IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v11y2002i6d10.1023_a1020639132471.html

On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt

Author

Listed:
  • Christina Fang

    (The Wharton School, University of Pennsylvania)

  • Steven Orla Kimbrough

    (The Wharton School, University of Pennsylvania)

  • Stefano Pace

    (School of Management University of Bocconi)

  • Annapurna Valluri

    (The Wharton School, University of Pennsylvania)

  • Zhiqiang Zheng

    (The Wharton School, University of Pennsylvania)

Abstract

We study the emergence of trust behavior at both the individual and the population levels. At the individual level, in contrast to prior research that views trust as fixed traits, we model the emergence of trust or cooperation as a result of trial and error learning by a computer algorithm borrowed from the field of artificial intelligence (Watkins 1989). We show that trust can indeed arise as a result of trial and error learning. Emergence of trust at the population level is modeled by a grid-world consisting of cells of individual agents, a technique known as spatialization in evolutionary game theory. We show that, under a wide range of assumptions, trusting individuals tend to take over the population and trust becomes a systematic property. At both individual and population levels, therefore, we argue that trust behaviors will often emerge as a result of learning.

Suggested Citation

  • Christina Fang & Steven Orla Kimbrough & Stefano Pace & Annapurna Valluri & Zhiqiang Zheng, 2002. "On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt," Group Decision and Negotiation, Springer, vol. 11(6), pages 449-467, November.
    • Handle: RePEc:spr:grdene:v:11:y:2002:i:6:d:10.1023_a:1020639132471
    DOI: 10.1023/A:1020639132471
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1020639132471
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1020639132471?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. John H. Miller, "undated". "The Coevolution of Automata in the Repeated Prisoner's Dilemma," Papers _007, Carnegie Mellon, Department of Decision Sciences, revised 22 Mar 1993.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    4. Michael W. Macy, 1997. "Identity, Interest And Emergent Rationality," Rationality and Society, , vol. 9(4), pages 427-448, November.
    5. 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.
    6. Michael D. Cohen & Rick L. Riolo & Robert Axelrod, 1999. "The Emergence of Social Organization in the Prisoner's Dilemma: How Context-Preservation and Other Factors Promote Cooperation," Working Papers 99-01-002, Santa Fe Institute.
    7. Akbar Zaheer & Bill McEvily & Vincenzo Perrone, 1998. "Does Trust Matter? Exploring the Effects of Interorganizational and Interpersonal Trust on Performance," Organization Science, INFORMS, vol. 9(2), pages 141-159, April.
    8. repec:cup:cbooks:9780521555838 is not listed on IDEAS
    9. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Filipe Costa Souza & Leandro Chaves Rêgo, 2014. "Mixed Equilibrium, Collaborative Dominance and Burning Money: An Experimental Study," Group Decision and Negotiation, Springer, vol. 23(3), pages 377-400, May.
    2. Waltman, L. & Kaymak, U., 2006. "A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning," ERIM Report Series Research in Management ERS-2006-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Khemraj, Tarron, 2019. "Two ethnic security dilemmas and their economic origin," MPRA Paper 101263, University Library of Munich, Germany.
    4. Roos, Patrick & Gelfand, Michele & Nau, Dana & Lun, Janetta, 2015. "Societal threat and cultural variation in the strength of social norms: An evolutionary basis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 129(C), pages 14-23.
    5. Loureiro, Sandra Maria Correia & Guerreiro, João & Tussyadiah, Iis, 2021. "Artificial intelligence in business: State of the art and future research agenda," Journal of Business Research, Elsevier, vol. 129(C), pages 911-926.

    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. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    2. 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.
    3. Benaïm, Michel & Hofbauer, Josef & Hopkins, Ed, 2009. "Learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1694-1709, July.
    4. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    5. B Kelsey Jack, 2009. "Auctioning Conservation Contracts in Indonesia - Participant Learning in Multiple Trial Rounds," CID Working Papers 35, Center for International Development at Harvard University.
    6. Brit Grosskopf & Ido Erev & Eldad Yechiam, 2006. "Foregone with the Wind: Indirect Payoff Information and its Implications for Choice," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(2), pages 285-302, August.
    7. Jacob K. Goeree & Charles A. Holt, 2001. "Ten Little Treasures of Game Theory and Ten Intuitive Contradictions," American Economic Review, American Economic Association, vol. 91(5), pages 1402-1422, December.
    8. Andreas Nicklisch, 2011. "Learning strategic environments: an experimental study of strategy formation and transfer," Theory and Decision, Springer, vol. 71(4), pages 539-558, October.
    9. Prajapati, Hari Ram, 2012. "An Application of Game Theory in Strategic Decision of Marriage Occurrence," MPRA Paper 105344, University Library of Munich, Germany, revised 2013.
    10. Giovanna Devetag, 2000. "Coordination in "Critical Mass" Games: An Experimental Study," LEM Papers Series 2000/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. Rick, Scott & Weber, Roberto A., 2010. "Meaningful learning and transfer of learning in games played repeatedly without feedback," Games and Economic Behavior, Elsevier, vol. 68(2), pages 716-730, March.
    12. Nobuyuki Hanaki, 2007. "Individual and Social Learning," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 421-421, May.
    13. Torsten Heinrich & Yoojin Jang & Luca Mungo & Marco Pangallo & Alex Scott & Bassel Tarbush & Samuel Wiese, 2021. "Best-response dynamics, playing sequences, and convergence to equilibrium in random games," Papers 2101.04222, arXiv.org, revised Nov 2022.
    14. Tesfatsion, Leigh, 1998. "Teaching Agent-Based Computational Economics To Graduate Students," Economic Reports 18193, Iowa State University, Department of Economics.
    15. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    16. Hofbauer, Josef & Hopkins, Ed, 2005. "Learning in perturbed asymmetric games," Games and Economic Behavior, Elsevier, vol. 52(1), pages 133-152, July.
    17. Apesteguia, Jose & Huck, Steffen & Oechssler, Jorg, 2007. "Imitation--theory and experimental evidence," Journal of Economic Theory, Elsevier, vol. 136(1), pages 217-235, September.
    18. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    19. Franke, Reiner, 2003. "Reinforcement learning in the El Farol model," Journal of Economic Behavior & Organization, Elsevier, vol. 51(3), pages 367-388, July.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:spr:grdene:v:11:y:2002:i:6:d:10.1023_a:1020639132471. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.