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A Model of Satisficing Behaviour

Author

Listed:
  • Rajiv Sarin

    (Department of Economics, University of Exeter)

  • Hyun Chang Yi

    (Economic Research Institute, Bank of Korea)

Abstract

We build a model of satisficing behaviour. We explicitly introduce the value the decision maker “expects” from an action, where this expectation is adaptively formed. This valuation of an action is differentiated from her satisficing level which is taken to be the value the agent “expects” from her outside option. If the valuation is higher than her satisficing level she continues with the current action, updating her valuation of the action. If she receives a low payoff and her valuation of the action falls below her satisficing level, she updates her satisficing level downward the valuation and explores an alternative action. And, she occasionally experiences shocks on her satisficing level and choice of action. We show that in individual decision problems, satisficing behaviour results in cautious, maximin choice. In games like the Prisoner’s Dilemma and Stag Hunt, they in the long run play either cooperative or defective outcomes and in a class of coordination games, they coordinate on Pareto optimal outcomes.

Suggested Citation

  • Rajiv Sarin & Hyun Chang Yi, 2020. "A Model of Satisficing Behaviour," Working Papers 2020-21, Economic Research Institute, Bank of Korea.
  • Handle: RePEc:bok:wpaper:2021
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    References listed on IDEAS

    as
    1. Glenn Ellison, 1994. "Cooperation in the Prisoner's Dilemma with Anonymous Random Matching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(3), pages 567-588.
    2. Karandikar, Rajeeva & Mookherjee, Dilip & Ray, Debraj & Vega-Redondo, Fernando, 1998. "Evolving Aspirations and Cooperation," Journal of Economic Theory, Elsevier, vol. 80(2), pages 292-331, June.
    3. Youngse Kim, 1999. "Satisficing and optimality in 2þ2 common interest games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 13(2), pages 365-375.
    4. Kats, Amoz & Thisse, Jacques-Francois, 1992. "Unilaterally Competitive Games," International Journal of Game Theory, Springer;Game Theory Society, vol. 21(3), pages 291-299.
    5. Napel, Stefan, 2003. "Aspiration adaptation in the ultimatum minigame," Games and Economic Behavior, Elsevier, vol. 43(1), pages 86-106, April.
    6. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    7. Selten, Reinhard & Stoecker, Rolf, 1986. "End behavior in sequences of finite Prisoner's Dilemma supergames A learning theory approach," Journal of Economic Behavior & Organization, Elsevier, vol. 7(1), pages 47-70, March.
    8. Imhof, Lorens & Nowak, Martin & Fudenberg, Drew, 2007. "Tit-for-Tat or Win-Stay, Lose-Shift?," Scholarly Articles 3200671, Harvard University Department of Economics.
    9. Battalio, Raymond & Samuelson, Larry & Van Huyck, John, 2001. "Optimization Incentives and Coordination Failure in Laboratory Stag Hunt Games," Econometrica, Econometric Society, vol. 69(3), pages 749-764, May.
    10. Bendor Jonathan & Mookherjee Dilip & Ray Debraj, 2001. "Reinforcement Learning in Repeated Interaction Games," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 1(1), pages 1-44, March.
    11. Venkatesh Bala & Sanjeev Goyal, 2000. "A Noncooperative Model of Network Formation," Econometrica, Econometric Society, vol. 68(5), pages 1181-1230, September.
    12. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
    13. Jonathan Bendor & Dilip Mookherjee & Debraj Ray, 2001. "Aspiration-Based Reinforcement Learning In Repeated Interaction Games: An Overview," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 3(02n03), pages 159-174.
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    More about this item

    Keywords

    Satisficing Behaviour; Individual Decision Problem; Non-cooperative Game; Coordination Game; Markov Process; Invariant Distribution;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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