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Simple Heuristics as Equilibrium Strategies in Mutual Sequential Mate Search

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  • Ismail Saglam

Abstract

Human mate choice is a boundedly rational process where individuals search for their mates without appealing to optimization techniques due to informational, computational and time constraints. A seminal work by Todd and Miller (1999) models this search process using simple heuristics, i.e. decision rules that adjust individuals' aspiration levels adaptively. To identify the best heuristic among a number of alternatives, they consider fixed measures of success. In this paper, we deal with the same identification problem by examining whether these heuristics would be favored by behavioral selection. To this aim, we extend the two-phase search model of Todd and Miller (1999) to a behavioral (strategic-form) game in which each individual in the population is a distinct player, each player's strategy space contains the same four heuristics (adjustment rules), and the payoff of each player is measured by the likelihood of his/her mating. For this game, we ask whether any strategy profile at which the whole population plays the same heuristic can be behaviorally stable with respect to the Nash equilibrium concept. Our simulations show that the unanimous use of the Take the Next Best Rule by the whole population never becomes an equilibrium in the simulation range of adolescence lengths. While the Adjust Relative Rule is found to be behaviorally stable for a wide part of the simulation range, especially for medium to high adolescence lengths, the rules Adjust Up/Down and Adjust Relative/2 are favored by behavioral selection for a small part of the simulation range and only when the adolescence is long and short, respectively. We make the final evaluation of the four heuristics with respect to a new success measure that integrates a behavioral stability metric proposed in this paper with two metrics of Todd and Miller (1999), namely the likelihood and the assortativeness of the mating generated by the heuristic in use.

Suggested Citation

  • Ismail Saglam, 2014. "Simple Heuristics as Equilibrium Strategies in Mutual Sequential Mate Search," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-12.
  • Handle: RePEc:jas:jasssj:2013-10-4
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    References listed on IDEAS

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    1. Kimmo Eriksson & Olle Häggström, 2008. "Instability of matchings in decentralized markets with various preference structures," International Journal of Game Theory, Springer;Game Theory Society, vol. 36(3), pages 409-420, March.
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    6. Roth, Alvin E, 1984. "The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory," Journal of Political Economy, University of Chicago Press, vol. 92(6), pages 991-1016, December.
    7. Ayse Mumcu & Ismail Saglam, 2008. "Marriage Formation/Dissolution and Marital Distribution in a Two-Period Economic Model of Matching with Cooperative Bargaining," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(4), pages 1-3.
    8. Edmund J. Collins & John M. McNamara & David M. Ramsey, 2006. "Learning rules for optimal selection in a varying environment: mate choice revisited," Behavioral Ecology, International Society for Behavioral Ecology, vol. 17(5), pages 799-809, September.
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    Cited by:

    1. Ismail Saglam, 2018. "A New Heuristic in Mutual Sequential Mate Search," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 122-145.
    2. Saglam, Ismail, 2017. "Simulating the Mutual Sequential Mate Search Model under Non-homogenous Preferences," MPRA Paper 80522, University Library of Munich, Germany.
    3. Ismail Saglam, 2021. "The Success of the Deferred Acceptance Algorithm Under Heterogenous Preferences with Endogenous Aspirations," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 577-591, February.

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    More about this item

    Keywords

    Mate Choice; Mate Search; Simple Heuristics; Agent-Based Simulation; Behavioral Stability; Equilibrium Strategies;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • J12 - Labor and Demographic Economics - - Demographic Economics - - - Marriage; Marital Dissolution; Family Structure
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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