Estimating the Influence of Fairness on Bargaining Behavior
构建了一个嵌入公平偏好的模型，用简单一轮博弈估计公平偏好，并检验其对多轮博弈的预测能力，发现除极小谈判蛋糕外模型有显著预测力。
The strength of bargainers' preferences for fair settlements has important implications for predicting negotiation outcomes and guiding bargaining strategy. Existing literature reports a few calibration exercises for social utility models, but the predictive accuracy of these models for out-of-sample forecasting remains unknown. Therefore, we investigate whether fairness considerations are stable enough across bargaining situations to be quantified and used to forecast bargaining behavior accurately. We develop a model that embeds a preference for fair treatment in a quantal response framework to account for noise and experience. In addition, we estimate preference for fairness (willingness to pay) using the simplest, one-round version of sequential bargaining games and then employ it to perform out-of-sample forecasts of multiple-round games of various lengths, discount factors, pie sizes, and levels of bargainer experience. Except in circumstances in which the bargaining pie is very small, the fitted model has significant and substantial out-of-sample explanatory power. The stability we find implies that the model and techniques might ultimately be extended to estimates of the influence of fairness on field negotiations, as well as across subpopulations.