Robust Naïve Learning in Social Networks

Amir, Gideon; Arieli, Itai; Ashkenazi-Golan, Galit; Peretz, Ron

doi:10.2139/ssrn.3791413

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…DeGroot type of learning models convey an essential and robust idea that is taking a firmer foothold in theory [4][5][6]. They offer a functional form of updating.…”

Section: Introductionmentioning

confidence: 99%

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Averaging plus learning models and their asymptotics

Popescu

Vaidya

2023

Proc. R. Soc. A.

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We develop original models to study interacting agents in financial markets and in social networks. Within these models, randomness is vital as a form of shock or news that decays with time. Agents learn from their observations and learning ability to interpret news or private information in time-varying networks. Under general assumptions on the noise, a limit theorem is developed for the generalized averaging framework for certain type of conditions governing the learning. In this context, the agents’ beliefs (properly scaled) converge in distribution that is not necessarily normal. Fresh insights are gained not only from proposing a new setting for social learning models but also from using different techniques to study discrete-time random linear dynamical systems.

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“…DeGroot type of learning models convey an essential and robust idea that is taking a firmer foothold in theory [4][5][6]. They offer a functional form of updating.…”

Section: Introductionmentioning

confidence: 99%

“…Noise is also added in bounded confidence models [33] such as Hegselmann–Krause models, which are quite distinct from DeGroot dynamics [34–36]. Interestingly, but complementary to our work is that of 1/m-DeGroot dynamics [5]. A variant of DeGroot dynamics that is robust to stubborn agents and mis-specification is introduced.…”

Section: Introductionmentioning

confidence: 99%

Averaging plus learning models and their asymptotics

Popescu

Vaidya

2023

Proc. R. Soc. A.

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The asynchronous DeGroot dynamics

Elboim,

Peres,

Peretz

2024

Random Struct Algorithms

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We analyze the asynchronous version of the DeGroot dynamics: In a connected graph with nodes, each node has an initial opinions in and an independent Poisson clock. When a clock at a node rings, the opinion at is replaced by the average opinion of its neighbors. It is well known that the opinions converge to a consensus. We show that the expected time to reach ‐consensus is poly in undirected graphs and in Eulerian digraphs, but for some digraphs of bounded degree it is exponential. Our main result is that in undirected graphs and Eulerian digraphs, if the degrees are uniformly bounded and the initial opinions are i.i.d., then for every fixed . We give sharp estimates for the variance of the limiting consensus opinion, which measures the ability to aggregate information (“wisdom of the crowd”). We also prove generalizations to non‐reversible Markov chains and infinite graphs. New results of independent interest on fragmentation processes and coupled random walks are crucial to our analysis.

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Robust Naïve Learning in Social Networks

Cited by 2 publications

References 18 publications

Averaging plus learning models and their asymptotics

Averaging plus learning models and their asymptotics

The asynchronous DeGroot dynamics

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