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Abstract
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Computational complexity theory studies the computational resources (e.g., time, space, randomness, etc.) required for solving computational problems. Its analytical tools are not yet commonly taught in cognitive science and many cognitive scientists still go about their business without much concern for the computational resources presupposed by their theories and models. Yet, there are good reasons for cognitive scientists to care more about computational complexity. In this talk I will explain how computational complexity theory provides useful analytical tools to guide and constrain computational-level in cognitive science in general, and in decision-making research in particular. I will also discuss common objections (e.g., based on appeals to heuristics, “as if” explanations, irrationality or non-optimality) and show how they dissolve through rigorous analysis.
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