Synthese 200 (1):1-26 (2022)

Authors
Andreas Mogensen
Oxford University
David Thorstad
Oxford University
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.
Keywords Satisficing  Robust decision-making  Deep uncertainty
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DOI 10.1007/s11229-022-03566-5
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References found in this work BETA

The Foundations of Statistics.Leonard J. Savage - 1954 - Wiley Publications in Statistics.
Moral Thinking: Its Levels, Method, and Point.R. M. Hare (ed.) - 1981 - Oxford: Oxford University Press.
Risk, Uncertainty and Profit.Frank Knight - 1921 - University of Chicago Press.
Decision Theory with a Human Face.Richard Bradley - 2017 - Cambridge University Press.

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