Optimal utility and probability functions for agents with finite computational precision
When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximise reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximising choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision – in other words, that decisions are irreducibly corrupted by noise – the distortions of value and probability displayed by humans are approximately optimal in that they maximise reward and minimise uncertainty. In two empirical studies, we manipulate factors that change the reward-maximising form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make “irrational” economic choices.