IDEAS home Printed from https://ideas.repec.org/p/sce/scecf5/284.html
   My bibliography  Save this paper

An Analysis on Simulation Models of Competing Parties

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://repec.org/sce2005/up.19065.1107180109.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bärbel M. R. Stadler, 1998. "Abstention Causes Bifurcations in Two-Party Voting Dynamics," Working Papers 98-08-072, Santa Fe Institute.
    2. Vjollca Sadiraj & Jan Tuinstra & Frans Winden, 2006. "A computational electoral competition model with social clustering and endogenous interest groups as information brokers," Public Choice, Springer, vol. 129(1), pages 169-187, October.
    3. Michael J. Ensley & Michael W. Tofias & Scott De Marchi, 2009. "District Complexity as an Advantage in Congressional Elections," American Journal of Political Science, John Wiley & Sons, vol. 53(4), pages 990-1005, October.
    4. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    5. John Jackson, 2014. "Location, location, location: the Davis-Hinich model of electoral competition," Public Choice, Springer, vol. 159(1), pages 197-218, April.
    6. Bendor, Jonathan & Diermeier, Daniel & Ting, Michael M., 2000. "A Behavioral Model of Turnout," Research Papers 1627, Stanford University, Graduate School of Business.
    7. Jie-Shin Lin & Chris Birchenhall, 2000. "Learning And Adaptive Artificial Agents: An Analysis Of Evolutionary Economic Models," Computing in Economics and Finance 2000 327, Society for Computational Economics.
    8. Michel Schilperoord & Jan Rotmans & Noam Bergman, 2008. "Modelling societal transitions with agent transformation," Computational and Mathematical Organization Theory, Springer, vol. 14(4), pages 283-301, December.
    9. Michael Ensley & Scott Marchi & Michael Munger, 2007. "Candidate uncertainty, mental models, and complexity: Some experimental results," Public Choice, Springer, vol. 132(1), pages 231-246, July.
    10. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner, 2021. "Cold play: Learning across bimatrix games," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 419-441.
    11. Scott E. Page, 1991. "Simulations and Spatial Voting Methods," Discussion Papers 952, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    12. Cacho, Oscar J. & Simmons, Phil, 1999. "A genetic algorithm approach to farm investment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 43(3), pages 1-18, September.
    13. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    14. Bullard, James & Duffy, John, 1998. "Learning And The Stability Of Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 2(1), pages 22-48, March.
    15. Shu-Heng Chen & Chia-Hsuan Yeh, 1999. "Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market," Computing in Economics and Finance 1999 613, Society for Computational Economics.
    16. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February.
    17. Adalbert Mayer, 2022. "An Agent-Based Macroeconomic Model with Endogenous Intertemporal Decision Rules," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 48(4), pages 548-579, October.
    18. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    19. Arifovic, Jasmina, 2001. "Evolutionary dynamics of currency substitution," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 395-417, March.
    20. L. Lambertini, 2010. "Oligopoly with Hyperbolic Demand: A Differential Game Approach," Journal of Optimization Theory and Applications, Springer, vol. 145(1), pages 108-119, April.

    More about this item

    Keywords

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

    JEL classification:

    • Z - Other Special Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf5:284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.