Behavioral Modeling of Sequential Bargaining Games: Fairness and Limited Backward Induction

Experiments show that in sequential bargaining games (SBG), subjects usually deviate from game-theoretic predictions. Previous explanations have focused on considerations of fairness in the offers, and social utility functions have been formulated to model the data. However, a recent explanation by Ho and Su (2013) for observed deviations from gametheoretic predictions in sequential games is that players engage in limited backward induction. A suite of computational models that integrate different choice models with utility functions are comprehensively evaluated on SBG data. These include DeBruyn and Bolton’s recursive quantal response with social utility functions, those based on Ho and Su’s dynamic level-k, and analogous extensions of the cognitive hierarchy with dynamic components. Our comprehensive analysis reveals that in extended SBG with 5 rounds, models that capture violations of backward induction perform better than those that model fairness. However, we did not observe this result for SBG with less rounds, and fairness of the offer remains a key consideration in these games.