1 research outputs found
Knowledge Consensus in complex networks: the role of learning
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