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...

We characterize axiomatically a stochastic choice model, the consistent-mistakes model (CMM), that describes an error-prone decision maker's choices. In contrast to random utility models, CMMs generate closed-form choice probability. Under the axioms, we uniquely identify from the choices an...

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...