Risk Perception: Measurement and Aggregation

Abstract In a model inspired by neuroscience, we study choice between lotteries as a process of encoding and decoding noisy perceptual signals. The implications of this process for behavior depend on the decision-maker’s understanding of risk. When the aggregation of perceptual signals is coarse, encoding and decoding generate behavioral risk attitudes even for vanishing perceptual noise. We show that the optimal encoding of lottery rewards is S-shaped and that low-probability events are optimally oversampled. Taken together, the model can explain adaptive-risk attitudes and probability weighting, as in prospect theory. Furthermore, it predicts that risk attitudes are influenced by the anticipation of risk, time pressure, experience, salience, and availability heuristics.