Probabilistic analysis of agent-based opinion formation models
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 ana...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46789-3 |
| _version_ | 1797559986155421696 |
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| author | Carlos Andres Devia Giulia Giordano |
| author_facet | Carlos Andres Devia Giulia Giordano |
| author_sort | Carlos Andres Devia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:53:04Z |
| format | Article |
| id | doaj.art-93edaddf754542cea082c15d5156a0f5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:53:04Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-93edaddf754542cea082c15d5156a0f52023-11-20T09:17:06ZengNature PortfolioScientific Reports2045-23222023-11-0113111710.1038/s41598-023-46789-3Probabilistic analysis of agent-based opinion formation modelsCarlos Andres Devia0Giulia Giordano1Delft Center for Systems and Control, Delft University of TechnologyDelft Center for Systems and Control, Delft University of TechnologyAbstract 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.https://doi.org/10.1038/s41598-023-46789-3 |
| spellingShingle | Carlos Andres Devia Giulia Giordano Probabilistic analysis of agent-based opinion formation models Scientific Reports |
| title | Probabilistic analysis of agent-based opinion formation models |
| title_full | Probabilistic analysis of agent-based opinion formation models |
| title_fullStr | Probabilistic analysis of agent-based opinion formation models |
| title_full_unstemmed | Probabilistic analysis of agent-based opinion formation models |
| title_short | Probabilistic analysis of agent-based opinion formation models |
| title_sort | probabilistic analysis of agent based opinion formation models |
| url | https://doi.org/10.1038/s41598-023-46789-3 |
| work_keys_str_mv | AT carlosandresdevia probabilisticanalysisofagentbasedopinionformationmodels AT giuliagiordano probabilisticanalysisofagentbasedopinionformationmodels |