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An Integrated Computational Model of Multiparty Electoral Competition

dc.contributor.authorQuinn, Kevin M.
dc.contributor.authorMartin, Andrew D.
dc.date.accessioned2015-12-21T17:10:15Z
dc.date.available2015-12-21T17:10:15Z
dc.date.issued2002
dc.identifier.citationKevin M. Quinn and Andrew D. Martin. 2002. “An Integrated Computational Model of Multiparty Electoral Competition.” Statistical Science. 17: 405-419.en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/116238
dc.description.abstractMost theoretic models of multiparty electoral competition make the assumption that party leaders are motivated to maximize their vote share or seat share. In plurality-rule systems this is a sensible assumption. However, in proportional representation systems, this assumption is questionable since the ability to make public policy is not strictly increasing in vote shares or seat shares. We present a theoretic model in which party leaders choose electoral declarations with an eye toward the expected policy outcome of the coalition bargaining game induced by the party declarations and the parties’ beliefs about citizens’ voting behavior. To test this model, we turn to data from the 1989 Dutch parliamentary election. We use Markov chain Monte Carlo methods to estimate the parties’ beliefs about mass voting behavior and to average over measurement uncertainty and missing data. Due to the complexity of the parties’ objective functions and the uncertainty in objective function estimates, equilibria are found numerically. Unlike previous models of multiparty electoral competition, the equilibrium results are consistent with the empirical declarations of the four major Dutch parties.en_US
dc.language.isoen_USen_US
dc.publisherInstitute of Mathematical Statisticsen_US
dc.subjectMonte Carlo methoden_US
dc.subjectvoting behavioren_US
dc.subjectelectoral strategyen_US
dc.subjectcoalition formationen_US
dc.titleAn Integrated Computational Model of Multiparty Electoral Competitionen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPolitical Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumLSA Dean's Officeen_US
dc.contributor.affiliationotherHarvard Universityen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/116238/1/statsci02.pdf
dc.identifier.sourceStatistical Scienceen_US
dc.identifier.orcid0000-0002-6532-0721en_US
dc.identifier.name-orcidMartin, Andrew; 0000-0002-6532-0721en_US
dc.owningcollnamePolitical Science


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