When agent-based models are developed to capture opinion formation in large-scale populations, the opinion update equations often need to embed several complex psychological traits. The resulting models are more realistic, but also challenging to assess analytically, and hence numerical analysis techniques have an increasing importance in their study. Here, we propose the Qualitative Outcome Likelihood (QOL) analysis, a novel probabilistic analysis technique aimed to unravel behavioural patterns and properties of agent-based opinion formation models, and to characterise possible outcomes when only limited information is available. The QOL analysis reveals which qualitative categories of opinion distributions a model can produce, brings to light their relation to model features such as initial conditions, agent parameters and underlying digraph, and allows us to compare the behaviour of different opinion formation models. We exemplify the proposed technique by applying it to four opinion formation models: the classical Friedkin-Johnsen model and Bounded Confidence model, as well as the recently proposed Backfire Effect and Biased Assimilation model and Classification-based model.

Probabilistic analysis of agent-based opinion formation models / Devia, Carlos Andres; Giordano, Giulia. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 13:1(2023), p. 20152. [10.1038/s41598-023-46789-3]

Probabilistic analysis of agent-based opinion formation models

Giordano, Giulia
Ultimo
2023-01-01

Abstract

When agent-based models are developed to capture opinion formation in large-scale populations, the opinion update equations often need to embed several complex psychological traits. The resulting models are more realistic, but also challenging to assess analytically, and hence numerical analysis techniques have an increasing importance in their study. Here, we propose the Qualitative Outcome Likelihood (QOL) analysis, a novel probabilistic analysis technique aimed to unravel behavioural patterns and properties of agent-based opinion formation models, and to characterise possible outcomes when only limited information is available. The QOL analysis reveals which qualitative categories of opinion distributions a model can produce, brings to light their relation to model features such as initial conditions, agent parameters and underlying digraph, and allows us to compare the behaviour of different opinion formation models. We exemplify the proposed technique by applying it to four opinion formation models: the classical Friedkin-Johnsen model and Bounded Confidence model, as well as the recently proposed Backfire Effect and Biased Assimilation model and Classification-based model.
2023
1
Devia, Carlos Andres; Giordano, Giulia
Probabilistic analysis of agent-based opinion formation models / Devia, Carlos Andres; Giordano, Giulia. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 13:1(2023), p. 20152. [10.1038/s41598-023-46789-3]
File in questo prodotto:
File Dimensione Formato  
J067_2023_SciRep.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 4.16 MB
Formato Adobe PDF
4.16 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/409576
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact