The objective of this thesis is to improve the understanding of human behavior that goes beyond monetary rewards. In particular, it investigates social influences in individual’s decision making in situations that involve coordination, competition, and deciding for others. Further, it compares how monetary and social outcomes are perceived. The common theme of all studies is uncertainty. The first four essays study individual decisions that have uncertain consequences, be it due to the actions of others or chance. The last essay, in turn, uses the advances in research on decision making under uncertainty to predict behavior in riskless choices. The first essay, Fairness Versus Efficiency: How Procedural Fairness Concerns Affect Coordination, investigates whether preferences for fair rules undermine the efficiency of coordination mechanisms that put some individuals at a disadvantage. The results from a laboratory experiment show that the existence of coordination mechanisms, such as action recommendations, increases efficiency, even if one party is strongly disadvantaged by the mechanism. Further, it is demonstrated that while individuals’ behavior does not depend on the fairness of the coordination mechanism, their beliefs about people’s behavior do. The second essay, Dishonesty and Competition. Evidence from a stiff competition environment, explores whether and how the possibility to behave dishonestly affects the willingness to compete and who the winner is in a competition between similarly skilled individuals. We do not find differences in competition entry between competitions in which dishonesty is possible and in which it is not. However, we find that due to the heterogeneity in propensity to behave dishonestly, around 20% of winners are not the best-performing individuals. This implies that the efficient allocation of resources cannot be ensured in a stiff competition in which behavior is unmonitored. The third essay, Tracing Risky Decision Making for Oneself and Others: The Role of Intuition and Deliberation, explores how individuals make choices under risk for themselves and on behalf of other people. The findings demonstrate that while there are no differences in preferences for taking risks when deciding for oneself and for others, individuals have greater decision error when choosing for other individuals. The differences in the decision error can be partly attributed to the differences in information processing; individuals employ more deliberative cognitive processing when deciding for themselves than when deciding for others. Conducting more information processing when deciding for others is related to the reduction in decision error. The fourth essay, The Effect of Decision Fatigue on Surgeons’ Clinical Decision Making, investigates how mental depletion, caused by a long session of decision making, affects surgeon’s decision to operate. Exploiting a natural experiment, we find that surgeons are less likely to schedule an operation for patients who have appointment late during the work shift than for patients who have appointment at the beginning of the work shift. Understanding how the quality of medical decisions depends on when the patient is seen is important for achieving both efficiency and fairness in health care, where long shifts are popular. The fifth essay, Preferences for Outcome Editing in Monetary and Social Contexts, compares whether individuals use the same rules for mental representation of monetary outcomes (e.g., purchases, expenses) as for social outcomes (e.g., having nice time with friends). Outcome editing is an operation in mental accounting that determines whether individuals prefer to first combine multiple outcomes before their evaluation (integration) or evaluate each outcome separately (segregation). I find that the majority of individuals express different preferences for outcome editing in the monetary context than in the social context. Further, while the results on the editing of monetary outcomes are consistent with theoretical predictions, no existing model can explain the editing of social outcomes.

The U.S. Environmental Protection Agency (EPA) is one of several federal agencies responsible for protecting Americans against significant risks to human health and the environment. As part of that mission, EPA estimates the nature, magnitude, and likelihood of risks to human health and the environment; identifies the potential regulatory actions that will mitigate those risks and protect public health1 and the environment; and uses that information to decide on appropriate regulatory action. Uncertainties, both qualitative and quantitative, in the data and analyses on which these decisions are based enter into the process at each step. As a result, the informed identification and use of the uncertainties inherent in the process is an essential feature of environmental decision making. EPA requested that the Institute of Medicine (IOM) convene a committee to provide guidance to its decision makers and their partners in states and localities on approaches to managing risk in different contexts when uncertainty is present. It also sought guidance on how information on uncertainty should be presented to help risk managers make sound decisions and to increase transparency in its communications with the public about those decisions. Given that its charge is not limited to human health risk assessment and includes broad questions about managing risks and decision making, in this report the committee examines the analysis of uncertainty in those other areas in addition to human health risks. Environmental Decisions in the Face of Uncertainty explains the statement of task and summarizes the findings of the committee.

Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.

This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.

Much economic advice is bogus quantification, warn two leading experts in this essential book, now with a preface on COVID-19. Invented numbers offer a false sense of security; we need instead robust narratives that give us the confidence to manage uncertainty. “An elegant and careful guide to thinking about personal and social economics, especially in a time of uncertainty. The timing is impeccable." — Christine Kenneally, New York Times Book Review Some uncertainties are resolvable. The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible. Before President Barack Obama made the fateful decision to send in the Navy Seals, his advisers offered him wildly divergent estimates of the odds that Osama bin Laden would be in the Abbottabad compound. In 2000, no one—not least Steve Jobs—knew what a smartphone was; how could anyone have predicted how many would be sold in 2020? And financial advisers who confidently provide the information required in the standard retirement planning package—what will interest rates, the cost of living, and your state of health be in 2050?—demonstrate only that their advice is worthless. The limits of certainty demonstrate the power of human judgment over artificial intelligence. In most critical decisions there can be no forecasts or probability distributions on which we might sensibly rely. Instead of inventing numbers to fill the gaps in our knowledge, we should adopt business, political, and personal strategies that will be robust to alternative futures and resilient to unpredictable events. Within the security of such a robust and resilient reference narrative, uncertainty can be embraced, because it is the source of creativity, excitement, and profit.

Whether we like it or not we all feel that the world is uncertain. From choosing a new technology to selecting a job, we rarely know in advance what outcome will result from our decisions. Unfortunately, the standard theory of choice under uncertainty developed in the early forties and fifties turns out to be too rigid to take many tricky issues of choice under uncertainty into account. The good news is that we have now moved away from the early descriptively inadequate modeling of behavior. This book brings the reader into contact with the accomplished progress in individual decision making through the most recent contributions to uncertainty modeling and behavioral decision making. It also introduces the reader into the many subtle issues to be resolved for rational choice under uncertainty.

decision making.