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An Analysis on Simulation Models of Competing Parties

Author

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  • Jie-Shin Lin

    (Public Policy and Management I-Shou University)

Abstract

Down’s spatial theory of elections (1957) has occupied a prominent theoretical status within political science. Studies use a notion of ideological distance to develop explanations for observable electoral trends. In elections, voters by observing party ideologies and using the information to make decisions for their votes because voters do not always have enough information to appraise the difference of which they are aware. The Downsian idea suggests that parties’ effort to attract votes leads them to adopt a median position. However, many studies have questioned the result and have many different conclusions. In recent years there has been an increasing interest in learning and adaptive behaviour including simulation models. In this study, we model the dynamics of competing parties who make decisions in an evolving environment and construct simulation models of party competition. We illustrate and compare their consequences by analyzing two variants of computational models.

Suggested Citation

  • Jie-Shin Lin, 2005. "An Analysis on Simulation Models of Competing Parties," Computing in Economics and Finance 2005 284, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:284
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    References listed on IDEAS

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    1. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    2. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
    3. Kollman, Ken & Miller, John H. & Page, Scott E., 1998. "Political Parties and Electoral Landscapes," British Journal of Political Science, Cambridge University Press, vol. 28(1), pages 139-158, January.
    4. Kollman, Ken & Miller, John H. & Page, Scott E., 1997. "Landscape formation in a spatial voting model," Economics Letters, Elsevier, vol. 55(1), pages 121-130, August.
    5. Coughlin, Peter J, 1990. "Majority Rule and Election Models," Journal of Economic Surveys, Wiley Blackwell, vol. 4(2), pages 157-188.
    6. McKelvey, Richard D., 1976. "Intransitivities in multidimensional voting models and some implications for agenda control," Journal of Economic Theory, Elsevier, vol. 12(3), pages 472-482, June.
    7. Kollman, Ken & Miller, John H. & Page, Scott E., 1992. "Adaptive Parties in Spatial Elections," American Political Science Review, Cambridge University Press, vol. 86(4), pages 929-937, December.
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    More about this item

    Keywords

    Spatial Voting Model; Party Competition; Evolutionary Modelling; Learning;
    All these keywords.

    JEL classification:

    • Z - Other Special Topics

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