Setting Environmental Policy When Experts Disagree

Abstract

How can a decision-maker assess the potential of environmental policies when a group of experts provides divergent estimates on their effectiveness? To address this question, we propose and analyze a variant of the well-studied α-maxmin model in decision theory. In our framework, and consistent to the paper's empirical focus on renewable-energy R&D investment, experts' subjective probability distributions are allowed to be action-dependent. In addition, the decision maker constrains the sets of priors to be considered via a parsimonious measure of their distance to a benchmark "average" distribution that grants equal weight to all experts. While our model is formally rooted in the decision-theoretic framework of Olszewski [37], it may also be viewed as a structured form of sensitivity analysis. We apply our framework to original data from a recent expert elicitation survey on solar energy. The analysis suggests that more aggressive investment in solar energy R&D is likely to yield significant dividends even, or rather especially, after taking expert ambiguity into account.

Future Prospects for Energy Technologies: Insights from Expert Elicitations

“…An emerging literature argues in favor of aggregating expert judgments in the context of a specific decision problem through the use of non-traditional decision rules. For example, there are a number of papers applying Ambiguity Aversion frameworks to the climate change problem (Athanassoglou and Bosetti 2015;Millner et al 2013;Millner 2014, Berger et al 2015). A similar approach is Robust Optimization (Gabrel et al 2014, Bertsimas et al 2011Mortazavi-Naeini 2015).…”

Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology

“…Conversely, inappropriately using the Vincent average may underestimate these probabilities, leaving decision makers unprepared. Identifying qualitative and quantitative decision outcomes across a range of aggregation methods will reduce the importance of selecting a single best method [ 52 ].…”

“…Here, and throughout, we use ‘between-prediction’ uncertainty to refer to the uncertainty captured across the set of independent predictions; this does not imply predictions are related in any way. The LOP treats individual predictions as alternative possible futures across which uncertainty should be retained [ 52 ], yielding an aggregate distribution that superimposes the shapes of each individual prediction. The Vincent average assumes individual predictions are each an imperfect representation of a single distribution of interest, appropriately capturing uncertainty despite random noise across predictions.…”

Robust portfolio decision analysis: An application to the energy research and development portfolio problem

“…This leads to a question of how to synthesize the conflicting experts' views to support decision making; and whether subjective expected utility is the right approach. Recent work on climate change (Heal and Millner 2014;Millner et al 2013) has argued for including ambiguity aversion into the objective function, but there is no consensus on which non-expected utility decision rules should be used (Borgonovo and Marinacci 2015); see Millner et al (2013), Loulou and Kanudia (1999), Woodward and Bishop (1997), Athanassoglou and Bosetti (2015) for various applications to climate change. This is not a minor theoretical point: in the broad context of climate change policy-making Drouet et al (2015) show that accounting for ambiguity aversion can lead to much more stringent mitigation strategies.…”