Social Learning in Neural Agent-Based Models

Dados Bibliográficos

ID10708200
AutoresIgor Douven (0000-0003-3413-080X, Institut d'Histoire et de Philosophie des Sciences et des Techniques, autor correspondente)
Ano2025
Volume92
Fascículo1
Páginas141-161
Data de publicação2025-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPhilosophy of Science (JOURNAL)
Identificadores do periódicoISSN: 0031-8248 • E-ISSN: 1539-767X
EditoraCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/psa.2024.33
OpenAlexW4403849860
IdiomaEN
Citações recebidas5
Referências citadas58

Agent-based models (ABMs) are widely used to study how individual interactions shape collective behaviors. Critics argue that ABMs are often too simplistic to capture real-world complexities. We address this by integrating artificial neural networks into ABMs, focusing on enhancing the Hegselmann–Krause (HK) model. By using multilayer perceptrons as agents, we create more realistic ABMs that better reflect actual agents. This approach yields multiple models, as core elements of the HK model can be defined in various ways. We conduct two computational studies to compare these models with each other and with traditional individual-learning paradigms

Agent-based model · Artificial neural network · Cognitive science · Core (optical fiber) · Knowledge management · Machine learning · Perceptron · Social learning · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Opinion Dynamics and Social Influence · Psychology

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