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Much research on advice taking examines how people revise point estimates given input from others. This work has established that people often egocentrically discount advice. If they were to place more weight on advice, their point estimates would be more accurate. Yet the focus on point estimates and accuracy has resulted in a narrow conception of what it means to heed advice. We distinguish between revisions of point estimates and revisions of attendant probability distributions. Point estimates represent a single best guess; distributions represent the probabilities that people assign to all possible answers. A more complete picture of advice taking is provided by considering revisions of distributions, which reflect changes in both confidence and best guesses. We capture this using a new measure of advice utilization: the influence of advice. We observe that, when input from a high-quality advisor largely agrees with a person’s initial opinion, it engenders little change in one’s point estimate and, hence, little change in accuracy yet significantly increases confidence. This pattern suggests more advice taking than generally suspected. However, it is not necessarily beneficial. Because people are typically overconfident to begin with, receiving advice that agrees with their initial opinion can exacerbate overconfidence. In several experiments, we manipulate advisor quality and measure the extent to which advice agrees with a person’s initial opinion. The results allow us to pinpoint circumstances in which heeding advice is beneficial, improving accuracy or reducing overconfidence, as well as circumstances in which it is harmful, hurting accuracy or exacerbating overconfidence. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

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This article uses decision analysis concepts and techniques to address an extremely important problem to any family with children, namely, how to avoid the tragic death of a child during the high-risk ages of 15-24. Descriptively, our analysis indicates that of the 35,000 annual deaths among this age group in the United States, approximately 20,000 could be avoided if individuals chose readily available alternatives for decisions relating to these deaths. Prescriptively, we develop a decision framework for parents and a child to both identify and proactively pursue decisions that can lower that child's exposure to life-threatening risks and positively alter decisions when facing such risks. Applying this framework for parents and the youth themselves, we illustrate the logic and process of generating proactive alternatives with numerous examples that each could pursue to lower these life-threatening risks and possibly avoid a tragic premature death, and discuss some public policy implications of our findings.

This paper studies the effect of limited information in a sequential search setting where a single selection is to be made from a set of random potential options. We consider both a full-information problem, where the decision maker observes the exact value of each option as she searches, and a partial-information problem, in which the decision maker only learns the rank of the current option relative to the options that have already been observed. We develop a model that allows for a sharp contrast between search behavior in the two information settings, both theoretically and empirically. We present the results of an experiment that tests, and supports, the key prediction of our model analysis—limited information induces longer search. Our data further suggest systematic deviations from the theoretical benchmarks in both informational settings. Importantly, subjects in our partial-information conditions are prone to stop prematurely during early stages of the search process and to suboptimally continue the search during late stages. We propose a simple model that succinctly captures the interplay of two symmetric choice and judgment biases that have asymmetric (but opposing) effects on the length of search. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1902 . This paper was accepted by Teck-Hua Ho, behavioral economics.