Abstract

This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers whose research addresses the topic of 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 decision-theoretic and voting-theoretic motivations for robust satisficing, then use these motivations to select among candidate formulations of the robust satisficing norm. We also discuss two challenges for robust satisficing: whether the norm might in fact 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.

Introduction

We as a species confront a range of profound challenges to our long-term survival and flourishing, including nuclear weapons, climate change, and existential risks from biotechnology and artificial intelligence (Bostrom and Cirkovic 2008; Ord 2020). Policy decisions made in answer to these threats will impact the development of human civilization hundreds or thousands of years into the future. In the extreme, they will determine whether humanity has any future at all.

Unfortunately, severe uncertainty clouds our efforts to forecast the impact of policy decisions over long-run timescales. The term ‘deep uncertainty’ has been adopted in operations research and engineering to denote decision problems in which our evidence is profoundly impoverished in this way (Marchau et al. 2019). The third edition of the Encyclopedia of Operations Research and Management Science defines ‘deep uncertainty’ as arising in situations “in which one is able to enumerate multiple plausible alternatives without being able to rank the alternatives in terms of perceived likelihood” or “what is known is only that we do not know.” (Walker, Lempert, and Kwakkel 2013: 397) A multidisciplinary professional organization dedicated to research on decisionmaking under deep uncertainty (DMDU) was established in December 2015.

Given the paucity of evidence that could constrain probability assignments in these contexts, analysts associated with the study of DMDU argue that orthodox approaches to decision analysis based on expected value maximization are unhelpful. Thus, Ben-Haim (2006: 11) suggests that “[d]espite the power of classical decision theories, in many areas such as engineering, economics, management, medicine and public policy, a need has arisen for a different format for decisions based on severely uncertain evidence.” The DMDU community has developed a range of tools for decision support designed to address this need, including Robust Decision Making (RDM) (Lempert, Popper, and Bankes 2003), Dynamic Adaptive Policy Pathways (DAPP) (Haasnoot et al. 2013), and Info-Gap Decision Theory (IG) (Ben-Haim 2006).

In spite of what a name like ‘Info-Gap Decision Theory’ might suggest, these tools are not faithfully characterized as decision theories, at least not in the sense in which philosophers would most naturally understand that term. They primarily comprise procedures for framing and exploring decision-problems. Sometimes their proponents seem reluctant to suggest normative criteria for solving decision-problems once suitably framed. Thus, Lempert et al. (2006: 523) write: “RDM does not determine the best strategy. Rather, it uses information in computer simulations to reduce complicated, multidimensional deeply uncertain problems to a small number of key trade-offs for decision makers to ponder.”

This is the aspect of DMDU research highlighted in a recent paper by Helgeson (2020), one of the few papers by philosophers to examine research in this area in depth. Whereas decision theory, as practiced by philosophers, characteristically aims to instruct decision makers in how to solve a decision problem that is appropriately framed, Helgeson argues that DMDU research focuses principally on how to frame the decision problem in the first place, and so provides “a counterbalancing influence to decision theory’s comparative focus on the choice task.” (267)

DMDU researchers nonetheless also provide suggestions for normative criteria appropriate to the solution of the choice task. In particular, they tend to advocate a norm of robust satisficing. Lempert (2002: 7309) states that DMDU decision support tools “facilitate the assessment of alternative strategies with criteria such as robustness and satisficing rather than optimality. The former are particularly appropriate for situations of deep uncertainty.” In the same vein, Schwartz, Ben-Haim, and Dasco (2011: 213) argue that “[t]here is a quite reasonable alternative to utility maximization. It is maximizing the robustness to uncertainty of a satisfactory outcome, or robust satisficing. Robust satisficing is particularly apt when probabilities are not known, or are known imprecisely.” In a sense, it is unsurprising that conceptual innovations in decision framing should be accompanied by the proposal of novel decision criteria, as any reasonable decision support tool must make some assumptions about what constitutes a good decision in order to determine what information should be emphasized in framing the problem at hand.

Our interest in this paper is in robust satisficing as a norm for decision making under deep uncertainty. There has been remarkably little philosophical discussion of robust satisficing as a candidate decision norm, given its popularity among those at the coalface working on DMDU. Our aim in this paper is to open a dialogue between our two research communities. In order to simplify the discussion, we focus specifically on robust satisficing in the context of RDM and set aside other approaches, such as IG. The key questions on which we focus are how to characterize robust satisficing as a decision norm in the context of RDM, its relationship to more familiar decision criteria discussed among philosophers and economists, and whether in fact it is a rationally defensible norm that is suited to decision making under deep uncertainty.

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