Using Hierarchical Bayesian Models to Learn about Reputation

This paper addresses the problem of learning with whom to interact in situations where obtaining information about others is associated with a cost, and this information is potentially unreliable. It considers settings in which agents need to decide whether to engage in a series of interactions with partners of unknown competencies, and can purchase reports about partners' competencies from others. The paper shows that hierarchical Bayesian models offer a unified approach for (1) inferring the reliability of information providers, and (2) learning the competencies of individual agents as well as the general population. The performance of this model was tested in experiments of varying complexity, measuring agents' performance as well as error in estimating others' competencies. Results show that agents using the hierarchical model to make decisions outperformed other probabilistic models from the recent literature, even when there was a high ratio of unreliable information providers.