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Decision Modeling and Behavior in Complex and Uncertain Environments

  • Book
  • © 2008

Overview

Editors:
  1. Tamar Kugler

    1. Management and Organizations, University of Arizona, Tucson, USA

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  2. J. Cole Smith

    1. Dept. Industrial & Systems Engineering, University of Florida, Gainesville, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Terry Connolly

    1. Eller College of Management, University of Arizona, Tucson, USA

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  4. Young-Jun Son

    1. College of Engineering Dept. Systems & Industrial Engineering, University of Arizona, Tucson, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Interdisciplinary research with new developments
  • Outstanding contributions with state-of-the-art insight and research directions
  • Bridges behavioral modeling and normative analysis
  • Provides rich applications to environments dominated by complexity and uncertainty

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 21)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xii
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  3. Innovations in Behavioral Model Analysis

    1. On Optimal Satisficing: How Simple Policies Can Achieve Excellent Results

      • J. Neil Bearden, Terry Connolly
      Pages 79-97

    2. There Are Many Models of Transitive Preference: A Tutorial Review and Current Perspective

      • Michel Regenwetter, Clintin P. Davis-Stober
      Pages 99-124

    3. Integrating Compensatory and Noncompensatory Decision-Making Strategies in Dynamic Task Environments

      • Ling Rothrock, Jing Yin
      Pages 125-141

    4. Repeated Prisoner's Dilemma and Battle of Sexes Games: A Simulation Study

      • Jijun Zhao, Ferenc Szidarovszky, Miklos N. Szilagyi
      Pages 143-161

  4. Innovations in Descriptive Behavior Models

    1. Individual Values and Social Goals in Environmental Decision Making

      • David H. Krantz, Nicole Peterson, Poonam Arora, Kerry Milch, Ben Orlove
      Pages 165-198

    2. Does More Money Buy You More Happiness?

      • Manel Baucells, Rakesh K. Sarin
      Pages 199-226

    3. Cognitive Biases Affect the Acceptance of Tradeoff Studies

      • Eric D. Smith, Massimo Piatelli-Palmarini, Terry Bahill
      Pages 227-249

  5. Experimental Studies in Behavioral Research

    1. Valuation of Vague Prospects with Mixed Outcomes

      • David V. Budescu, Sara Templin
      Pages 253-275

    2. Collective Search in Concrete and Abstract Spaces

      • Robert L. Goldstone, Michael E. Roberts, Winter Mason, Todd Gureckis
      Pages 277-308

    3. Braess Paradox in the Laboratory: Experimental Study of Route Choice in Traffic Networks with Asymmetric Costs

      • Amnon Rapoport, Tamar Kugler, Subhasish Dugar, Eyran J. Gisches
      Pages 309-337

    4. Non-Euclidean Traveling Salesman Problem

      • John Saalweachter, Zygmunt Pizlo
      Pages 339-358

  6. Back Matter

    Pages 359-364
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Keywords

About this book

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.

Editors and Affiliations

  • Management and Organizations, University of Arizona, Tucson, USA

    Tamar Kugler

  • Dept. Industrial & Systems Engineering, University of Florida, Gainesville, USA

    J. Cole Smith

  • Eller College of Management, University of Arizona, Tucson, USA

    Terry Connolly

  • College of Engineering Dept. Systems & Industrial Engineering, University of Arizona, Tucson, USA

    Young-Jun Son

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