Coupling agent based simulation with dynamic networks analysis to study the emergence of mutual knowledge as a percolation phenomenon

TL;DR

This paper investigates the emergence of mutual knowledge in co-located groups as a percolation phenomenon by coupling agent-based simulation with dynamic network analysis, providing a theoretical basis for social organization analysis.

Contribution

It introduces a novel coupling of agent-based simulation with dynamic networks analysis to explain mutual knowledge emergence as a percolation process.

Findings

Mutual knowledge emergence resembles a percolation process.

Microscopic changes affect percolation and robustness.

Provides a theoretical basis for social organization analysis.

Abstract

The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge (MK) in a group of co-located individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: after using an agent-based simulation we generated and then analysed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. We specifically focus on how changes at the microscopic level in our agent based simulator affect percolation and robustness. These results therefore provides theoretical basis for the analysis of social organizations.