Modeling Agents through Bounded Rationality Theories

Effectively modeling an agent's cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many elements from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive empirical results from over a hundred agents programmed to perform in optimization problems involving solving for one and two variables.

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