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.

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.”

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).