Decision Making under Deep Uncertainty : : From Theory to Practice.

• Author: Marchau, Vincent A. W. J.
• TeilnehmendeR: Walker, Warren E.; Bloemen, Pieter J. T. M.; Popper, Steven W.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2019.; ©2019.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (408 pages)

Table of Contents

• Intro
• Preface
• Acknowledgements
• Contents
• 1 Introduction
• 1.1 The Need for Considering Uncertainty in Decisionmaking
• 1.2 A Framework for Decision Support
• 1.3 Dealing with Uncertainty in Decisionmaking
• 1.4 Decisionmaking Under Deep Uncertainty
• 1.5 Generic Elements of DMDU Approaches-A Framework
• 1.6 An Introduction to the DMDU Tools and Approaches
• 1.7 Structure of the Book
• References
• DMDU Approaches
• 2 Robust Decision Making (RDM)
• 2.1 Introduction
• 2.2 RDM Foundations
• 2.3 RDM Process
• 2.4 Tools
• 2.5 Example: Carrots and Sticks for New Technology
• 2.5.1 Frame the Analysis
• 2.5.2 Perform Exploratory Uncertainty Analysis
• 2.5.3 Choose Initial Actions and Contingent Actions
• 2.5.4 Iterate and Re-Examine (RDM Steps 2, 3, and 5)
• 2.6 Recent Advances and Future Challenges
• References
• 3 Dynamic Adaptive Planning (DAP)
• 3.1 Introduction
• 3.2 The DAP Approach
• 3.3 A DAP Illustration: Strategic Planning for Schiphol Airport
• 3.4 Implementation and Adaptation
• 3.5 Conclusions
• References
• 4 Dynamic Adaptive Policy Pathways (DAPP)
• 4.1 Introduction
• 4.2 The DAPP Approach
• 4.3 A DAPP Illustration: Navigation along the Waas River
• 4.4 Under What Conditions Is This Approach Useful?
• 4.5 Recent Advances
• 4.6 Links with Other DMDU Approaches
• 4.7 Future Challenges
• References
• 5 Info-Gap Decision Theory (IG)
• 5.1 Info-Gap Theory: A First Look
• 5.2 IG Robustness: Methodological Outline
• 5.2.1 Three Components of IG Robust Satisficing
• 5.2.2 IG Robustness
• 5.2.3 Prioritization of Competing Decisions
• 5.2.4 How to Evaluate Robustness: Qualitative or Quantitative?
• 5.3 IG Robustness: A Qualitative Example
• 5.3.1 Five Conceptual Proxies for Robustness
• 5.3.2 Simple Qualitative Example: Nuclear Weapon Safety.
• 5.4 IG Robustness and Opportuneness: A Quantitative Example
• 5.4.1 IG Robustness
• 5.4.2 Discussion of the Robustness Results
• 5.4.3 IG Opportuneness
• 5.4.4 Discussion of Opportuneness Results
• 5.4.5 An Innovation Dilemma
• 5.4.6 Functional Uncertainty
• 5.5 Conclusion and Future Challenges
• References
• 6 Engineering Options Analysis (EOA)
• 6.1 Introduction
• 6.2 Methodology of Engineering Options Analysis
• 6.2.1 Setting the Scene
• 6.2.2 Definition of an Option
• 6.2.3 Main Steps of Analysis
• 6.2.4 Details of Each Step
• 6.3 A Simple Example: A Parking Garage
• 6.4 Contrasting Engineering Options Analysis with Real Options Analysis
• 6.4.1 Different Professional Contexts
• 6.4.2 Some Specific Differences
• 6.5 Contrasting Engineering Options Analysis with Other Approaches in This Book
• 6.5.1 Engineering Options Analysis as a Planning Approach
• 6.5.2 Engineering Options Analysis as a Computational Decision-Support Tool
• 6.6 Conclusions
• References
• DMDU Applications
• 7 Robust Decision Making (RDM): Application to Water Planning and Climate Policy
• 7.1 Long-Term Planning for Water Resources and Global Climate Technology Transfer
• 7.2 Review of Robust Decision Making
• 7.2.1 Summary of Robust Decision Making
• 7.3 Case Study 1: Using RDM to Support Long-Term Water Resources Planning for the Colorado River Basin
• 7.3.1 Decision Framing for Colorado River Basin Analyses
• 7.3.2 Vulnerabilities of Current Colorado River Basin Management
• 7.3.3 Design and Simulation of Adaptive Strategies
• 7.3.4 Evaluating Regret of Strategies Across Futures
• 7.3.5 Updating Beliefs About the Future to Guide Adaptation
• 7.3.6 Robust Adaptive Strategies, and Implementation Pathways
• 7.3.7 Need for Transformative Solutions
• 7.4 Case Study 2: Using RDM to Develop Climate Mitigation Technology Diffusion Policies.
