  Consumer referral in a small world network , 2006, Soc. Networks.

[4]  T. Ida.  Coevolution of product quality and consumer preferences , 2010 .

[5]  Robert E. Marks,et al.  Validating Simulation Models: A General Framework and Four Applied Examples , 2007 .

[6]  J. Bettman.  An information processing theory of consumer choice , 1979 .

[7]  Tao Zhang,et al.  Agent-based simulation of consumer purchase decision-making and the decoy effect , 2007 .

[8]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[9]  Robert D. Groot.  Consumers don't play dice, influence of social networks and advertisements , 2006 .

[10]  Maya Cohen-Meidan,et al.  The effects of standardization process on competition: An event study of the standardization process in the US cable modem market , 2007 .

[11]  Joe Tidd,et al.  The Influence of Project Novelty on the New Product Development Process , 2002 .

[12]  H. Leibenstein.  Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand , 1950 .

[13]  Neil Gandal,et al.  The Dvd Versus Divx Standard War: Empirical Evidence of Network Effects and Preannouncement Effects , 2002 .

[14]  W. Arthur,et al.  INCREASING RETURNS AND LOCK-IN BY HISTORICAL EVENTS , 1989 .

[15]  W. Brock,et al.  Interactions-Based Models , 2000 .

[16]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[17]  A. McGahan.  Bandwagon Effects in High Technology Industries , 2001 .

[18]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[19]  G. Nigel Gilbert,et al.  Agent-Based Models , 2007 .

[20]  Elizabeth J. Altman,et al.  Technology and Innovation Management , 2013 .

[21]  Jarunee Wonglimpiyarat,et al.  Standard competition: Is collaborative strategy necessary in shaping the smart card market? , 2005 .

[22]  John W. Payne,et al.  The adaptive decision maker: Name index , 1993 .

[23]  P. David,et al.  The Economics Of Compatibility Standards: An Introduction To Recent Research 1 , 1990 .

[24]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[25]  W. Mitchell,et al.  Coalition Formation in Standard-Setting Alliances , 1995 .

[26]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  Robin Cowan,et al.  Network Structure and the Diffusion of Knowledge , 2004 .

[29]  Paul Windrum,et al.  A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems , 2007 .

[30]  Joseph Farrell,et al.  Coordination Through Committees and Markets , 1987 .

[31]  Tammo H. A. Bijmolt,et al.  Targeting and timing promotional activities : An agent-based model for the takeoff of new products , 2007 .

[32]  Graham K. Rand,et al.  Quantitative Applications in the Social Sciences , 1983 .

[33]  Paul Windrum,et al.  Leveraging technological externalities in complex technologies: Microsoft’s exploitation of standards in the browser wars , 2004 .

[34]  G. Haines,et al.  The Theory of Buyer Behavior. , 1970 .

[35]  Manuel Cartier.  An Agent-Based Model of Innovation Emergence in Organizations: Renault and Ford Through the Lens of Evolutionism , 2004, Comput. Math. Organ. Theory.

[36]  Clayton M. Christensen.  The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail , 2013 .

[37]  Jeffrey H. Rohlfs.  A theory of interdependent demand for a communications service , 1974 .

[38]  Learning Dynamics in Platform Externality , 2004 .

[39]  P. Zarembka.  Frontiers in econometrics , 1973 .

[40]  R A Fisher,et al.  The evolution of sexual preference. , 1915, The Eugenics review.

[41]  John Bessant,et al.  Managing Innovation: Integrating Technological, Market, and Organizational Change, 2nd Edition , 2001 .

[42]  E. Rogers.  Diffusion of Innovations , 1962 .

[43]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.