Misinformation Due to Asymmetric Information Sharing

We introduce a model of social learning in which agents share true and false information with diﬀerent decays and to diﬀerent networks of people. Our results establish that these asymmetries, thus far largely ignored in theory despite being empirically established, govern the long-run beliefs of a society. We derive a single threshold condition under which misinformation prevails. Misinformation is more likely when false information decays less, and when the false information network is locally denser, as measured by the largest eigenvalue of its adjacency matrix. Under these conditions, all agents guess the wrong state; a result that the literature reaches only in the presence of forceful or stubborn agents. Additionally, we measure speed of convergence and show that agents who are more central in the false information network are more prone to be misinformed. We illustrate our results using numerical simulations that incorporate societies segmented into groups, and derive policy implications that center on human sharing behaviors and network structures. with diﬀerent decay and with diﬀerent people. Our results show that both decay and network asymmetries can cause a society to go from being able to discover the true state to being misinformed in the long run. By distinguishing between networks in which true information and false information are shared, we establish a threshold condition that determines which state a society converges to in the long run. The condition compares the product of decay factor and largest eigenvalue between true and false information sharing networks. Noticeably, the long-run outcome in most cases does not depend on the initial distribution of signals (as long as there is at least one signal of each) and is