好建议的坏处:理解建议何时以及如何加剧过度自信

The Bad Thing About Good Advice: Understanding When and How Advice Exacerbates Overconfidence

Management Science · 2021
被引 34
人大 A+FT50UTD24ABS 4*

中文导读

研究区分了点估计和概率分布修正,发现当高质量建议与初始意见一致时,虽不改变点估计但显著增加自信,从而加剧过度自信,并识别了建议有益或有害的条件。

Abstract

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.

建议采纳过度自信概率分布修正建议影响力