Coping with Uncertainty Modeling and Policy Issues 1st Edition by Kurt Marti, Yuri Ermoliev, Marek Makowski, Georg Pflug ISBN 9783540352624 3540352627

Ongoing global changes bring fundamentally new scientific problems requiring new concepts and tools. A key issue concerns a vast variety of practically irreducible uncertainties, which challenge our traditional models and require new concepts and analytical tools. The uncertainty critically dominates, e.g., the climate change debates. In short, the dilemma is concerned with enormous costs vs. massive uncertainties of potential extreme impacts. Traditional scientific approaches usually rely on real observations and experiments. Yet no sufficient observations exist for new problems, and “pure” experiments and learning by doing may be very expensive, dangerous, or simply impossible. In addition, available historical observations are contaminated by actions, policies. The complexity of new problems does not allow to achieve enough certainty by increasing the resolution of models or by bringing in more links. Hence, new tools for modeling and management of uncertainty are needed, as given in this book which was prepared for an interdisciplinary audience, and addresses open problems, limitations of known approaches, novel methods and techniques, or lessons from the applications of various approaches. Thus, the book contributes to a better understanding between practitioners dealing with the management of uncertainty, and scientists working on either corresponding modeling approaches that can be applied for improving understanding or management of uncertainty.

 

Coping with Uncertainty Modeling and Policy Issues 1st Edition Table of contents:

Part I: Introduction to Uncertainty in Decision Making

1. Introduction to Uncertainty

   • Types of Uncertainty: Aleatory vs. Epistemic
   • Sources and Impacts of Uncertainty in Policy and Decision Making
   • The Role of Uncertainty in Modeling Complex Systems
   • Overview of Techniques for Coping with Uncertainty

2. Fundamentals of Uncertainty Modeling

   • Probability Theory: Basics and Applications
   • Bayesian Inference and Probabilistic Models
   • Decision Trees and Influence Diagrams
   • Sensitivity Analysis and Uncertainty Propagation

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Part II: Quantitative Methods for Modeling Uncertainty

3. Stochastic Modeling Techniques

   • Stochastic Processes and Their Applications
   • Monte Carlo Simulation in Decision Analysis
   • Markov Chains and Dynamic Programming
   • Stochastic Optimal Control Models

4. Robust Optimization and Decision Making

   • Introduction to Robust Optimization
   • Decision Making Under Uncertainty: Min-Max and Min-Max-Regret Approaches
   • Applications in Finance, Engineering, and Economics
   • Case Studies: Portfolio Management and Supply Chain Optimization

5. Fuzzy Logic and Uncertainty

   • Basics of Fuzzy Sets and Fuzzy Logic
   • Fuzzy Decision Making and Modeling
   • Hybrid Approaches: Fuzzy Sets and Probability
   • Applications in Risk Assessment and Policy Making

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Part III: Uncertainty in Policy and Decision Analysis

6. Uncertainty in Economic and Environmental Policy

   • The Role of Uncertainty in Environmental Modeling
   • Economic Decision Making Under Uncertainty
   • Climate Change and Environmental Risk Assessment
   • Policy Design and Uncertainty Management

7. Uncertainty in Health and Safety Decision Making

   • Risk Assessment Models for Health and Safety
   • Uncertainty in Public Health Policy
   • The Role of Uncertainty in Safety Regulations
   • Case Studies: Epidemic Control and Environmental Health

8. Uncertainty in Engineering and Infrastructure

   • Engineering Systems Design Under Uncertainty
   • Reliability and Safety Engineering Models
   • Infrastructure Investment and Risk Assessment
   • Applications in Transport, Energy, and Civil Engineering

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Part IV: Advanced Methods and Applications

9. Scenario Analysis and Simulation

   • Scenario Planning in Decision Making
   • Applications of Simulation Models in Uncertainty Analysis
   • Risk and Uncertainty in Strategic Management
   • Case Studies in Corporate Risk Management

10. Game Theory and Strategic Uncertainty

    • Introduction to Game Theory in Uncertainty
    • Non-Cooperative vs. Cooperative Games Under Uncertainty
    • Bayesian Games and Decision Making
    • Applications in Economics, Security, and Policy

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Part V: Practical Approaches and Future Directions

11. Computational Methods for Uncertainty Management

    • Computational Tools and Software for Uncertainty Analysis
    • High-Performance Computing in Risk and Decision Modeling
    • Optimization Algorithms for Large-Scale Uncertainty Problems
    • Software Applications and Case Studies

12. Future Challenges in Uncertainty Modeling and Policy

    • Emerging Trends in Uncertainty Research
    • Integrating Machine Learning and AI with Uncertainty Models
    • Policy Implications in an Uncertain Global Environment
    • The Future of Uncertainty Management in Complex Systems

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