Exploratory modeling and analysis: a promising method to deal with deep uncertainty

Faced with policy problems with high stakes, decisionmakers have increasingly recognized the importance of appropriately handling uncertainties. The nature of policy problems, however, is changing. Of particular concern are policy problems involving deep uncertainty when analysts do not know or the parties to a decision cannot agree upon (1) the appropriate conceptual models to describe interactions among a system's variables, (2) the probability distributions to represent uncertainty about key parameters in the models, and/or (3) how to value the desirability of alternative outcomes. Exploratory Modeling and Analysis (EMA) is an analytical, model-based method for dealing with deep uncertainty. One of the foundations of EMA is the idea of exploring multiple hypotheses about the system of interest by broadening the assumptions underlying the system model. EMA explores multiple hypotheses about the system by means of computational experiments. A computational experiment is a single computer run of the system model using one set of assumptions. Each run is treated as a deterministic hypothesis about the system of interest. One can explore the system's behavior by asking for each run, what if the hypothesis was correct. Broadening the assumptions of a system model and exploring the resulting behavior poses some major challenges. These challenges include, among others, how to sample from an (almost) infinite uncertainty space, and how to digest and present the information from the many computer runs (thousands to hundreds of thousands) in a way that is useful for policy design. This dissertation addresses these challenges. It uses three policy analysis cases as a testing ground for the application, development, and evaluation of EMA: (1) a real options analysis of a power plant investment decision, (2) the implementation of Intelligent Speed Adaptation (a technological solution for improving road safety), and (3) the analysis of policies to mitigate carbon emissions in the Dutch household sector. These three applications demonstrate a number of insights that can be obtained from EMA that complement those that can be obtained from traditional policy analysis methods. First, EMA can provide insights into the boundaries between the success and failure of a policy, which can help to identify "landmines" for the policy. Second, EMA can help identify the different sets of exogenous, system and policy assumptions that can lead to the achievement of a given policy goal, which can support different parties in negotiating a common policy. Third, EMA can provide insights into the robustness of a policy across the uncertainty space, which may enable policy implementation to begin despite the uncertainties. Finally, EMA can provide a policy menu that shows which policy performs best in which circumstances, which can support policy adaptation over time. In addition to these insights into the added value of EMA, the dissertation makes a number of original contributions to the EMA methodology, in particular regarding the sampling method, the analysis of the data generated, and the presentation of the insights obtained.