Knowledge Consensus in complex networks: the role of learning

TL;DR

This paper introduces a likelihood category game model (LCGM) that integrates feature learning with naming processes in agents, revealing how complex knowledge impacts consensus formation in networks.

Contribution

The paper presents the LCGM, a novel framework combining perception-based learning and naming, offering new insights into consensus dynamics in complex networks.

Findings

More complex knowledge hinders consensus

High-degree agents contribute more to knowledge formation

Agents with larger degree are more likely to be intelligent

Abstract

To reach consensus among interacting agents is a problem of interest for social, economical, and political systems. A computational and mathematical framework to investigate consensus dynamics on complex networks is naming games. In general, naming is not an independent process but relies on perception and categorization. Existing works focus on consensus process of vocabulary evolution in a population of agents. However, in order to name an object, agents must first be able to distinguish objects according to their features. We 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 perception. Extensive simulations with LCGM reveal that a more complex knowledge makes it harder to reach consensus. We also find that agents with larger degree contribute more to the knowledge formation and are more likely to be intelligent. The proposed LCGM and the findings provide new insights into the emergence and evolution of consensus in complex systems in general.