Decision Making under Deep Uncertainty 

Dr. Jan Kwakkel (TUDelft) shared some insights on decision making under deep uncertainty.
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Dating back to the aftermath of the second world war, there has been a growing use of models to support public policy decision making. Starting with the emergence of operations, or operational, research, which morphed into systems analysis, and later policy analysis, we now live in an era where model-based analyses play a major role in public deliberation. In parallel to the rise of model-based decision support, there have been various critiques of it as well: public policy problems are wicked, they are societal messes, models are just useless arithmetic, public decision making requires post-normal science, and science in public policy makes controversies worse. At least in part, these critiques arise out of the use of mathematical optimization and the associated reliance on heroic assumptions to make real world policy problems analyzable. However, real world policy problems arise in complex systems, characterized by non-linearity, emergence, and co-evolution. Policy analysis has to shift to using techniques suitable for analyzing such systems, rather than forcing them into the procrustean bed of techniques that historically have been used. In this talk, I will explore the use of computational experimentation with simulation models for supporting public policy decision making as one direction for aligning model-based decision support for public policy with the intrinsically complex nature of the systems that are being studied.