International Journal of Knowledge Society Research, 1(1), 1-11, January-March 2010 1
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Keywords: Innovation, Quantum Physics, Social Dynamics, Social Networks, Social Science
InTroduCTIon
In recent years new organizational forms are
emerging in response to new environmental
forces that call for new organizational and mana-
gerial capabilities. Organizational communities,
interdisciplinary teams and industry consortia
are becoming the governance model suitable
to build a sustainable competitive advantage,
representing a viable adaptation to an unstable
environment. The theoretical framework used
in this research is known as complexity science
(Clippinger, 1999; Newman, 2003). According
to this approach, teams are considered complex
adaptive systems (CAS): they co-evolve with
the environment because of the self-organizing
behavior of the agents determining fitness land-
scape of market opportunities and competitive
dynamics (Lewin, 1999). A system is complex
when equations that describe its progress over
time cannot be solved analytically (Pavard &
Dugdale, 2000). Understanding complex sys-
Quantum Modeling of
Social dynamics
C. Bisconti, University of Salento, Italy
A. Corallo, B. University of Salento, Italy
M. De Maggio, University of Salento, Italy
F. Grippa, University of Salento, Italy
S. Totaro, University of Salento, Italy
abSTraCT
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 specifc defned models; 3) visualize the macroscopic results
emerging during the analysis/simulation phases through a digital representation of the social network.
DOI: 10.4018/jksr.2010010101