• 7.4.1 Decision Framing for Climate Technology Policy Analysis
• 7.4.2 Modeling International Technological Change
• 7.4.3 Evaluating Policies Across a Wide Range of Plausible Futures
• 7.4.4 Key Vulnerabilities of Climate Technology Policies
• 7.4.5 Developing a Robust Adaptive Climate Technology Policy
• 7.5 Reflections
• References
• 8 Dynamic Adaptive Planning (DAP): The Case of Intelligent Speed Adaptation
• 8.1 Introduction to the Approach
• 8.2 Introduction to the Case
• 8.3 Reason for Choosing the DAP Approach
• 8.4 Methods for Applying DAP
• 8.5 Setting up a DAP Workshop on ISA Implementation
• 8.6 Results of the DAP-ISA Workshop
• 8.7 Evaluation of the DAP Approach
• 8.8 Lessons Learned About the Process of Developing Dynamic Adaptive Plans
• 8.9 Conclusions
• References
• 9 Dynamic Adaptive Policy Pathways (DAPP): From Theory to Practice
• 9.1 Introduction to the Case
• 9.2 Reason for Choosing DAPP
• 9.3 Setup of Approach for Case Study in Practice
• 9.4 Applying DAPP in Practice
• 9.5 Results of Applying the Approach
• 9.6 Reflections (Lessons Learned) for Practice and Theory
• References
• 10 Info-Gap (IG): Robust Design of a Mechanical Latch
• 10.1 Introduction
• 10.2 Application of Info-Gap Robustness for Policymaking
• 10.3 Formulation for the Design of a Mechanical Latch
• 10.4 The Info-Gap Robust Design Methodology
• 10.5 Assessment of Two Competing Designs
• 10.6 Concluding Remarks
• References
• 11 Engineering Options Analysis (EOA): Applications
• 11.1 Case Study 1: Liquid Natural Gas in Victoria State, Australia
• 11.2 Setup of the EOA Approach for the LNG Case Study
• 11.2.1 Design Alternatives
• 11.2.2 Parameter Values
• 11.2.3 Characterization of Sources of Uncertainty
• 11.3 Results from Applying the EOA Approach to the LNG Case Study
• 11.3.1 Fixed Design.
