Table of Contents
Chapter 1 – INTRODUCTION …………………………………………………………………………. 1
1.1. Policymaking under uncertainty ……………………………………………………………………. 1
1.2. Use of mathematical models in policymaking…………………………………………………. 2
1.3. Recent developments of analytical support in model-based policymaking under
deep uncertainty ……………………………………………………………………………………………………. 3
1.4. Gaps to be addressed for supporting model-based policymaking………………………. 6
1.5. Objective and research questions…………………………………………………………………… 7
1.6. Scope and aims of the study ………………………………………………………………………….. 8
References ……………………………………………………………………………………………………………. 9
Chapter 2 – Model-based Policymaking under Uncertainty …………………………………..17
2.1. Introduction ………………………………………………………………………………………………..17
2.2. Methodology: Exploratory Modeling and Analysis (EMA)……………………………….18
2.3. Analytical techniques used together with EMA……………………………………………….19
2.3.1. Feature Scoring………………………………………………………………………………………………………. 19
2.3.2. Classification and Regression Trees (CART)………………………………………………………….. 20
2.3.3. Patient Rule Induction Method (PRIM)…………………………………………………………………. 20
2.4. Case: A simple case on energy transitions …………………………………………………….. 20
2.4.1. Details of the model ………………………………………………………………………………………………. 20
2.4.2. Results without Policy ……………………………………………………………………………………………. 23
2.4.3. Advanced analysis (Feature Scoring, CART, PRIM) ………………………………………………. 25
2.4.4. Results with Static Policy ……………………………………………………………………………………….. 28
2.4.5. Results with Dynamic Policy………………………………………………………………………………….. 29
2.5. Conclusions……………………………………………………………………………………………….. 32
References ………………………………………………………………………………………………………….. 34
Chapter 3 – The Adaptive Robust Design (ARD) Approach ………………………………… 39
3.1. Introduction ………………………………………………………………………………………………. 39
3.2. Methodology: The Adaptive Robust Design (ARD) Approach………………………….41
3.2.1. The Adaptive Policymaking Framework ………………………………………………………………… 41
3.2.2. The Adaptive Robust Design approach …………………………………………………………………. 43
3.3. Case: The ARD Process Elucidated……………………………………………………………… 45
3.3.1. Introduction to the Energy Transitions case ………………………………………………………….. 45
3.3.2. Results without policy ……………………………………………………………………………………………. 47
3.3.3. Basic adaptive policy………………………………………………………………………………………………. 49
3.3.4. Robust policy …………………………………………………………………………………………………………. 50
3.4. Conclusions……………………………………………………………………………………………….. 52
References ………………………………………………………………………………………………………….. 54
Chapter 4 – ARD & Multi-Objective Robust Optimization……………………………………61
4.1. Introduction ………………………………………………………………………………………………..61
4.2. Methodology: Multi-Objective Robust Optimization……………………………………… 63
4.3. Case: An elaborated case on energy transitions ……………………………………………… 67
4.3.1. Results: From ETS toward an adaptive policy ……………………………………………………….. 70
4.3.2. Fine-tuning trigger values……………………………………………………………………………………….. 71
4.4. Conclusions……………………………………………………………………………………………….. 77
References ………………………………………………………………………………………………………….. 79
Chapter 5 – Conclusions, Discussion and Reflection…………………………………………… 89
5.1. Brief integrated summary ……………………………………………………………………………. 89
5.1.1. Answers to key research questions…………………………………………………………………………. 89
5.2. Review of the research………………………………………………………………………………….91
5.2.1. Patient Rule Induction Method (PRIM)…………………………………………………………………. 92
5.2.2. Robustness metrics………………………………………………………………………………………………… 92
5.2.3. Multi-objective optimization ………………………………………………………………………………….. 93
5.2.4. Limitations of the research …………………………………………………………………………………….. 94
5.3. Reflection on the relevance for real-life policy issues ……………………………………… 95
5.4. Future research agenda ………………………………………………………………………………. 96
References ………………………………………………………………………………………………………….. 98
Appendix A. Python Scripts ……………………………………………………………………………….103
Executive Summary……………………………………………………………………………………………..115
Samenvatting………………………………………………………………………………………………………119
Acknowledgements ……………………………………………………………………………………………..123
List of publications………………………………………………………………………………………………125
NGInfra PhD Thesis Series on infrastructures………………………………………………………..127
About the Author…………………………………………………………………………………………………133
Abstract
Policymaking often involves different parties such as policymakers, stakeholders, and analysts each with distinct roles in the process. To assist policymakers, policy analysts help in structuring the problem, designing, and evaluating policy alternatives. Analysts face many challenges, like complexity and uncertainty in a system of interest, while supporting the policymaking process. Frequently, analysts rely on mathematical models that represent the key features of the system. Assumptions made during modelling introduce a significant level of uncertainty in the models, and forecasting based on models is therefore always bound by this uncertainty. Instead of focusing on limited best-estimate predictions under uncertainty, exploring a plethora of plausible futures by using mathematical models can help supporting decision-making. In current practice, uncertainty analysis for decision-making is mostly limited to technical and shallow uncertainties but not focused on deep uncertainty. This thesis contributes to a solution for enhanced handling of deep uncertainty to support policymaking. We have developed a new methodological approach for improving analytical support for policymaking under deep uncertainty, and demonstrated each analytical advancement stage with case studies. This thesis proposes to improve analytical support for policymaking to better handle deep uncertainty. Building upon the existing pragmatic practice, a systematic approach for designing adaptive policies under uncertainty is developed. The Adaptive Robust Design (ARD) approach in combination with multi-objective robust optimization will improve the support for policymaking under deep uncertainty. The effectiveness of ARD for developing adaptive robust policies under deep uncertainty is shown by illustrative case studies.
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