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Policy issues have grown ever more complex and politically more contestable. So governments in advanced democracies often do not understand the problems they have to deal with and do not know how to solve them. Thus, rational problem-solving models are highly unconvincing. Conversely, the Multiple-Streams Framework starts out from these conditions, which has led to increasing interest in it. Nevertheless, there has not yet been a systematic attempt to assess the potential of such scholarship. This volume is the first attempt to fill that gap by bringing together a group of international scholars to assess the strengths and weaknesses of the Framework from different angles. Chapters explore systematically and empirically the Framework’s potential in different national contexts and in policy areas from climate change and foreign policy to healthcare and the welfare state.
This dissertation addresses the issue of how to make decisions involving both time delay and ambiguous information. This dissertation is arranged into three chapters. Chapter 1 reviews a set of studies on the influence of ambiguity and time delay on individual decision making and raises two relevant research questions: (1) Are decision makers' ambiguity preferences different for prospects resolved in the present and the future?; and (2) Do decision makers' time preferences differ under ambiguous and unambiguous payoffs? Chapter 2 and 3 are two independent essays, each addressing one of the above questions. The first essay examines ambiguity preferences under present and delayed resolutions across low and high probabilities. Results of three studies show an interaction effect between resolution time and probability level. Under the immediate resolution, we find that individuals exhibit ambiguity aversion at high probabilities and weak ambiguity seeking or indifference at low probabilities, consistent with prior literature. However, delayed resolution regresses aversion and seeking behaviors to neutrality. Drawing on the construal level theory and the dual-process theory, we attribute this interaction effect to the difference in processing styles for present and future prospects. The second essay demonstrates the impact of ambiguous future payoffs on intertemporal preferences. Six studies show that, despite the fact that ambiguous and delayed payoffs are generally disliked separately, together they produce a positive effect. That is, ambiguous future payoffs are more likely to be preferred than precise payoffs (with equal expected values) in intertemporal decision-making. We propose the overshadowing hypothesis to explain this effect and rule out three other possibilities. Finally, we establish boundary conditions by systematically examining whether the effect persists at various ambiguity levels and time points.
In the first approximation, decision making is nothing else but an optimization problem: We want to select the best alternative. This description, however, is not fully accurate: it implicitly assumes that we know the exact consequences of each decision, and that, once we have selected a decision, no constraints prevent us from implementing it. In reality, we usually know the consequences with some uncertainty, and there are also numerous constraints that needs to be taken into account. The presence of uncertainty and constraints makes decision making challenging. To resolve these challenges, we need to go beyond simple optimization, we also need to get a good understanding of how the corresponding systems and objects operate, a good understanding of why we observe what we observe – this will help us better predict what will be the consequences of different decisions. All these problems – in relation to different application areas – are the main focus of this book.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Two leaders in the field explore the foundations of bounded rationality and its effects on choices by individuals, firms, and the government. Bounded rationality recognizes that human behavior departs from the perfect rationality assumed by neoclassical economics. In this book, Sanjit Dhami and Cass R. Sunstein explore the foundations of bounded rationality and consider the implications of this approach for public policy and law, in particular for questions about choice, welfare, and freedom. The authors, both recognized as experts in the field, cover a wide range of empirical findings and assess theoretical work that attempts to explain those findings. Their presentation is comprehensive, coherent, and lucid, with even the most technical material explained accessibly. They not only offer observations and commentary on the existing literature but also explore new insights, ideas, and connections. After examining the traditional neoclassical framework, which they refer to as the Bayesian rationality approach (BRA), and its empirical issues, Dhami and Sunstein offer a detailed account of bounded rationality and how it can be incorporated into the social and behavioral sciences. They also discuss a set of models of heuristics-based choice and the philosophical foundations of behavioral economics. Finally, they examine libertarian paternalism and its strategies of “nudges.”
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.
Some years ago we, the editors of this volume, found out about each other's deeply rooted interest in the concept of time, the usage of time, and the effects of shortage of time on human thought and behavior. Since then we have fostered the idea of bringing together different perspectives in this area. We are now, there fore, very content that our idea has materialized in the present volume. There is both anecdotal and empirical evidence to suggest that time con straints may affect behavior. Managers and other professional decision makers frequently identify time pressure as a major constraint on their behavior (Isen berg, 1984). Chamberlain and Zika (1990) provide empirical support for this view, showing that complaints of insufficient time are the most frequently report ed everyday minor stressors or hassles for all groups of people except the elderly. Similarly, studies in occupational settings have identified time pressure as one of the central components of workload (Derrich, 1988; O'Donnel & Eggemeier, 1986).
This volume focuses on the evolution of public policy and the role of agenda setting with regard to policymaking in countries of the Global South. The authors illustrate the emergence of public policy research as an academic discipline, and highlight various aspects of history, governance, politics, and economics as components of public policy theory development. By offering a cross-national perspective, the papers contribute to a better understanding of when, how, and by whom a given policy agenda is designed, which is essential to grasping how policy is implemented. In turn, the authors investigate how the development of public policy research has influenced policymaking in fields such as democratization, migration, corruption, agriculture, environment, education, and entrepreneurship and, more specifically, agenda setting in selected countries of the Global South.