Group for Research in Decision Analysis

# Social learning under behavioral assumptions

## Rabih Salhab – Institute for Data, Systems, and Society, MIT, United States

The second work is Social Learning with Unreliable Agents and Self-reinforcing Stochastic Dynamics. We consider a group of agents that have fixed unobservable binary beliefs’’. An individual’s belief models for example their political support (Democrat or Republican). At each time period, agents broadcast binary opinions on a social network. We assume that individuals may lie and declare opinions different from their true beliefs to conform with their neighbors. This raises the natural question as to whether one can estimate the agents’ true beliefs from observations of declared opinions. We analyze this question in the special case of complete graph. We show that, as long as the population does not include large majorities, estimation of aggregate true belief and individual true beliefs is possible. On the other hand, large majorities force minorities to lie as time goes to infinity, which makes asymptotic estimation impossible. This is a joint work with Anuran Makur, Ali Jadbabaie, and Elchanan Mossel.