Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/913

Reinforcement-based adaptive learning in asymmetric two-person bargaining with incomplete information

Authors Rapoport, Amnon
Daniel, Terry E.
Issue Date 1998-02
Source Marketing Working Paper Series ; MKTG 98.107
Summary 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 (in press), 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.
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Language English
Format Working paper
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