Likelihood category game model for knowledge consensus

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.