RT Conference Proceedings
T1 A new approach to polarization modeling using Markov chains
A1 Guevara Gil, Juan Antonio
A1 Gómez González, Daniel
A1 Castro Cantalejo, Javier
A1 Gutiérrez García-Pardo, Inmaculada
A1 Robles Morales, José Manuel
A2 Ciucci, Davide
A2 Couso, Inés
A2 Medina, Jesús
A2 Ślęzak, Dominik
A2 Petturiti, Davide
A2 Bouchon-Meunier, Bernadette
A2 Yager, Ronald R.
AB Abstract: In this study, we approach the problem of polarization modeling with Markov Chains (PMMC). We propose a probabilistic model that provides an interesting approach to knowing what the probability for a specific attitudinal distribution is to get to an i.e. social, political, or affective Polarization. It also quantifies how many steps are needed to reach Polarization for that distribution. In this way, we can know how risky an attitudinal distribution is for reaching polarization in the near future. To do so, we establish some premises over which our model fits reality. Furthermore, we compare this probability with the polarization measure proposed by Esteban and Ray and the fuzzy polarization measure by Guevara et al. In this way, PMMC provides the opportunity to study in deep what is the performance of these polarization measures in specific conditions. We find that our model presents evidence that in fact, some distributions will presumably show higher risk than others even when the entire population holds the same attitude. In this sense, according to our model, we find that moderate/indecisive attitudes present a higher risk for polarization than extreme attitudes and should not be considered the same scenario despite the fact that the entire population maintains the same attitude.
SN 978-3-031-08973-2
SN 978-3-031-08974-9
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/104417
UL https://hdl.handle.net/20.500.14352/104417
LA eng
NO Guevara, J.A., Gómez, D., Castro, J., Gutiérrez, I., Robles, J.M. (2022). A New Approach to Polarization Modeling Using Markov Chains. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_12
NO Communications in Computer and Information Science (CCIS, volume 1601)
NO Ministerio de Asuntos Económicos y Transformación Digital (2020-2023), España
NO Ministerio de Economía, Comercio y Empresa (2023-2024), España
DS Docta Complutense
RD 23 abr 2025