Adapting the Adaptive Toolbox: The computational cost of building rational behavior

Abstract

One of the main challenges in cognitive science is to explain how people make reasonable inferences in daily life. Theories that attempt to explain this either fail to capture inference in its full generality or they seem to postulate intractable computations. One account that seems to aspire to achieve generality without running into the problem of computational intractability is “the adaptive toolbox” by Gigerenzer and Todd (1999b). This theory proposes that humans have a toolbox, adapted through learning and/or evolution to the environment. Such a toolbox contains heuristics, each of them computationally tractable, and a selector which selects a heuristic for every situation so that the toolbox can solve the type of inference problems that people solve in their daily life. In this project we investigate whether such a toolbox can have adapted and under what circumstances. We propose a formalization of an adaptive toolbox and two formalizations of the adaptation process and analyze them with computational complexity theory. Our results show that applying a toolbox is doable in reasonable amount of time, but adapting a toolbox can only be done efficiently when certain restrictions are placed on the environment. If these restrictions occur in the environment and the adaptation processes exploit them humans could have indeed adapted an adaptive toolbox.