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 reprodu...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 colocated 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 the authors generated and then analyzed 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. The authors specifically focus on how changes at the microscopic level in the proposed agent based simulator affect percolation and robustness. These results therefore provide theoretical basis for the analysis of social organizations.展开更多
文摘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 colocated 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 the authors generated and then analyzed 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. The authors specifically focus on how changes at the microscopic level in the proposed agent based simulator affect percolation and robustness. These results therefore provide theoretical basis for the analysis of social organizations.
