Where Do Human Heuristics Come From?

Marcel Binz; Dominik Endres

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arXiv · 1902.07580

Where Do Human Heuristics Come From?

Marcel Binz·Dominik Endres

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Abstract

Human decision-making deviates from the optimal solution, that maximizes cumulative rewards, in many situations. Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies. More specifically we investigate the hypothesis, that humans do not know optimal decision-making algorithms in advance, but instead employ a learned, resource-bounded approximation. The idea is formalized through combining a recently proposed meta-learning model based on Recurrent Neural Networks with a resource-bounded objective. The resulting approach is closely connected to variational inference and the Minimum Description Length principle. Empirical evidence is obtained from a two-armed bandit task. Here we observe patterns in our family of models that resemble differences between individual human participants.

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Marcel Binz, Dominik Endres. 2019-02-20. Where Do Human Heuristics Come From?. https://arxiv.org/abs/1902.07580

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