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 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 init

SUBMITTER: Devia CA 

PROVIDER: S-EPMC10656423 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature