Group Decisions, Contracts and Informational Cascades

We analyze the (Perfect Bayesian) equilibrium in an observational learning model with communication and bilateral incentive contracts when pairs of agents sequentially make decisions based on internal communications and the observed history of decisions by other groups. One of the agents in each pair receives a private noisy signal on an unknown state and we characterize asymptotic learning through the design of efficient bilateral contracts. Long run information aggregation depends not only on the properties of the signal structure but also on the intensity of the conflicts of interest between the informed and uninformed agents. In contrast to the results from the herding literature, we find that information can be asymptotically complete with bounded (private) beliefs while there may be herding even with unbounded beliefs; in the latter case herding occurs when the conflicts are sufficiently high that it is incentive-efficient to divorce decisions from private signals. We derive sufficient conditions on agents' preferences and the signal structure for learning to be asymptotically complete as the number of agent-pairs gets large and illustrate their applications by adapting models from the price-quality discrimination and investment contracting literatures