In making choices under uncertainty, individuals often can obtain more information by observing previous decisions of others. We develop a theoretical framework of individual choice under uncertainty for agents connected via a directed network which allows for observational learning. The decision is made once, and thus, learning from repetition is not possible. We obtain properties of networks that affect accuracy of individual choice and information aggregation. Network performance is evaluated using two criteria: Individual (final agent) and Social (group) choice accuracy. We design an experiment, with network structure as the main treatment variable, to test the theoretical predictions. In all treatments, there is efficiency loss compared to a benchmark with all Bayesian agents. Despite evidence that individuals understand the value of information, they display a propensity to overweight information inferred from observed actions, which is increasing in the number of actions they observe. In most cases group accuracy predictions, conditional on network properties, are supported by the data.
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