Boundedly rational backward induction
提出简单公理刻画一种广义逆向归纳，决策者在决策树中向前看固定阶段数，之后用函数聚合后续价值以应对不可预测性，模型由决策树偏好唯一识别。
This paper proposes simple axioms that characterize a generalization of backward induction. At any node of a decision tree, the decision maker looks forward a fixed number of stages perfectly. Beyond that, the decision maker aggregates continuation values according to a function that captures reasoning under unpredictability. The model is uniquely identified from the decision maker's preference over decision trees. Confronting a decision tree, the decision maker iteratively revises her plan for the future as she moves forward in the decision tree. A comparative measure of unpredictability aversion and several examples are discussed.