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Belief Samples Are All You Need For Social Learning
Agents can share their learning experience with their peers by taking actions observable to them, with values from a finite feasible set of states.
Estimating True Beliefs from Declared Opinions
To address this, Jadbabaie et al. formulated the interacting P\'olya urn model of opinion dynamics under social pressure and studied it on complete-graph social networks using an aggregate estimator, and found that their estimator converges to the inherent beliefs unless majority pressure pushes the network to consensus.
Stochastic Opinion Dynamics under Social Pressure in Arbitrary Networks
To study this, the interacting Polya urn model was introduced by Jadbabaie et al., in which each agent has two kinds of opinion: inherent beliefs, which are hidden from the other agents and fixed; and declared opinions, which are randomly sampled at each step from a distribution which depends on the agent's inherent belief and her neighbors' past declared opinions (the social pressure component), and which is then communicated to their neighbors.
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations.
