Cheap Talk with Multiple Experts and Uncertain Biases

A decision maker solicits information from two partially informed experts and then makes a choice under uncertainty. The experts can be either moderately or extremely biased relative to the decision maker, which is their private information. I investigate the incentives of the experts to share their private information with the decision maker and analyze the resulting effects on information transmission. I show that it may be optimal to consult a single expert rather than two experts if the decision maker is sufficiently concerned about taking advice from extremely biased experts. In contrast to what may be expected, this result suggests that getting a second opinion may not always be helpful for decision making.

If It Is Surely Better, Do It More? Implications for Preferences Under Ambiguity

Consider a canonical problem in choice under uncertainty: choosing from a convex feasible set consisting of all (Anscombe–Aumann) mixtures of two acts f and g, [Formula: see text]. We propose a preference condition, monotonicity in optimal mixtures, which says that surely improving the act f (in the sense of weak dominance) makes the optimal weight(s) on f weakly higher. We use a stylized model of a sales agent reacting to incentives to illustrate the tight connection between monotonicity in optimal mixtures and a monotone comparative static of interest in applications. We then explore more generally the relation between this condition and preferences exhibiting ambiguity-sensitive behavior as in the classic Ellsberg paradoxes. We find that monotonicity in optimal mixtures and ambiguity aversion (even only local to an event) are incompatible for a large and popular class of ambiguity-sensitive preferences (the c-linearly biseparable class. This implies, for example, that maxmin expected utility preferences are consistent with monotonicity in optimal mixtures if and only if they are subjective expected utility preferences. This incompatibility is not between monotonicity in optimal mixtures and ambiguity aversion per se. For example, we show that smooth ambiguity preferences can satisfy both properties as long as they are not too ambiguity averse. Our most general result, applying to an extremely broad universe of preferences, shows a sense in which monotonicity in optimal mixtures places upper bounds on the intensity of ambiguity-averse behavior. This paper was accepted by Manel Baucells, decision analysis.

Decision Making and Equilibrium under Uncertainty

Rational choice under uncertainty for individuals with incomplete preferences is examined for three choice environments: the standard financial portfolio model, producer choice in the absence of financial markets, and producer choice in the presence of financial markets. Each problem is analyzed using distance functions and the zero-maximum (zero-minimum) principle. General equilibrium is analyzed using the zero-maximum (zero-minimum) principle and related to equilibrium representation in a nonstochastic setting. Choice under uncertainty for individuals with complete preferences is examined

Choice under uncertainty

This chapter examines how many important consumption decisions concern choices, the consequences of which are uncertain at the time the choice is made. It begins with the theory of von Neumann–Morgenstern expected utility. In this theory, uncertain prospects are modeled as probability distributions over a given set of prizes. That is, the probabilities of various prizes are given as part of the description of the object. The chapter then takes up the special case where the prizes are amounts of money; then one is able to say a bit more about the nature of the utility function that represents preferences. It discusses a few applications of this theory to the topic of market demand. Finally, the chapter turns to a richer theory, where uncertain prospects are functions from “states of nature” to prizes, and where probabilities arise subjectively, as part of the representation of a consumer's preferences.

Ship Emission Mitigation Strategies Choice Under Uncertainty

Various mitigation strategies have been proposed to reduce the CO2 emissions from ships, which have become a major contributor to global emissions. The fuel consumption under different mitigation strategies can be evaluated based on two data sources, real data from the real ship systems and simulated data from the simulation models. In practice, the uncertainties in the obtained data may have non-negligible impacts on the evaluation of mitigation strategies. In this paper, a Gaussian process metamodel-based approach is proposed to evaluate the ship fuel consumption under different mitigation strategies. The proposed method not only can incorporate different data sources but also consider the uncertainties in the data to obtain a more reliable evaluation. A cost-effectiveness analysis based on the fuel consumption prediction is then applied to rank the mitigation strategies under uncertainty. The accuracy and efficiency of the proposed method is illustrated in a chemical tanker case study, and the results indicate that it is critical to consider the uncertainty, as they can lead to suboptimal decisions when ignored. Here, trim optimisation is ranked more effective than draft optimisation when the uncertainty is ignored, but the reverse is the case when the uncertainty in the estimations are fully accounted for.

Forming COVID-19 Policy Under Uncertainty

This paper presents my thinking and concerns about formation of COVID-19 policy. Policy formation must cope with substantial uncertainties about the nature of the disease, the dynamics of transmission, and behavioral responses. Data uncertainties limit our knowledge of the past trajectory and current state of the pandemic. Data and modeling uncertainties limit our ability to predict the impacts of alternative policies. I explain why current epidemiological and macroeconomic modeling cannot deliver realistically optimal policy. I describe my recent work quantifying basic data uncertainties that make policy analysis difficult. I discuss approaches for policy choice under uncertainty and suggest adaptive policy diversification.

Optimising nudges and boosts for financial decisions under uncertainty

Behavioural interventions that directly influence decision-making are increasingly popular policy tools. Two prominent interventions used are nudges, which promote an optimal choice without restricting options, and boosts, which promote individual capabilities to make more informed choices. Direct comparison is a critical step toward understanding the populations and contexts where they may be most efficient, or potentially complementary toward improving their effectiveness. Two trials in the US and Serbia (N = 1423) tested a series of choices under uncertainty using both nudge and boost interventions. In a replication setting, hypothetical and consequential decisions are used. Findings indicate that disclosure nudges and boosts, unlike social nudges, promote more advantageous financial decisions. Furthermore, the effects of disclosure nudges and boosts generally differ depending on loss and gain framing—boosts promoted more advantageous decisions under gain frames while disclosure nudges did so under loss frames. Finally, boosts were typically more effective for those who initially made suboptimal choices and sociodemographic factors did not mediate the effectiveness of the interventions. These insights provide clarity to highly nuanced, complex patterns across population behaviours in the context of financial choice under uncertainty and considerable implications for the design of interventions for policies that impact population behaviours.