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Cheap talk when interests conflict.

Silk1, Kaldor, Boyd

  • 1Department of Anthropology, University of California, Los Angeles

Animal Behaviour
|February 17, 2000
PubMed
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Honest, low-cost animal signals can evolve even with conflicting interests, especially in repeated interactions. This challenges previous assumptions about costly signaling in evolutionary biology.

Area of Science:

  • Evolutionary biology
  • Animal communication
  • Game theory

Background:

  • Evolutionary analyses typically assume low-cost signals require aligned interests between signalers and receivers.
  • Honest signaling under conflicting interests is thought to necessitate costly signals.
  • Recent work suggests low-cost signals can be stable if coordination benefits are high.

Purpose of the Study:

  • To investigate the evolution of low-cost signals in animal communication under conflicting interests without coordination benefits.
  • To extend game theory models of signaling evolution.
  • To provide an empirical example of honest, low-cost signaling in a conflict scenario.

Main Methods:

  • Development of a game theory model analyzing signaling evolution.

Related Experiment Videos

  • Mathematical analysis of the model to determine conditions for stable low-cost signals.
  • Empirical observation and analysis of vocalizations in female rhesus macaques.

    • Main Results:

    • The game theory model demonstrates that low-cost signals can evolve despite conflicts of interest and no need for coordination, provided interactions are repeated.
    • Female rhesus macaques exhibit honest, low-cost vocal signaling during interactions where conflicts of interest are present.

    Conclusions:

    • Repeated interactions can facilitate the evolution of honest, low-cost signals even when signalers and receivers have divergent interests.
    • The findings challenge the traditional view that honest signals must be costly when interests conflict.
    • Vocalizations in female rhesus macaques serve as an empirical example supporting the model's predictions.