Non-Bayesian Learning in Social Networks
Summary
Non-Bayesian learning in social networks refers to a paradigm in which distributed agents iteratively update probabilistic beliefs about an underlying state by combining private observations with the opinions of their neighbours. Unlike fully Bayesian approaches, where each agent computes a global posterior, non-Bayesian methods employ simple update rules that fuse local information and peer judgments, enabling scalable inference across large, heterogeneous networks. Key theoretical findings establish conditions under which agents’ beliefs converge to the true state, often characterised by an exponential learning rate. The topology of the communication graph critically influences both the speed and accuracy of learning, with phenomena such as leader–follower behaviour emerging in weakly connected structures. Recent advances address non-stationary environments through adaptive mechanisms, and exploit sequences of exchanged beliefs to infer network structure and influence patterns. Applications span from sensor networks and cooperative positioning to the analysis of opinion dynamics and misinformation control in online platforms.
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Non-Bayesian Learning in Social Networks publication trend
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Technical terms
Non-Bayesian social learning: A framework where agents update beliefs through local fusion of private observations and neighbours’ opinions without computing a full global posterior.
Diffusion strategy: A local combination rule by which each agent merges its own belief with those of its adjacent peers in the network.
Weakly-connected graph: A network partitioned into sending and receiving sub-networks, where information flows predominantly in one direction.
Exponential learning rate: The rate at which the probability of incorrect belief decays exponentially as a function of algorithm parameters or time.
References
- Adaptive Social Learning. IEEE Transactions on Information Theory (2021).
- Interplay Between Topology and Social Learning Over Weak Graphs. IEEE Open Journal of Signal Processing (2020).
- Discovering Influencers in Opinion Formation Over Social Graphs. IEEE Open Journal of Signal Processing (2023).
- Belief Control Strategies for Interactions Over Weakly-Connected Graphs. IEEE Open Journal of Signal Processing (2021).
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