Abstract
We consider a group of agents connected by a social network who participate in majority dynamics: each agent starts with an opinion in {−1, +1} and repeatedly updates it to match the opinion of the majority of its neighbors.
We assume that one of {−1, +1} is the “correct” opinion S, and consider a setting in which the initial opinions are independent conditioned on S, and biased towards it. They hence contain enough information to reconstruct S with high probability. We ask whether it is still possible to reconstruct S from the agents’ opinions after many rounds of updates.
While this is not the case in general, we show that indeed, for a large family of bounded degree graphs, information on S is retained by the process of majority dynamics.
Our proof technique yields novel combinatorial results on majority dynamics on both finite and infinite graphs, with applications to zero temperature Ising models.
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Omer Tamuz is supported by ISF grant 1300/08, and is a recipient of the Google Europe Fellowship in Social Computing, and this research is supported in part by this Google Fellowship.
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Tamuz, O., Tessler, R.J. Majority Dynamics and the Retention of Information. Isr. J. Math. 206, 483–507 (2015). https://doi.org/10.1007/s11856-014-1148-2
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DOI: https://doi.org/10.1007/s11856-014-1148-2