• 11.3.2 Performance of Fixed Design Under Uncertainty
• 11.3.3 Flexible Strategies
• 11.3.4 Flexible Strategy-Timing (But No Learning)
• 11.3.5 Flexible Strategy-Timing and Location (But No Learning)
• 11.3.6 Flexible Strategy-Learning
• 11.3.7 Learning Combined with Economies of Scale
• 11.3.8 Multi-criteria Comparison of Strategies
• 11.3.9 Guidance from Applying EOA to This Case
• 11.4 Case Study 2: Water Management Infrastructure in the Netherlands: IJmuiden Pumping Station
• 11.5 Setup of the EOA Approach for the IJmuiden Pumping Station
• 11.5.1 Characterization of Sources of Uncertainty
• 11.5.2 Design Alternatives
• 11.5.3 Details of the Analysis
• 11.6 Results from Applying the EOA Approach to the IJmuiden Pumping Station
• 11.6.1 Inland Water Level Regulation Function
• 11.6.2 Flood Defense Function
• 11.6.3 Guidance from Applying EOA to This Case
• 11.7 Conclusions and Reflections for Practice and Theory
• References
• DMDU-Implementation Processes
• 12 Decision Scaling (DS): Decision Support for Climate Change
• 12.1 Introduction
• 12.2 Technical Approach
• 12.2.1 Overview
• 12.2.2 Step 1. Decision Framing
• 12.2.3 Step 2. Climate Stress Test
• 12.2.4 Step 3. Estimation of Climate-Informed Risks
• 12.3 Case Study: Assessing Climate Risks to the Water Supply for Colorado Springs, Colorado, USA
• 12.3.1 Step 1: Decision Framing
• 12.3.2 Step 2: Climate Stress Test
• 12.3.3 Step 3. Estimation of Climate-Informed Risks
• 12.4 Conclusions
• References
• 13 A Conceptual Model of Planned Adaptation (PA)
• 13.1 Introduction
• 13.2 Planned Adaptation Cases
• 13.2.1 Particulate Matter Standards
• 13.2.2 Delta Management in the Netherlands
• 13.2.3 Air Transportation Safety
• 13.2.4 Internet Number Delegation
• 13.3 Generalizing Elements of Planned Adaptation
• 13.3.1 Disentangling Primary and Secondary Rules.
• 13.3.2 Triggers and Events
• 13.3.3 Evaluation
• 13.4 Conclusions and Ongoing Work
• 13.4.1 Combinations of Adaptive Capabilities
• 13.4.2 Planning and Designing for Adaptation
• 13.4.3 Implications for Future Study
• References
• 14 DMDU into Practice: Adaptive Delta Management in The Netherlands
• 14.1 Organizational Aspects of Putting a DMDU Approach into Practice
• 14.2 The Case Study: Adaptive Delta Management
• 14.3 Phase I: Prior to the Start of ADM (Politicization and De-politicization)
• 14.3.1 Build a Constituency for Change that Will Allow Political Commitments to Be Made
• 14.3.2 Develop Attractive and Plausible Perspectives: The Second Delta Committee
• 14.3.3 Enhance Public Awareness and Political Commitment
• 14.3.4 Stabilize Processes
• Build Trust and Continuity into the Structure of the programme
• 14.4 Phase II: Developing Strategies and Decisionmaking
• 14.4.1 Create a Narrative that Mobilizes Administrative and Political Decisionmakers
• 14.4.2 Involve All Parties in Developing an Approach for Dealing with Deep Uncertainty
• 14.4.3 Evaluate and Upgrade the Approach Regularly
• 14.4.4 Operationalize the DMDU Approach
• 14.5 Phase III: Elaborating, Implementing, and Adjusting Strategies
• 14.5.1 Plan the Adaptation
• 14.5.2 Organize the Adaptation
• 14.5.3 Rethink Monitoring and Evaluation
• 14.6 Conclusions, Reflections, and Outlook
• References
• DMDU-Synthesis
• 15 Supporting DMDU: A Taxonomy of Approaches and Tools
• 15.1 Introduction
• 15.2 Key Ideas
• 15.2.1 Exploratory Modeling
• 15.2.2 Adaptive Planning
• 15.2.3 Decision Support
• 15.3 A Taxonomy of Approaches and Tools for Supporting Decision Making Under Deep Uncertainty
• 15.3.1 Policy Architecture
• 15.3.2 Generation of Policy Alternatives and Generation of Scenarios
• 15.3.3 Robustness Metrics
• 15.3.4 Vulnerability Analysis.
• 15.4 Application of the Taxonomy.