Analysis of agent-based opinion formation models

7 novembre 2022
Orario di inizio 
Polo Ferrari 2 - Via Sommarive 9, Povo (Trento)
Seminar room
Comunità studentesca UniTrento
Ingresso libero
Dipartimento di Ingegneria Industriale
Carlos Andrés Devia (Delft University of Technology, The Netherlands)

Agent-based opinion formation models embed the psychological traits of individuals to explain and mimic the mechanisms that produce the evolution of opinions in society. These models can produce a wide range of outcomes, and characterizing the possible qualitative behaviours that they can generate is an open problem. Which types of opinion distributions can a model produce starting from a given initial condition? Can an initial opinion distribution be transformed by the model evolution into a significantly different configuration? How do these outcomes depend on the agent parameters?
The presentation discusses a novel methodology aimed to solve this problem by identifying different qualitative opinion distribution categories (Perfect Consensus, Consensus, Polarization, Clustering, Dissensus) and investigating the possible transitions between different categories, also exploiting suitable representations that concisely capture the general level of agreement in the population. Simulation results showing the application of the proposed methodology to two classical and two new opinion formation models will be discussed, together with potential adaptation to agent-based models in other research fields.

The seminar will also be available via Zoom. Please write to giulia.giordano [at] to ask for the link.


Carlos Andrés Devia received a M.Sc. in Electrical Engineering from Pontifical Universidad Javeriana, Colombia where he worked on optimal control and model predictive control in unmanned aerial vehicles. Since 2019 he is a Ph.D. candidate at the Delft University of Technology, The Netherlands, dealing with the study of interconnected dynamical systems from a theoretical and numerical perspective. His current research focuses on the analysis and development of agent-based opinion formation models.