This text is devoted to examining new research at the interface of operations research, behavioral and cognitive sciences, and decision analysis. In these 14 self-contained chapters, four themes emerge, providing the reader with a variety of perspectives both theoretic and applied to meet the challenges of devising models to understand the decision-making process.

The main broad topics include:

·the integration of decision analysis and behavioral models

·innovations in behavioral models

·exploring descriptive behavior models

·experimental studies

From the cognitive behaviorist who collects empirical evidence as to how people make decisions to the engineer and economist who are the consumers of such understanding, the reader encounters the familiar Traveling Salesman Problem and Prisoner's dilemma, how agricultural decisions are made in Argentina's Pampas region, and some social goals that come into play as an element of rational decision-making.

Decision Modeling and Behavior in Complex and Uncertain Environments will benefit graduate students, mathematicians, engineers, psychologists, and other scientists.

Zusammenfassung
On February 27 and 28 of 2006, the University of Arizona held a workshop entitled, Decision Modeling and Behavior in Uncertain and Complex En- ronments, sponsored by the Air Force O?ce of Scienti?c Research under a Multidisciplinary University Research Initiative (MURI) grant. The purpose of the workshop was to assemble preeminent researchers studying problems at the interface of behavioral and cognitive science, decision analysis, and operations research. This book is a compilation of 14 chapters based on the presentations given during the workshop. These contributions are grouped into four general areas, which describe in some detail the challenges in conducting novel research in this ?eld. Part One is concerned with the need for integrating decision analysis and behavioral models. Robert T. Clemen discusses how the ?elds of behavioral - search and decision analysis have diverged over time, and makes a compelling case to establish new links between the disciplines. He recommends leveraging lessons learned from behavioral studies within prescriptive decision analysis studies and evaluating the practical impact of those prescriptive techniques in helping decision makers achieve their objectives. Jenna L. Marquard and Stephen M. Robinson address eleven common traps that face decision model analysts and users. An understanding of these traps leads to an understanding of modeling features that either help or hurt the decision-making process. The authors link theory and practice by examining a set of case studies across a diverse array of model scenarios, and provide a checklist of recommendations for analysts confronted by these eleven traps.

Inhalt
Integrating Decision Analysis and Behavioral Models.- Improving and Measuring the Effectiveness of Decision Analysis: Linking Decision Analysis and Behavioral Decision Research.- Reducing Perceptual and Cognitive Challenges in Making Decisions with Models.- Agricultural Decision Making in the Argentine Pampas: Modeling the Interaction between Uncertain and Complex Environments and Heterogeneous and Complex Decision Makers.- Innovations in Behavioral Model Analysis.- On Optimal Satisficing: How Simple Policies Can Achieve Excellent Results.- There Are Many Models of Transitive Preference: A Tutorial Review and Current Perspective.- Integrating Compensatory and Noncompensatory Decision-Making Strategies in Dynamic Task Environments.- Repeated Prisoner's Dilemma and Battle of Sexes Games: A Simulation Study.- Innovations in Descriptive Behavior Models.- Individual Values and Social Goals in Environmental Decision Making.- Does More Money Buy You More Happiness?.- Cognitive Biases Affect the Acceptance of Tradeoff Studies.- Experimental Studies in Behavioral Research.- Valuation of Vague Prospects with Mixed Outcomes.- Collective Search in Concrete and Abstract Spaces.- Braess Paradox in the Laboratory: Experimental Study of Route Choice in Traffic Networks with Asymmetric Costs.- Non-Euclidean Traveling Salesman Problem.