Backward induction is the most widely accepted principle for predicting behavior in dynamic games. In experiments, however, players frequently violate this principle. An alternative is a 2-parameter “dynamic level-k ” model, where players choose a rule from a rule hierarchy. The rule hierarchy is iteratively defined such that the level-k rule is a best-response to the level-(k − 1) rule and the level-∞ rule corresponds to backward induction. Players choose rules based on their best guesses of others ’ rules and use past plays to improve their guesses. The model captures two systematic violations of backward induction in the centipede game, limited induction and time unraveling. The dynamic level-k model can be considered as a tracing procedure for backward induction because the former always converges to the latter in the limit
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