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Reinforcement-Based Adaptive Learning in Asymmetric Two-Person Bargaining with Incomplete Information

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  • Amnon Rapoport
  • Terry Daniel
  • Darryl Seale
Abstract
The sealed bid k-double auction is a mechanism used to structure bilateral bargaining under two-sided incomplete information. This mechanism is tested in two experiments in which subjects are asked to bargain repeatedly for 50 rounds with the same partner under conditions of information disparity favoring either the buyer (Condition BA) or seller (Condition SA). Qualitatively, the observed bid and offer functions are in agreement with the Bayesian linear equilibrium solution (LES) constructed by Chatterjee and Samuelson (1983). A trader favored by the information disparity, whether buyer or seller, receives a larger share of the realized gain from trade than the other trader. Comparison with previous results reported by Daniel, Seale, and Rapoport (1998), who used randomly matched rather than fixed pairs, shows that when reputation effects are present this advantage is significantly enhanced. A reinforcement-based learning model captures the major features of the offer and bid functions, accounting for most of the variability in the round-to-round individual decisions. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Amnon Rapoport & Terry Daniel & Darryl Seale, 1998. "Reinforcement-Based Adaptive Learning in Asymmetric Two-Person Bargaining with Incomplete Information," Experimental Economics, Springer;Economic Science Association, vol. 1(3), pages 221-253, December.
    • Handle: RePEc:kap:expeco:v:1:y:1998:i:3:p:221-253
    DOI: 10.1023/A:1009903210510
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    References listed on IDEAS

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    Cited by:

    1. Seale, Darryl A. & Daniel, Terry E. & Rapoport, Amnon, 2001. "The information advantage in two-person bargaining with incomplete information," Journal of Economic Behavior & Organization, Elsevier, vol. 44(2), pages 177-200, February.
    2. Gabuthy, Yannick & Jacquemet, Nicolas & Marchand, Nadège, 2008. "Does resorting to online dispute resolution promote agreements? Experimental evidence," European Economic Review, Elsevier, vol. 52(2), pages 259-282, February.
    3. James E. Parco & Amnon Rapoport, 2004. "Enhancing Honesty in Bargaining Under Incomplete Information: An Experimental Study of the Bonus Procedure," Group Decision and Negotiation, Springer, vol. 13(6), pages 539-562, November.
    4. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
    5. Darryl Seale & Amnon Rapoport, 2000. "Elicitation of Strategy Profiles in Large Group Coordination Games," Experimental Economics, Springer;Economic Science Association, vol. 3(2), pages 153-179, October.
    6. Subhajyoti Bandyopadhyay & John M. Barron & Alok R. Chaturvedi, 2005. "Competition Among Sellers in Online Exchanges," Information Systems Research, INFORMS, vol. 16(1), pages 47-60, March.
    7. Terry E. Daniel & James E. Parco, 2005. "Fair, Efficient and Envy-Free Bargaining: An Experimental Test of the Brams-Taylor Adjusted Winner Mechanism," Group Decision and Negotiation, Springer, vol. 14(3), pages 241-264, May.
    8. Leonidas Spiliopoulos & Andreas Ortmann, 2018. "The BCD of response time analysis in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 21(2), pages 383-433, June.

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