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Decisions from Valuations of Unknown Payoff Distributions 

Ido Erev, Yefim Roth, Doron Sonsino

Forthcoming in Decision


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.