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Learning rules for optimal selection in a varying environment: mate choice revisited


  • Edmund J. Collins
  • John M. McNamara
  • David M. Ramsey


The quality of a chosen partner can be one of the most significant factors affecting an animal's long-term reproductive success. We investigate optimal mate choice rules in an environment where there is both local variation in the quality of potential mates within each local mating pool and spatial (or temporal) variation in the average quality of the pools themselves. In such a situation, a robust rule that works well across a variety of environments will confer a significant reproductive advantage. We formulate a full Bayesian model for updating information in such a varying environment and derive the form of the rule that maximizes expected reward in a spatially varying environment. We compare the theoretical performance of our optimal learning rule against both fixed threshold rules and simpler near-optimal learning rules and show that learning is most advantageous when both the local and environmental variances are large. We consider how optimal simple learning rules might evolve and compare their evolution with that of fixed threshold rules using genetic algorithms as minimal models of the relevant genetics. Our analysis points up the variety of ways in which a near-optimal rule can be expressed. Finally, we describe how our results extend to the case of temporally varying environments. Copyright 2006.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:beheco:v:17:y:2006:i:5:p:799-809

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    Cited by:

    1. Alpern, Steve & Katrantzi, Ioanna, 2009. "Equilibria of two-sided matching games with common preferences," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1214-1222, August.
    2. 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.
    3. repec:eee:thpobi:v:96:y:2014:i:c:p:1-7 is not listed on IDEAS
    4. Saglam, Ismail, 2017. "A New Heuristic in Mutual Sequential Mate Search," MPRA Paper 79448, University Library of Munich, Germany.


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