Decisions From Valuations of Unknown Payoff Distributions

Abstract

Four experiments are presented that clarify the impact of experience on the way people use valuations. In each of the 100 trials of Study 1, participants were asked to choose between the status quo and an unknown binary lottery based on valuations by two expert systems: a well-calibrated “expert” reporting the expected values, and an expert that ignores the low probability outcome and reports the medians (that equaled the modes). The results suggest that experience decreased the inclination to follow the recommendation of the well-calibrated expert. This deviation from maximization appears to reflect two biases: the tendency to follow the expert that has recommended the action that provided higher payoff in most cases, and the tendency to follow the more extreme valuation. Studies 2 and 3 suggest similar reliance on experts that recommend the best choice in most trials, in choices between two payoff distributions, and even when these experts do not provide the median or the mode of the distributions. Study 4 shows that the impact of experts that direct people toward the optimal choice can be increased by exaggeration (inflating the estimated advantage of the payoff maximizing option). However, the long-term impact of exaggeration depends on the proportion of cases in which the optimal choice leads to the best payoff; a lasting positive effect of exaggeration was observed only when this proportion was high. These results can be captured by assuming several “expert weighting rules” and the selection between the rules based on small samples of past experiences.

Failures in the communication of risk: Decisions and numeracy

“…Bolton and Katok (2018) study the efficacy of probability forecasts versus recommendation forecasts. They found that forecasts in the form of a recommendation are prone to a "cry wolf effect"-a tendency to ignore the forecast when the forecast has been wrong in the past (also found in previous studies; e.g., Bliss et al, 1995;Erev et al, 2020;Meyer & Bitan, 2002). Overall, probability forecasts performed better.…”

“…One obvious alternative, natural language descriptions of uncertainty, has been shown to be ambiguous: Interpretation of verbal descriptions of uncertainty are sensitive to context (e.g., Harris & Corner, 2011;Weber & Hilton, 1990) and vary widely by individual (e.g., Karelitz & Budescu, 2004;Wallsten et al, 1986), limiting their ability to deliver a clear message about risk. Recommendations based on expert assessments of the forecast offer a way around both user numeracy and verbal description shortcomings (Erev et al, 2020). Non-numeric in nature, recommendations can, in theory, convey the optimal action to the decision maker as effectively as quantitative measures.…”