Improving Individual, Group, and Organizational Decisions: Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

The descriptive, causal, predictive, and evaluation analytics illustrated in Chaps. 3 – 11 are largely about risk assessment. That is, they are about quantifying how large risks are now; predicting how much smaller they would become if costly interventions were undertaken (e.g., shifting pigs from closed to open production or further reducing air pollution levels); and evaluating how effective past interventions have been and how well current systems that help to monitor and control potential risks are performing. Such analytics help to inform decision-makers about current risks and the probable effectiveness and tradeoffs among objectives created by proposed risk management actions. This chapter and those that follow turn to prescriptive risk management issues: deciding what to do next and learning how to better achieve desired goals. This chapter reviews principles of benefit-cost analysis and practical psychological pitfalls that make it difficult for individuals, groups, and organizations to learn optimally from experience. It proposes possible ways to overcome these obstacles, drawing on insights from learning analytics and adaptive optimization from Chap. 2 . Chapter 13 offers advice on how to help move organizations toward effective risk management practices by recognizing and rejecting common excuses that inhibit excellent collective risk management decision-making and by taking advantage of opportunities to learn and collaborate in sensing, interpreting, and responding to warning signs. Chapter 14 considers how regulatory and judicial institutions can work together to promote improved societal risk management and to advance the public interest by assuring that sound causal analytics, manipulative causation, and valid causal inferences, are made the basis for regulatory interventions. Chapter 15 , which concludes this book, brings together and extends these prescriptive threads by considering philosophical, game-theoretic, and economic models for how to make risk management decisions with consequences that span multiple generations.