Learning Aversion in Benefit-Cost Analysis with Uncertainty

The decision biases discussed in previous chapters can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. This chapter argues that well-documented decision biases encourage learning-aversion, or predictably sub-optimal learning and premature decision-making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information-seeking, under-estimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and sub-optimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). This chapter presents a conceptual framework for understanding and overcoming learning-aversion and for implementing low-regret learning strategies. Regulation of air pollutants with uncertain health effects is used as a motivating example.