John W.E. Cremin

2025-26

Bot Got Your Tongue? Social Learning with Timidity and Noise

John W.E. Cremin

Abstract Models of social learning conventionally assume that all actions are visible, whereas in reality, we can often choose whether or not to advertise our choices. Inthis paper, I study a model of sequential social learning in which social agents choose whether or not to let successors see their action, only wanting to do so if they are sufficiently confident in their choice (they are timid), and noise agents act randomly. I find that in sparse networks, this produces a form of unravelling to the effect that noise agents are overrepresented. This can damage learning to an arbitrary extent if social agents are sufficiently timid. In dense networks, however, no such unravelling occurs, and the combination of noise and timidity can facilitate complete learning even with bounded beliefs.

Keywords Sequential Social Learning, Endogenous Social Networks, Network Theory, Information Economics

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2025-27

Too Much Information & The Death of Consensus

John W.E. Cremin

Abstract Modern society is increasingly polarized, even on purely factual questions, despite greater access to information than ever. In a model of sequential sociallearning, I study the impact ofmotivated reasoningon information aggregation. This is a belief formation process in which agents trade-off accuracy against ideological convenience. I find that even Bayesian agents only learn in very highly connected networks, where agents have arbitrarily large neighborhoods asymptotically. This is driven by the fact that motivated agents sometimes reject information that can be inferred from their neighbors’ actions when it refutes their desired beliefs. Observing any finite neighborhood, there is always some probability that all of an agent’s neighbors will have disregarded information thus. Moreover, I establish thatconsensus, where all agents eventually choose the same action, is only possible with relatively uninformative private signals and low levels of motivated reasoning.

Keywords Social Learning, Motivated Reasoning, Networks, Polarization

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