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A comparison of zero and minimal intelligence agendas in Markov Chain voting models

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  • Brewer, Paul J
  • Moberly, Raymond

Abstract

Emergent behavior in repeated collective decisions of minimally intelligent agents -- who at each step in time invoke majority rule to choose between a status quo and a random challenge -- can manifest through the long-term stationary probability distributions of a Markov Chain. We use this known technique to compare two kinds of voting agendas: a zero-intelligence agenda that chooses the challenger uniformly at random, and a minimally-intelligent agenda that chooses the challenger from the union of the status quo and the set of winning challengers. We use Google Co-Lab's GPU accelerated computing environment, with code we have hosted on Github, to compute stationary distributions for some simple examples from spatial-voting and budget-allocation scenarios. We find that the voting model using the zero-intelligence agenda converges more slowly, but in some cases to better outcomes.

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  • Brewer, Paul J & Moberly, Raymond, 2021. "A comparison of zero and minimal intelligence agendas in Markov Chain voting models," OSF Preprints ajfdb, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:ajfdb
    DOI: 10.31219/osf.io/ajfdb
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    1. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 603-630.
    2. Elizabeth Maggie Penn, 2009. "A Model of Farsighted Voting," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 36-54, January.
    3. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    4. Kalandrakis, Anastassios, 2004. "A three-player dynamic majoritarian bargaining game," Journal of Economic Theory, Elsevier, vol. 116(2), pages 294-322, June.
    5. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
    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. John Duggan & César Martinelli, 2017. "The Political Economy of Dynamic Elections: Accountability, Commitment, and Responsiveness," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 916-984, September.
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