Reinforcement-Based Adaptive Learning in Asymmetric Two-Person Bargaining with Incomplete Information
通过两个实验检验密封投标k-双拍卖机制，发现信息优势方获得更大交易收益，且固定配对下的声誉效应会显著增强这一优势，强化学习模型能较好拟合个体决策变化。
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.