We compare contrarian to conformist advice, a contrarian expert being one whose preference bias is against the decision-maker’s prior optimal decision. Optimality of an expert depends on characteristics of prior information and learning. If either the expert is fully informed or fine information can be acquired cheaply, then for symmetric distributions F (of the state), a conformist (contrarian) is superior if F is single peaked bimodal. If only coarse information can be acquired, then a contrarian acquires more on average and hence is superior. If information is verifiable, a contrarian has less incentive to hide unfavorable evidence and again is superior.
There are many economic environments in which an object is offered sequentially to prospective buyers. It is often observed that once the object for sale is turned down by one or more agents, those that follow do the same. One explanation that has been proposed for this phenomenon is that agents making choices further down the line rationally ignore their own assessment of the object’s quality and herd behind their predecessors. Our research adds a new dimension to the canonical herding model by allowing agents to di er in their ability to assess the quality of the offered object. We develop novel tests of herding based on this ability heterogeneity and also examine its efficiency consequences, applied to organ transplantation in the U.K. We nd that herding is common but that the information lost due to herding does not substantially increase false discards of good organs or false acceptances of bad organs. Our counter-factual analysis indicates that this is due (in part) to the high degree of heterogeneity in ability across transplant centers. In other settings, such as the U.S., where organ transplantation is organized very differently and the ability distribution will not be the same, the inefficiencies due to herding might well be substantial.