Social Learning in Neural Agent-Based Models.
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–Kr...
| Publicado en: | Philosophy of Science Vol. 92; no. 1; pp. 141 - 162 |
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| Formato: | Artículo |
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Cambridge University Press
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=182582340&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182582340 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318248 PSC jtl: Philosophy of Science issn: 00318248 maglogo: N pubinfo: dt: Jan2025 vid: 92 iid: 1 pid: 15979 pub: Cambridge University Press artinfo: ui: 182582340 10.1017/psa.2024.33 ppf: 141 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.6MB tig: atl: Social Learning in Neural Agent-Based Models. aug: au: Douven, Igor affil: Paris 1 Université Panthéon-Sorbonne, Paris, France su: Multilayer perceptrons Artificial neural networks Collective behavior Social learning sug: subj: Multilayer perceptrons Artificial neural networks Collective behavior Social learning ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Philosophy of Science is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Philosophy of Science holder: Cambridge University Press dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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