Abstract
This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers working on decision-making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision-Making developed by Robert Lempert and colleagues at RAND. We discuss two challenges for robust satisficing: whether the norm might derive its plausibility from an implicit appeal to probabilistic representations of uncertainty of the kind that deep uncertainty is supposed to preclude; and whether there is adequate justification for adopting a satisficing norm, as opposed to an optimizing norm that is sensitive to considerations of robustness. We discuss decision-theoretic and voting-theoretic motivations for robust satisficing, and use these motivations to select among candidate formulations of the robust satisficing norm.
Highlights
We as a species confront a range of profound challenges to our long-term survival and flourishing, including nuclear weapons, climate change, and risks from biotechnology and artificial intelligence (Bostrom & Cirkovic, 2008; Ord, 2020)
Insofar as robust satisficing is appealing as a decision norm, we think there is a strong case to be made that its appeal has to reflect certain a priori intuitions about normative criteria for choice under deep uncertainty, and we focus principally on the nature and credibility of the intuitive considerations that may be taken to support robust satisficing as a decision norm, starting with the intuitive case for emphasizing robustness in the context of decision-making under deep uncertainty
This takes the form of an intuitive objection to the application of subjective expected utility theory to problems involving deep uncertainty. This objection receives varied expression in the Robust Decision Making (RDM) literature. We show that it generalizes to other decision norms besides subjective expected utility maximization, and serves as a flashpoint for the worry, mentioned in Sect. 2.3., that robust satisficing invokes a form of second-order probability theory
Summary
We as a species confront a range of profound challenges to our long-term survival and flourishing, including nuclear weapons, climate change, and risks from biotechnology and artificial intelligence (Bostrom & Cirkovic, 2008; Ord, 2020). The DMDU community has developed a range of tools for decision support designed to address this need, including Robust Decision Making (RDM) (Lempert, Popper, & Bankes, 2003), Dynamic Adaptive Policy Pathways (Haasnoot, Kwakkel, Walker, & ter Maat, 2013), and Info-Gap Decision Theory (Ben-Haim, 2006). These tools are not faithfully characterized as decision theories, at least not in the sense in which philosophers would most naturally understand that term.
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