Bounded rationality and learning in dynamic programming environments
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The purpose of this thesis is to explain “excess sensitivity” puzzle observed in consumption behavior an alternative way. By deviating from full optimization axiom, in a dynamic extension of Arthur’s stochastic decision model, it was observed that a tendency of excess consumption following temporary income shock prevails. Another main technical contribution achieved in this thesis is in modelling behavior and learning in intertemporal decision problems. In particular, an extension of Arthur’s type of behavior to dynamic situations and comparison of the corresponding values with those of Bellman’s dynamic programming solution is achieved. Moreover it was shown by using stochastic approximation theory that classifier systems learning ends up at the ‘strength’ values corresponding to the Arthur’s value function.
excess sensitivity puzzle
stochastic approximation theory
classifier systems learning