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Market design for standardization problems with agent-based social simulation

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Abstract

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.

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References

  • Arthur WB (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J 99(394):116–131

    Article  Google Scholar 

  • Axelrod R, Mitchell W, Thomas RE, Scott BD, Bruderer E (1995) Coalition formation in standard-setting alliances. Manage Sci 41(9):1493–1508

    Article  Google Scholar 

  • Baker JE (1985) Adaptive selection methods for genetic algorithms. In: Grefenstette JJ (ed) Proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum Associates, Hillsdale, pp 101–111

    Google Scholar 

  • Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  • Bettman JR (1979) An information processing theory of consumer choice. Addison-Wesley, Reading

    Google Scholar 

  • Bianchi C, Cirillo P, Gallegati M, Vagliasindi PA (2007) Validating and calibrating agent-based models: a case study. Comput Econ 30(3):245–264

    Article  Google Scholar 

  • Blackwell RD, Miniard PW, Engel JF (2001) Consumer behavior (9th ed). South-Western, Cincinnati

    Google Scholar 

  • Brock W, Durlauf S (2001) Interactions-based models. In: Heckman J, Leamer E (eds) Handbook of econometrics 5. Elsevier Science, Amsterdam, pp 3297–3380

    Google Scholar 

  • Cartier M (2004) An agent-based model of innovation emergence in organizations: Renault and Ford through the lens of evolutionism. Comput Math Organ Theory 10(2):147–153

    Article  Google Scholar 

  • Christensen CM (1997) The innovator’s dilemma: when new technologies cause great firms to fail. Harvard Business School Press, Boston

    Google Scholar 

  • Cohen-Meidan M (2007) The effects of standardization process on competition: an event study of the standardization process in the US cable modem market. Telecommun Policy 31:619–631

    Article  Google Scholar 

  • Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28:1557–1575

    Article  Google Scholar 

  • David PA, Greenstein S (1990) The economics of compatibility standards: an introduction to recent research. Econ Innov New Technol 1:3–41

    Article  Google Scholar 

  • Deguchi H (2003) Learning dynamics in platform externality. In: Takahashi S, Kijima K, Sato R (eds) Applied general systems research on organization, Springer jpn, pp 167–176

  • Delre SA, Jager W, Bijmolt THA, Janssen MA (2006) Targeting and timing promotional activities: an agent-based model for the takeoff of new products. J Bus Res 60(8):826–835

    Article  Google Scholar 

  • Dranove D, Gandal N (2003) The DVD-vs.-DIVX standard war: empirical evidence of network effects and preannouncement effects. J Econ Manage Strategy 12:363–386

    Article  Google Scholar 

  • Fagiolo G, Moneta A, Windrum PA (2007) Critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput Econ 30:195–226

    Article  Google Scholar 

  • Farrell J, Saloner G (1988) Coordination through committee and markets. Rand J Econ 19(2):235–252

    Article  Google Scholar 

  • Fisher RA (1915) The evolution of sexual preferences. Eugen Rev 7:184–192

    Google Scholar 

  • Gilbert N (2007) Agent-based models. Quantitative applications in the social sciences. Sage, London

    Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston

    Google Scholar 

  • Grefenstette JJ, Baker JE (1989) How genetic algorithms work: a critical look at implicit parallelism. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, California, pp 20–27

    Google Scholar 

  • Groot RD (2006) Consumers don’t play dice, influence of social networks and advertisements. Physica A 363(2):446–458

    Article  Google Scholar 

  • Howard JA, Sheth JN (1969) The theory of buyer behavior. Wiley, New York

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Ida T (2003) On the coevolution of the product quality and the consumer preference. Graduate School of Economics. Kyoto University Working Paper 66:1–28

    Google Scholar 

  • Inoki T (1998) Stand face to face with market principles, meanings and limitations of competitive system in market. Organ Sci 32(2):4–14

    Google Scholar 

  • Jun T, Kim J, Kim BJ, Choi MY (2006) Consumer referral in a small world network. Soc Netw 28(3):232–246

    Article  Google Scholar 

  • Leibenstein H (1950) Bandwagon, Snob, and Veblen effects in the theory of consumers’ demand. Q J Econ 64:183–207

    Article  Google Scholar 

  • Marks R (2007) Validating simulation models: a general framework and four applied examples. Comput Econ 30(3):265–290

    Article  Google Scholar 

  • McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York

    Google Scholar 

  • Ohori K, Takahashi S (2007) Modeling essential micro interactions for analyzing emergent phenomena in market. The Fourth European Social Simulation Association Conference, 379–390

  • Payne JW, Bettman JR, Johnson EJ (1993) The adaptive decision maker. Cambridge University Press, New York

    Google Scholar 

  • Rogers EM (1962) Diffusion of innovations. The Free Press, New York

    Google Scholar 

  • Rohlfs JH (1974) A theory of interdependent demand for a communications service. Bell J Econ Manage Sci 5(1):16–37

    Article  Google Scholar 

  • Rohlfs JH (2001) Bandwagon effects in high technology industries. MIT, Cambridge

    Google Scholar 

  • Takahashi S (2004) Framework in agent-based approach for analysis of evolutionary processes of consumers’ preferences. J Jpn Soc Manage Info 13:1–17

    Google Scholar 

  • Takahashi S, Ohori K (2005) Agent-based model of coevolutionary processes of firms technologies and consumers preferences -simulation of lock-in behavior-. Proceedings of NAACSOS CD-ROM

  • Tidd J, Bodley K (2002) The influence of project novelty on the new product development process. R&D Manage 32(2):127–138

    Article  Google Scholar 

  • Tidd J, Bessant J, Pavitt K (2005) Managing innovation: integrating technological, market and organizational change. Wiley, New York

    Google Scholar 

  • Tschirky H, Hans-Helmuth J, Pascal S (2005) Technology and innovation management on the move. Orell Füssli, Zürich

    Google Scholar 

  • Watts D, Strogatz S (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442

    Article  Google Scholar 

  • Windrum P (2004) Leveraging technological externalities in complex technologies: Microsoft’s exploitation of standards in the browser wars. Res Policy 33(3):385–394

    Article  Google Scholar 

  • Wonglimpiyarat J (2005) Standard competition: is collaborative strategy necessary in shaping the smart card market? Technol Forecast Soc Change 72:1001–1010

    Article  Google Scholar 

  • Zhang T, Zhang D (2007) Agent-based simulation of consumer purchase decision-making and the decoy effect. J Bus Res 60(8):912–922

    Article  Google Scholar 

  • Yamada H (2004) Competitive Strategies for de fact Standard (in Japanese). Hakuto-Shobo

Download references

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 169-8555, Japan

    Kotaro Ohori & Shingo Takahashi

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  1. Kotaro Ohori
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  2. Shingo Takahashi
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Correspondence to Kotaro Ohori.

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Ohori, K., Takahashi, S. Market design for standardization problems with agent-based social simulation. J Evol Econ 22, 49–77 (2012). https://doi.org/10.1007/s00191-010-0196-y

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  • DOI: https://doi.org/10.1007/s00191-010-0196-y

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