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The Evolution of Ambiguity in Sender—Receiver Signaling Games

Author

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  • Roland Mühlenbernd

    (Leibniz-Centre General Linguistics (ZAS), 10117 Berlin, Germany
    Centre for Language Evolution Studies, Nicolaus Copernicus University, 87-100 Toruń, Poland)

  • Sławomir Wacewicz

    (Centre for Language Evolution Studies, Nicolaus Copernicus University, 87-100 Toruń, Poland)

  • Przemysław Żywiczyński

    (Centre for Language Evolution Studies, Nicolaus Copernicus University, 87-100 Toruń, Poland)

Abstract

We study an extended version of a sender–receiver signaling game—a context-signaling (CS) game that involves external contextual cues that provide information about a sender’s private information state. A formal evolutionary analysis of the investigated CS game shows that ambiguous signaling strategies can achieve perfect information transfer and are evolutionarily stable. Moreover, a computational analysis of the CS game shows that such perfect ambiguous systems have the same emergence probability as non-ambiguous perfect signaling systems in multi-agent simulations under standard evolutionary dynamics. We contrast these results with an experimental study where pairs of participants play the CS game for multiple rounds with each other in the lab to develop a communication system. This comparison shows that unlike virtual agents, human agents clearly prefer perfect signaling systems over perfect ambiguous systems.

Suggested Citation

  • Roland Mühlenbernd & Sławomir Wacewicz & Przemysław Żywiczyński, 2022. "The Evolution of Ambiguity in Sender—Receiver Signaling Games," Games, MDPI, vol. 13(2), pages 1-19, February.
    • Handle: RePEc:gam:jgames:v:13:y:2022:i:2:p:20-:d:755978
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    3. Karl H. Schlag & Dieter Balkenborg, 2001. "Evolutionarily stable sets," International Journal of Game Theory, Springer;Game Theory Society, vol. 29(4), pages 571-595.
    4. repec:cup:cbooks:9780521555838 is not listed on IDEAS
    5. Christian Hilbe & Štěpán Šimsa & Krishnendu Chatterjee & Martin A. Nowak, 2018. "Evolution of cooperation in stochastic games," Nature, Nature, vol. 559(7713), pages 246-249, July.
    6. Pawlowitsch, Christina, 2008. "Why evolution does not always lead to an optimal signaling system," Games and Economic Behavior, Elsevier, vol. 63(1), pages 203-226, May.
    7. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    8. Marco LiCalzi & Roland Mühlenbernd, 2019. "Categorization and Cooperation across Games," Games, MDPI, vol. 10(1), pages 1-21, January.
    9. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Sandholm, William H., 2019. "An introduction to ABED: Agent-based simulation of evolutionary game dynamics," Games and Economic Behavior, Elsevier, vol. 118(C), pages 434-462.
    Full references (including those not matched with items on IDEAS)

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