High-stakes failures of backward induction
利用美国电视游戏节目《价格猜猜看》40多年的数据，研究逆向归纳策略在高风险情境下的实际表现，发现参赛者系统性地偏离子博弈完美纳什均衡，而有限前瞻的修正模型能更好解释其行为。
We examine high-stakes strategic choice using more than 40 years of data from the American TV game show The Price Is Right. In every episode, contestants play the Showcase Showdown, a sequential game of perfect information for which the optimal strategy can be found through backward induction. We find that contestants systematically deviate from the subgame perfect Nash equilibrium. These departures from optimality are well explained by a modified agent quantal response model that allows for limited foresight. The results suggest that many contestants simplify the decision problem by adopting a myopic representation, and optimize their chances of beating the next contestant only. In line with learning, contestants' choices improve over the course of our sample period.