Computational learning mechanisms of information propagating in social networks Short title: Learning Rules in Social Networks
Year: 2025
Type: preprint
Abstract: What mechanisms underlie how individuals integrate disparate and redundant information that spreads in social networks? The DeGroot (DG) model suggests that we adjust our beliefs according to the weighted average opinions of our social connections. In contrast, reinforcement learning (RL) models assume that learning occurs sequentially, depending on error-driven belief updating. Here, we compare these models directly using two datasets of information propagating in social networks, and determine which learning rule best accounts for the integration of information spreading in social networks. We found that variants of the DG model provide a better overall fit across participants, and are more effective in building consensus, and generating accurate estimates of the true state of the world. Furthermore, simulated models show how social network topology affects choice accuracy and consensus, depending on the learning rule. Together, our findings characterize the computational mechanisms used by individuals to propagate information in social networks.
Source: HAL
Institutions: Centre National de la Recherche Scientifique, Université de Lille, Institut des Sciences Cognitives Marc Jeannerod, Institut d'Economie Scientifique Et de Gestion, Lille Économie Management, Dartmouth College, Groupe d'Analyse et de Théorie Economique Lyon St Etienne, École de management de Lyon
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G1291134351, G14109637, G1416082926, G2039374035, G2418904852, G247940636, G2871482981, G2936332253, Neurocomputational mechanisms of information propagation in social networks, COmputational Modeling of COllaboration and Mentalization in networks with Benevolent Bots, Computational and Neural Mechanisms of Social Learning in Social Networks, G4774453313, G4866930200, CRCNS US-French Research Proposal: Computational and Neural Mechanisms of Social Learning in Social Networks, Construction, Fonction Cognitive et Réhabilitation du Cerveau, IDEXLYON, G8247062983, G8936884176
OpenAlex ID: W4416179057