TY - GEN
T1 - A Multi-agent Model for Opinion Evolution in Social Networks Under Cognitive Biases
AU - Alvim, Mário S.
AU - Gaspar da Silva, Artur
AU - Knight, Sophia
AU - Valencia, Frank
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region [-1,1]2 and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions (R), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in R or not included in R. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.
AB - We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region [-1,1]2 and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions (R), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in R or not included in R. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.
KW - Cognitive bias
KW - Multi-Agent Systems
KW - Social Networks
UR - https://www.scopus.com/pages/publications/85197255253
U2 - 10.1007/978-3-031-62645-6_1
DO - 10.1007/978-3-031-62645-6_1
M3 - Conference contribution
AN - SCOPUS:85197255253
SN - 9783031626449
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 19
BT - Formal Techniques for Distributed Objects, Components, and Systems - 44th IFIP WG 6.1 International Conference, FORTE 2024, Held as Part of the 19th International Federated Conference on Distributed Computing Techniques, DisCoTec 2024, Proceedings
A2 - Castiglioni, Valentina
A2 - Francalanza, Adrian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 44th IFIP WG 6.1 International Conference on Formal Techniques for Distributed Objects, Components, and Systems, FORTE 2024, held as part of the 19th International Federated Conference on Distributed Computing Techniques, DisCoTec 2024
Y2 - 17 June 2024 through 21 June 2024
ER -