Skewed Noise

Experimental evidence suggests that individuals who face an asymmetric distribution over the likelihood of a specific event might actually prefer not to know the exact value of this probability. We address these findings by studying a decision maker who has recursive, non-expected utility preferences over two-stage lotteries. For a binary lottery that yields the better outcome with probability p, we identify noise around p with a compound lottery that induces a probability distribution over the exact value of the probability and has an average value p. We first propose and characterize a new notion of skewed distributions. We then use this result to provide conditions under which a decision maker who always rejects symmetric noise around p will always reject skewed to the left noise, but might accept skewed to the right noise. The model can be applied to the areas of investment under risk, medical decision making, and criminal law procedures, and can also be used to address the phenomenon of ambiguity seeking in the context of decision making under uncertainty.