Optimal R&D investment is defined by deep uncertainty that can only partially be addressed through historical data. Thus, expert judgments expressed as subjective probability distributions are seen as an alternative way of assessing the potential of new technologies. In this paper we propose a...

Optimal R&D investment is defined by deep uncertainty that can only partially be addressed through historical data. Thus, expert judgments expressed as subjective probability distributions are seen as an alternative way of assessing the potential of new technologies. In this paper we propose a...

How should a decision-maker allocate R&D funds when a group of experts provides divergent estimates on a technology's potential effectiveness? To address this question, we propose a simple decision-theoretic framework that takes into account ambiguity over the aggregation of expert opinion and a...

How should a decision-maker allocate R&D funds when a group of experts provides divergent estimates on a technology's potential effectiveness? To address this question, we propose a simple decision-theoretic framework that takes into account ambiguity over the aggregation of expert opinion and a...

How should a decision-maker assess the potential of an investment when a group of experts provides strongly divergent estimates on its expected payoff? To address this question, we propose and analyze a variant of the well-studied -maxmin model in decision theory. In our framework, and...

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 $\alpha$-maxmin model in decision theory. In our framework, and...