• Corpus ID: 246411520

Stochastic Consensus and the Shadow of Doubt

@inproceedings{Macault2022StochasticCA,
  title={Stochastic Consensus and the Shadow of Doubt},
  author={Emilien Macault},
  year={2022}
}
We propose a stochastic model of opinion exchange in networks. Consider a nite set of agents organized in a xed network structure. There is a binary state of the world and, ex ante, each agent is informed either about the true state of the world with probability α or about the wrong state with probability 1 − α . We model beliefs as urns where white balls represent the true state and black balls the wrong state. Communication happens in discrete time and, at each period, agents draw and display… 

References

SHOWING 1-10 OF 35 REFERENCES

Robust Naïve Learning in Social Networks

A model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state, which can sway the public consensus to any other value.

Bayesian learning in social networks

We Can't Disagree Forever

Naïve Learning in Social Networks and the Wisdom of Crowds

It is shown that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows.

Dynamic competition over social networks

Opinion Dynamics and Learning in Social Networks

An overview of recent research on belief and opinion dynamics in social networks is provided and the implications of the form of learning, sources of information, and the structure of social networks are discussed.

Social Learning Equilibria

Social Learning Equilibria is introduced, a static equilibrium concept that abstracts away from the details of the given dynamics, but nevertheless captures the corresponding asymptotic equilibrium behavior.

Fragility of Asymptotic Agreement Under Bayesian Learning

Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many

Communication, consensus, and knowledge