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 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.
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References
Mell J, Study of verbal communication between pilot and airtraffic controller in standard and non-standard situations, Doctoral Thesis, Paris: Ecole Nationale de l’Aviation Civile, 1992.
Hutchins E, The technology of team navigation. In Intellectual Teamwork. Eds. by Galegher J, Kraut R E, and Edigo C, Hillsdale, NJ: LEA, 1990.
Bressolle M C, Pavard B, and Leroux M, The role of multimodal communication in cooperation and intention recognition: The case of air traffic control, Proceedings of the International Conference on Cooperative Multimodal Communication, Theory and Applications, Eindhoven, The Netherlands, 24–26 May, 1995.
Lalouette C and Pavard B, Enhancing interorganizational resilience by loose coupling concept and complexity paradigm, Proceedings of the Third Resilience Engineering Symposium, 2008.
Moffat J, Modelling human decision-making in simulation models of conflict, The International C2 Journal, 2007, 1(1): 31–60.
Rateb F, Pavard B, et al. Modelling malaria with multi-agent systems, International Journal of Intelligent Information Technologies, 2005, 1(2): 17–27.
Pavard B, Dugdale J, Saoud N, et al., Design of robust sociotechnical systems, Resilience Engineering: Remaining Sensitive to the Possibility of Failure, Ashgate, 2007.
Broadbent S R and Hammersley J M, Percolation processes. I. Crystals and Mazes, Proceedings of the Cambridge Philosophical Society, 1957, 53: 53–629.
Bunde A and Havlin S, Percolation and disordered systems: Theory and applications, Physica A, 1999.
Cantono S and Solomon S, New J. Phys., 2010, 12(7): 075038.
Solomon S, Weisbuch G, de Arcangelis L, et al., Social percolation models, Physica A, 2000, 277(1–2): 239–247.
Roussenq J, Percolation, Encyclopaedia Universalis, 1992, 17: 17–838.
Pajot S, Percolation et économie, thèse de doctorat de l’Universite de Nantes, 2001.
Kesten H, Percolation theory and first-passage percolation, Journal of the Royal Statistical Society B, 1987, 16: 16–23.
Barrat A, Barthelemy M, and Vespignani A, Dynamical Processes on Complex Networks, Cambridge University Press, 2008.
Callaway D S, Newman M E J, Strogatz S H, et al., Network robustness and fragility: Percolation on random graphs, Phys. Rev. Lett., 2000, 85: 85–5468.
Newman M E J, The structure and function of complex networks, SIAM Rev., 2003, 45: 45–167.
Krauss R and Fussell S, Mutual knowledge and communicative effectiveness, Intellectual teamwork: The social and technological bases of cooperative work, Eds. by Galegher J, Kraut R E, and Egido C, Hillsdale, NJ: Erlbaum, 1990.
Clark H H and Carlson T, Hearers and speech acts, Language, 1982, 2: 2–332.
Clark H H and Marshall C R, Definite reference and mutual knowledge, Eds. by Joshi A K, Webber B, and Sag I A, Elements of discourse understanding, Cambridge, United Kingdom: Cambridge University Press, 1981.
Cramton C, The mutual knowledge problem and its consequences for dispersed collaboration, Organization Science, 2001, 12(3): 346–371.
Barr D J, Establishing conventional communication systems: Is common knowledge necessary? Cognitive Science, 2004, 28: 28–937.
Benchekroun H, Modélisation des mécanismes d’interlocution, Thèse de doctorat, CNAM, Paris, 1992.
Dugdale J, Bellamine Ben Saoud N, Pavard B, et al., Simulation and emergency management. Information Systems for Emergency Management, Eds. by Van de Walle B, Turoff M, and Starr Roxanne Hiltz S. Advances in Management Information Systems. Vladimir Swass Series Editor, 2008.
Repast Suite, http://repast.sourceforge.net/.
Pajek, http://pajek.imfm.si/doku.php.
R software, http://www.r-project.org/.
Erdos P and Renyi A, On random graphs. I. Publicationes Mathematicae, 1959, 6: 6–290.
Grice P, Logic and conversation, J. Syntax and Semantics, 1975, 41–58.
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Dugdale, J., Bellamine Ben Saoud, N., Zouai, F. et al. Coupling agent based simulation with dynamic networks analysis to study the emergence of mutual knowledge as a percolation phenomenon. J Syst Sci Complex 29, 1358–1381 (2016). https://doi.org/10.1007/s11424-016-4298-y
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DOI: https://doi.org/10.1007/s11424-016-4298-y