Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

• Published in: PloS one Vol. 12; no. 8; p. e0182490
• Main Authors: Zubek, Julian, Denkiewicz, Michał, Barański, Juliusz, Wróblewski, Przemysław, Rączaszek-Leonardi, Joanna, Plewczynski, Dariusz
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.08.2017; Public Library of Science (PLoS)
• Subjects: Adaptation; Biology and Life Sciences; Categories; Classification; Communication; Computer and Information Sciences; Computer simulation; Decision making; Flow; Humans; Information dissemination; Information flow; Information management; Information Services; Linguistics; Models, Theoretical; Multiagent systems; Network topologies; Physical Sciences; Research and Analysis Methods; Social Adjustment; Social networks; Social organization; Social Sciences; Studies; Topology; Topology optimization; Poland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182490

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.