Information Aggregation in Overlapping Generations

We study a model of social learning with overlapping generations, where agents meet others and share data about an underlying state over time. We examine under what conditions the society will produce individuals with precise knowledge about the state of the world. Under the full information sharing technology, individuals exchange the information about their point estimates of the underlying state, as well as the precision of their signals and update their beliefs accordingly. Under the limited information sharing technology, agents observe the point estimates but not precisions, and update their beliefs by taking a weighted average, where weights can depend on the sequence of meetings, as well as the ‘age’ and the number of previous meetings an agent has had. Our main result shows that, unlike static settings, using linear learning rules without access to the precision information will not guide the population (or even a fraction of its members) to converge to a unique belief, and having access to the precision of a source signal is essential for having an informed population.