Alice C.W. Huang

In the literature on expert trust, it is often assumed that track records are the gold standard for evaluating expertise, and the difficulty of expert identification arises from either the lack of access to track records, or the inability to assess them. I show, using a computational model, that even in an idealized environment where agents have a God’s eye view on track records, they may fail to identify experts. Under plausible conditions, selecting testimony based on track records ends up reducing overall accuracy, and preventing the community from identifying the real experts.

Two types of formal models - landscape search tasks and two-armed bandit models - are often used to study the effects that various social factors have on epistemic performance. I argue that they can be understood within a single framework. In this unified framework, I develop a model that may be used to understand the effects of functional and demographic diversity and their interaction. Using the unified model, I find that the benefit of demographic diversity is most pronounced in a functionally homogeneous group, and decreases with the increase of functional diversity.

The threshold view says that a person forms an outright belief P if and only if her credence for P reaches a certain threshold. Using computer simulations, I compare different versions of the threshold view to understand how they perform under time pressure in decision problems. The results illuminate the strengths and weaknesses of the various cognitive strategies in different decision contexts. A threshold view that performs well across diverse contexts is likely to be a cognitively flexible and context-dependent fusion of several of the existing theories. The results of the simulations also cast doubts on the possibility of a threshold view that is both simple enough to streamline our reasoning while also allowing us to form good action-guiding beliefs.