IDEAS home Printed from https://ideas.repec.org/a/gam/jgames/v13y2022i2p20-d755978.html
   My bibliography  Save this article

The Evolution of Ambiguity in Sender—Receiver Signaling Games

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4336/13/2/20/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-4336/13/2/20/
    Download Restriction: no
    ---><---

    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. 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.
    4. 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.
    5. Marco LiCalzi & Roland Mühlenbernd, 2019. "Categorization and Cooperation across Games," Games, MDPI, vol. 10(1), pages 1-21, January.
    6. Karl H. Schlag & Dieter Balkenborg, 2001. "Evolutionarily stable sets," International Journal of Game Theory, Springer;Game Theory Society, vol. 29(4), pages 571-595.
    7. repec:cup:cbooks:9780521555838 is not listed on IDEAS
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Foley & Rory Smead & Patrick Forber & Christoph Riedl, 2021. "Avoiding the bullies: The resilience of cooperation among unequals," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-18, April.
    2. Daskalova, Vessela & Vriend, Nicolaas J., 2021. "Learning frames," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 78-96.
    3. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    4. Anthony Ziegelmeyer & Frédéric Koessler & Kene Boun My & Laurent Denant-Boèmont, 2008. "Road Traffic Congestion and Public Information: An Experimental Investigation," Journal of Transport Economics and Policy, University of Bath, vol. 42(1), pages 43-82, January.
    5. DeJong, D.V. & Blume, A. & Neumann, G., 1998. "Learning in Sender-Receiver Games," Other publications TiSEM 4a8b4f46-f30b-4ad2-bb0c-1, Tilburg University, School of Economics and Management.
    6. Jean-François Laslier & Bernard Walliser, 2015. "Stubborn learning," Theory and Decision, Springer, vol. 79(1), pages 51-93, July.
    7. Blume, A. & DeJong, D.V. & Neumann, G. & Savin, N.E., 2000. "Learning and Communication in Sender-Reciever Games : An Economic Investigation," Other publications TiSEM 138dc36b-5269-421a-9e79-b, Tilburg University, School of Economics and Management.
    8. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    9. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard, 2005. "Rage against the machines : how subjects learn to play against computers," Papers 05-36, Sonderforschungsbreich 504.
    10. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    11. Gary Charness & Dan Levin, 2003. "Bayesian Updating vs. Reinforcement and Affect: A Laboratory Study," Levine's Bibliography 666156000000000180, UCLA Department of Economics.
    12. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    13. Gary Charness & Dan Levin, 2005. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," American Economic Review, American Economic Association, vol. 95(4), pages 1300-1309, September.
    14. Ying-Fang Kao & Ragupathy Venkatachalam, 2021. "Human and Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(3), pages 889-909, March.
    15. Battalio,R. & Samuelson,L. & Huyck,J. van, 1998. "Risk dominance, payoff dominance and probabilistic choice learning," Working papers 2, Wisconsin Madison - Social Systems.
    16. Blume, Andreas & Gneezy, Uri, 2000. "An Experimental Investigation of Optimal Learning in Coordination Games," Journal of Economic Theory, Elsevier, vol. 90(1), pages 161-172, January.
    17. Pedro Dal Bo & Guillaume R. Frochette, 2011. "The Evolution of Cooperation in Infinitely Repeated Games: Experimental Evidence," American Economic Review, American Economic Association, vol. 101(1), pages 411-429, February.
    18. Ho, Teck H. & Camerer, Colin F. & Chong, Juin-Kuan, 2007. "Self-tuning experience weighted attraction learning in games," Journal of Economic Theory, Elsevier, vol. 133(1), pages 177-198, March.
    19. Mertikopoulos, Panayotis & Sandholm, William H., 2018. "Riemannian game dynamics," Journal of Economic Theory, Elsevier, vol. 177(C), pages 315-364.
    20. Martin G. Kocher & Matthias Sutter, 2005. "The Decision Maker Matters: Individual Versus Group Behaviour in Experimental Beauty-Contest Games," Economic Journal, Royal Economic Society, vol. 115(500), pages 200-223, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgames:v:13:y:2022:i:2:p:20-:d:755978. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.