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Adaptive Parties in Spatial Elections

  • Ken Kollman (a1), John H. Miller (a2) and Scott E. Page (a1)


We develop a model of two-party spatial elections that departs from the standard model in three respects: parties' information about voters' preferences is limited to polls; parties can be either office-seeking or ideological; and parties are not perfect optimizers, that is, they are modelled as boundedly rational adaptive actors. We employ computer search algorithms to model the adaptive behavior of parties and show that three distinct search algorithms lead to similar results. Our findings suggest that convergence in spatial voting models is robust to variations in the intelligence of parties. We also find that an adaptive party in a complex issue space may not be able to defeat a well-positioned incumbent.



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Arifovic, Jasmina. 1989. “Learning by Genetic Algorithms in Economic Environments.” Santa Fe Institute Working Paper 90–001.
Axelrod, Robert. 1986. “An Evolutionary Approach to Norms.” American Political Science Review 80:10961111.
Axelrod, Robert. 1987. “The Evolution of Strategies in the Iterated Prisoner's Dilemma.” In Genetic Algorithms and Simulated Annealing, ed. Davis, Lawrence. Los Altos, CA: Mungan Kaufmann.
Bates, Robert H. 1990. “Macropolitical Economy in the Field of Development.” In Perspectives on Positive Political Economy, ed. Alt, James and Shepsle, Kenneth. New York: Cambridge University Press.
Cohen, Michael D. 1984. “Conflict and Complexity: Goal Diversity and Organizational Search Effectiveness.” American Political Science Review 78:435–51.
Coleman, James S. 1989. “Simulation Games and the Development of Social Theory.” Simulation and Games 20:144–64.
Coughlin, Peter J. 1990a. “Majority Rule and Election Models.” Journal of Economic Surveys 3:157n88.
Coughlin, Peter J. 1990b. “Candidate Uncertainty and Electoral Equilibria.” In Advances in the Spatial Theory by Voting, ed. Enelow, James and Hinich, Melvin. New York: Cambridge University Press.
Davis, Otto A., Hinich, Melvin, and Ordeshook, Peter. 1970. “An Expository Development of a Mathematical Model of the Electoral Process.” American Political Science Review 64: 426–48.
Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper & Brothers.
Goldberg, David E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Menlo Park, CA: Addison-Wesley.
Holland, John H. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
Holland, John H., and Miller, John. 1991. “Artificial Adaptive Agents in Economic Theory.” Presented at annual meeting of the American Economic Association, New Orleans.
Kramer, Gerald. 1977. “A Dynamical Model of Political Equilibrium.” Journal of Economic Theory 15:310–34.
McKelvey, Richard. 1976. “Intransitivities in Multidimensional Voting Models and Some Implications for Agenda Control.” Journal of Economic Theory 12:472–82.
Marimon, Ramon, McGrattan, Ellen, Sargent, Thomas J.. 1990. “Money as a Medium of Exchange in an Economy with Artificially Intelligent Agents.” Journal of Economic Dynamics and Control 14:329–73
Miller, John H. 1986. “A ‘Genetic Model’ of Adaptive Economic Behavior.” University of Michigan. Mimeo.
Miller, John H. 1987. “The Evolution of Automata in the Repeated Prisoner's Dilemma.” University of Michigan. Mimeo.
Page, Scott E., Kollman, Ken, and Miller, John H.. 1992. “Political Parties and Electoral Landscapes.” Presented at annual meeting of the American Political Science Association, Chicago.
Plott, Charles. 1967. “A Notion of Equilibrium and Its Possibility under Majority Rule.” American Economic Review 79: 787806.
Riker, William. 1982. Liberalism against Populism. San Francisco: Freeman.
Whicker, Marcia L., and Strickland, Ruth A.. 1990. “U.S. Constitutional Amendments, the Ratification Process, and Public Opinion: A Computer Simulation.” Simulation and Gaming 21:115–32.

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Adaptive Parties in Spatial Elections

  • Ken Kollman (a1), John H. Miller (a2) and Scott E. Page (a1)


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