Cognitive Identity and Social Reflexivity of the Industrial District Firms. Going Beyond the "Complexity Effect" with Agent-Based Simulations

Industrial districts (IDs) are complex inter-organizational systems based on an evolutionary network of interactions among heterogeneous, localized, functionally integrated and complementary firms. With an agent-based prototype, we explore how cognitive processes and social reflexivity dynamics of ID firms affect technological adaptation and economic performance of ID as a whole. Rather than observing IDs just by the point of view of the so-called bottom-up emerging properties, we try to study how firms develop over time districtualized behavioral attitudes, through cognitive capabilities of typifying and contextualizing in a social sense their technological, organizational and economic action. The question is: do cognitive processes, like those mentioned, have a great impact on technological learning and economic performance of firms over time?

[1]  Cristiano Castelfranchi,et al.  Simulating Multi-Agent Interdependencies. A Two-Way Approach to the Micro-Macro Link , 1995, Social Science Microsimulation.

[2]  A. Shaw,et al.  On the Analytical Dimension of Proximity Dynamics , 2000 .

[3]  Cristina Boari,et al.  Networks within Industrial Districts: Organising Knowledge Creation and Transfer by Means of Moderate Hierarchies , 1999 .

[4]  Pietro Terna,et al.  How to build and use agent-based models in social science , 2000 .

[5]  Gérard Ballot,et al.  Technological change, learning and macro-economic coordination: An evolutionary model , 1999, J. Artif. Soc. Soc. Simul..

[6]  Guido Fioretti,et al.  Information Structure and Behaviour of a Textile Industrial District , 2001, J. Artif. Soc. Soc. Simul..

[7]  A. Giddens The Constitution of Society , 1985 .

[8]  T. Fuller,et al.  Small enterprises as complex adaptive systems: a methodological question? , 2001 .

[9]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[10]  Daniel A. Levinthal,et al.  ABSORPTIVE CAPACITY: A NEW PERSPECTIVE ON LEARNING AND INNOVATION , 1990 .

[11]  Pietro Terna Simulation tools for social scientists: Building agent-based models with SWARM , 1998, J. Artif. Soc. Soc. Simul..

[12]  J. March Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.

[13]  F. Craik,et al.  Levels of processing: A framework for memory research , 1972 .

[14]  Sergio Albertini,et al.  Networking and division of labour - The case of industrial districts in the North-East of Italy , 1999 .

[15]  Giorgio Gottardi,et al.  Evolutionary patterns of local industrial systems : towards a cognitive approach to the industrial district , 2000 .

[16]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[17]  Werner Sengenberger,et al.  Industrial districts and local economic regeneration , 1993 .

[18]  Mark H. Bickhard,et al.  Information and representation in autonomous agents , 2000, Cognitive Systems Research.

[19]  A. Kellerman,et al.  The Constitution of Society : Outline of the Theory of Structuration , 2015 .

[20]  Flaminio Squazzoni,et al.  Economic Performance, Inter-Firm Relations and Local Institutional Engineering in a Computational Prototype of Industrial Districts , 2002, J. Artif. Soc. Soc. Simul..

[21]  David A. Lane,et al.  Complexity and Local Interactions: Towards a Theory of Industrial Districts , 2002 .

[22]  Lucio Biggiero,et al.  Identity and Identification in Industrial Districts , 2012 .

[23]  Enzo Rullani,et al.  The Industrial District (ID) as a cognitive system , 2003 .

[24]  Rosaria Conte The necessity of intelligent agents in social simulation , 2000, Adv. Complex Syst..

[25]  Elisabet Viladecans-Marsal,et al.  The District Effect and the Competitiveness of Manufacturing Companies in Local Productive Systems , 1999 .

[26]  Joshua M. Epstein,et al.  Growing artificial societies , 1996 .

[27]  V. Albino,et al.  KNOWLEDGE TRANSFER AND INTER-FIRM RELATIONSHIPS IN INDUSTRIAL DISTRICTS : THE ROLE OF THE LEADER FIRM , 1998 .

[28]  Francesco Luna,et al.  Economic simulations in swarm: Agent - based modelling and object , 2000 .

[29]  G. Dosi Innovation, organization and economic dynamics : selected essays , 2000 .

[30]  Gn Gilbert Holism, individualism and emergent properties: an approach from the perspective of simulation , 1996 .

[31]  Thomas Malsch,et al.  Naming the Unnamable: Socionics or the Sociological Turn of/to Distributed Artificial Intelligence , 2001, Autonomous Agents and Multi-Agent Systems.

[32]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[33]  Lisa De Propris,et al.  Systemic Flexibility, Production Fragmentation and Cluster Governance , 2001 .

[34]  Gianni Lorenzoni,et al.  Resisting Organizational Inertia: The Evolution of Industrial Districts , 1999 .

[35]  G. Dosi Innovation, Organization and Economic Dynamics , 2000 .

[36]  Nigel Gilbert,et al.  Holism, Individualism and Emergent Properties , 1996 .

[37]  Rosaria Conte,et al.  Social Intelligence Among Autonomous Agents , 1999, Comput. Math. Organ. Theory.

[38]  Bart Nooteboom,et al.  Innovation, Learning and Industrial Organisation , 1999 .

[39]  Gianni Lorenzoni,et al.  The firms that feed industrial districts: A return to the Italian source , 1999 .

[40]  Mustafa Emirbayer,et al.  What Is Agency?1 , 1998, American Journal of Sociology.

[41]  Ivana Paniccia One, a Hundred, Thousands of Industrial Districts. Organizational Variety in Local Networks of Small and Medium-sized Enterprises , 1998 .

[42]  Alessandro Perrone,et al.  Agent-based methods in economics and finance : simulations in Swarm , 2002 .

[43]  Paul Moran,et al.  Personality Characteristics and Growth-orientation of the Small Business Owner-manager , 1998 .

[44]  Bruce Edmonds,et al.  Sociology and Social Theory in Agent Based Social Simulation: A Symposium , 2001, Comput. Math. Organ. Theory.