In cooperative games, one generally assumes that the agents know exactly the joint (monetary) gains that can be achieved by any possible coalition of cooperating agents. In reality, however, only little is known with certainty. This does not necessarily imply that traditional cooperative game theory cannot be applied in practical situations, for in various cases knowledge of the expected gains suffices. In many other cases, however, it is just the sharing of risk that is beneficial. Joint ventures, for instance, exist since cooperation reduces the risk of the investment for the individual parties. Since the existing models fail to incorporate such risks, they are not suitable for analyzing cooperative decision-making under risk. This book aims to rectify this deficiency by discussing a model of cooperative games with random payoffs.

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.

This handbook in two parts covers key topics of the theory of financial decision making. Some of the papers discuss real applications or case studies as well. There are a number of new papers that have never been published before especially in Part II.Part I is concerned with Decision Making Under Uncertainty. This includes subsections on Arbitrage, Utility Theory, Risk Aversion and Static Portfolio Theory, and Stochastic Dominance. Part II is concerned with Dynamic Modeling that is the transition for static decision making to multiperiod decision making. The analysis starts with Risk Measures and then discusses Dynamic Portfolio Theory, Tactical Asset Allocation and Asset-Liability Management Using Utility and Goal Based Consumption-Investment Decision Models.A comprehensive set of problems both computational and review and mind expanding with many unsolved problems are in an accompanying problems book. The handbook plus the book of problems form a very strong set of materials for PhD and Masters courses both as the main or as supplementary text in finance theory, financial decision making and portfolio theory. For researchers, it is a valuable resource being an up to date treatment of topics in the classic books on these topics by Johnathan Ingersoll in 1988, and William Ziemba and Raymond Vickson in 1975 (updated 2 nd edition published in 2006).

Frequently (and often inappropriately) decision making in the work environment has been analyzed and modeled in terms of isolated decisions made by one person. In reality, decision making is a continuous, interpersonal process usually involving several ``decision makers'' aiming at dynamic and cooperative control of the state of affairs at work. Based on original contributions from researchers and research teams, this book provides an urgently needed cognitive approach to models of distributed decision making, exploring the basis for design of decision support systems in various complex, collective, modern work environments. It identifies the state of the art of modeling distributed decision making and the problems imposed by modern high-tech systems. A also formulates promising research avenues.

These authors draw on nearly 50 years of combined teaching and consulting experience to give readers a straightforward yet systematic approach for making estimates about the likelihood and consequences of future events -- and then using those assessments to arrive at sound decisions. The book's real-world cases, supplemented with expository text and spreadsheets, help readers master such techniques as decision trees and simulation, such concepts as probability, the value of information, and strategic gaming; and such applications as inventory stocking problems, bidding situations, and negotiating.

The development of modular product families holds enormous economic potential for companies, as there are always great opportunities but also risks associated with all life phases of a product. However, these fundamental and far-reaching effects inevitably lead to conflicting objectives when defining modular product structures, which makes decision-making in product development particularly complex.Considering relevant theories from decision theory and product family design, this book presents an innovative method to support decision makers in the development of modular product families. The central element of the method is a novel Modularity Decision Dashboard (MDD), which interactively visualizes all decision-relevant data. The findings presented here confirm that applying the method to real-world decision-making problems leads to a more balanced ratio between internal and external variety, and thus significantly contributes to the efficient economic benefit of modularization.

This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.