Quantum Modeling of Social Dynamics

Quantum Modeling of Social Dynamics

C. Bisconti, A. Corallo, M. De Maggio, F. Grippa, S. Totaro
Copyright: © 2010 |Pages: 11
DOI: 10.4018/jksr.2010010101
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In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.
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Theoretical Background

Social Network Analysis (SNA) represents a widely adopted methodological approaches generally applied to the study of organizational networks during the past years (Wasserman & Faust, 1994). SNA is based on a set of methods and tools to investigate the patterning of relations among social actors. It provides a visual and dynamic representation of social and economic phenomena and relies on the topological properties of the networks to measure the characteristics of the phenomena.

The main limitation of SNA is to be mainly a structural method. Its unit of analysis is not the single actor with its attributes, but the relations between actors (e.g., dyads, triads), defined identifying the pair of actors and the properties of theie relation. By focusing mainly on the relations, SNA might underestimate many organizational elements which might influence the ability of an organization to reach its goals. Perceptive measures are sometimes ignored by SNA researchers. What seems to be missing in current SNA research is an approach to study how the individual actors' characteristics change the network configuration and performance. Furthermore, the empirical work on network information advantage is still considered “content agnostic” (Hansen, 1999). Paying attention only to the structural facets of community interactions is like considering all the ties as indistinguishable and homogeneous. In this perspective, actors performing different activities, or involved in different projects are detected simply as interacting members, with no distinction among sub-categories that might change over time. A recent field within SNA is Dynamic Network Analysis, that uses longitudinal data to perform an evolutionary study of the organizational networks

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