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Structuring Decisions Under Deep Uncertainty

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Abstract

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring (also called framing) of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature.

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Fig. 1

Based on a figure in Brown et al. (2012, p. 9)

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Notes

  1. While economists develop decision models mainly for descriptive purposes, philosophers, policy analysts, and others evaluate the merits of those models as normative guides or apply them as such. It is only this normative reading that I address here.

  2. Mitchell (2009, pp. 85–104) and Sprenger (2012) are two exceptions.

  3. Though the literature does also include proposals about how to redistribute probability after minor restructuring of the state space partition (Walker and Dietz 2011; Binmore 2015) and incorporate awareness that a state space is incomplete (Karni and Vierø 2013, 2014).

  4. Bottom-up approaches to partitioning, and the robustness-based decision support frameworks in which they are typically embedded, also have wider consequences for modelling strategies and distribution of resources within climate change modelling intended to inform decision-making (see Dessai and Hulme 2004; Dessai and Sluijs 2007; Weaver et al. 2013).

  5. Bryant and Lempert (2010) use Latin Hypercube sampling within limits given by expert judgement of upper and lower bounds for each dimension in the space.

  6. Here, a scenario is a precisely defined region within the space of external conditions. Other uses of ‘scenario’ in the decision support literature understand scenarios as points within that space (e.g., Schwartz 1996; Rounsevell and Metzger 2010; Carlsen et al. 2013, 2016a, b). Point-scenarios do not partition a space, though they can be understood to structure the decision in a looser sense that is not captured by the notion of structuring used in this paper (establishing the state-consequence matrix).

  7. Bradley and Steele (2015) have in mind a decision model from Walker and Dietz (2011), while §4.1.2 of Hall et al. (2012) gives the flavour of the deciding norms promoted within Robust Decision Making—the broader framework within which scenario discovery has been developed and deployed.

  8. See Walker et al. (2001) and Kwakkel et al. (2010) for more details and additional steps in the Adaptive Policymaking framework. Further developments of the basic ideas of Adaptive Policymaking include a computer-assisted approach to adaptive policy design (Kwakkel et al. 2012, 2015) and an expanded, hybrid approach called Dynamic Adaptive Policy Pathways (Haasnoot et al. 2013; Kwakkel et al. 2015) that is the result of incorporating elements of Adaptive Policymaking into the Adaptation Pathways (Haasnoot et al. 2011, 2012; Ranger et al. 2013) decision support framework.

  9. Case-based decision theory (Gilboa and Schmeidler 2001) is an exception to this and many other generalisations about decision theory.

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Acknowledgements

This work was supported by the Arts and Humanities Research Council through the Managing Severe Uncertainty Project (AH/J006033/1), the Agence Nationale de la Recherche through Decision-Making & Belief Change Under Severe Uncertainty: A Confidence-Based Approach (DUSUCA) (ANR-14-CE29-0003-01), and the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) (GEO-1240507).

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    Casey Helgeson

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Helgeson, C. Structuring Decisions Under Deep Uncertainty. Topoi 39, 257–269 (2020). https://doi.org/10.1007/s11245-018-9584-y

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