Likelihood category game model for knowledge consensus

Zhong Yan Fan, Ying Cheng Lai, Wallace Kit Sang Tang

Research output: Contribution to journalArticlepeer-review

Abstract

To reach consensus among interacting agents is a problem of interest for social, economical, and political systems. To investigate consensus dynamics, naming game, as a computational and mathematical framework, is commonly used. Existing works mainly focus on the consensus process of vocabulary evolution in a population of agents. However, in real-world cases, naming is not an independent process but relies on perception and categorization. In order to name an object, agents must first distinguish the object according to its features. We thus articulate a likelihood category game model (LCGM) to integrate feature learning and the naming process. In the LCGM, self-organized agents can define category based on acquired knowledge through learning and use likelihood estimation to distinguish objects. The information communicated among the agents is no longer simply in some form of absolute answer, but involves one's self perception and determination. With its distinguished features, LCGM allows coexistence of multiple categories for an observation. It also provides quantitative explanation that consensus is hard to be reached among serious agents who have a more complex knowledge formation. The proposed LCGM and this study are able to provide new insights into the emergence and evolution of consensus in complex systems.

Original languageEnglish (US)
Article number123022
JournalPhysica A: Statistical Mechanics and its Applications
Volume540
DOIs
StatePublished - Feb 15 2020

Keywords

  • Categorization
  • Complex networks
  • Naming game

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

Fingerprint Dive into the research topics of 'Likelihood category game model for knowledge consensus'. Together they form a unique fingerprint.

Cite this