[JAVASCRIPT] ©Copyright JASSS
Francesca Borrelli, Cristina Ponsiglione, Luca Iandoli and Giuseppe Zollo (2005)
Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an
Agent-Based Approach
Journal of Artificial Societies and Social Simulation vol. 8, no. 3
<http://jasss.soc.surrey.ac.uk/8/3/4.html>
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Received: 12-Jan-2005 Accepted: 30-May-2005 Published: 30-Jun-2005
Abstract
Literature about Industrial Districts has largely emphasized the importance of both economic and social factors in determining the
competitiveness of these particular firms' clusters. For thirty years, the Industrial District productive and organizational model
represented an alternative to the integrated model of fordist enterprise. Nowadays, the district model suffers from competitive gaps,
largely due to the increase of competitive pressure of globalization. This work aims to analyze, through an agent-based simulation
model, the influence of informal socio-cognitive coordination mechanisms on district's performances, in relation to different
competitive scenarios. The agent-based simulation approach is particularly fit for this purpose as it is able to represent the Industrial
District's complexity. Furthermore, it permits to develop dynamic analysis of district's performances according to different types of
environment evolution. The results of this work question the widespread opinion that cooperative districts can answer to
environmental changes more effectively than non-cooperative ones. In fact, the results of simulations show that, in the presence of
turbulent scenarios, the best performer districts are those in which cooperation and competition, trust and opportunism balance out.
Keywords:
Firm Networks, Collective Memory, Agent Based Models, Uncertainty
Introduction
1.1
The notion of Industrial District (ID) was introduced by Alfred Marshall in 1919; he identified the external economies for the firm as
a crucial factor of competitiveness for localized systems of specialized small and medium enterprises. Nevertheless, only after World
War II we saw a "renewal" of the ID's model in the economic thought.
1.2
Becattini (1979) identified the ID as an elementary and autonomous unit of analysis. The ID conceptualization proposed by literature
(Aydalot 1986; Becattini 1989; Brusco 1982; Camagni 1989; Camagni 1991; De Rosa and Turri 2002; Fuà and Zacchia 1983;
Garofoli 1978; Rullani 1992) is characterized by two central elements:
ID's structure is based on a dense and strong network of relationships among autonomous and heterogeneous agents (firms,
families, local institutions);
ID's competitiveness can be attribute to the co-evolution of district's productive organization and of local formal and informal
institutions.
For thirty years, the ID productive and organizational model represented an alternative to the integrated model of fordist enterprise.
Nowadays, the district model suffers from competitive gaps, which are largely due to the increase of competitive pressure related to
globalization phenomena.
1.3
This work aims to analyze, through an agent-based simulation model, the influence of informal socio-cognitive coordination
mechanisms on district's performances with respect to different competitive scenarios, whereas the most recent applications of social
computation to firms clusters and supply chains are usually focused on productive and managerial coordination mechanisms (Boero
and Squazzoni 2001; Strader, Lin and Shaw 1998; Péli and Nooteboom 1997).
1.4
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In general, agent-based simulation enables to analyse self-organising systems, learning systems and systems with high complexity.
With reference to the latter, Arthur, Durlauf and Lane (1997) identify the following elements characterising Complex Adaptive
Systems (CAS):
wide interaction among agents operating at local level;
lacking of a central controller;
multi-level organisation with distributed interactions;
continual adaptation;
presence of turbulent elements (new markets, new technologies, new behaviour);
bounded rationality;
agents adaptation and continuous evolution.
These elements can be easily found in the ID's model; in fact IDs are adaptive learning agent-based systems with bounded rationality,
where agents interact at different levels, in other words are Complex Adaptive System.
Background
2.1
In section 2.2 some recent applications of agent-based models to IDs and firms' networks are reviewed, in order to place the
contribute of this work in the research agenda about the development of models to study IDs' dynamics. Section 2.6 reviews main
literature approaches to IDs, in order to outline the theoretical underpinnings of the proposed agent-model.
Modeling firms networks and ID through agent-based simulation: a review of existing approaches
2.2
In this paper, the dynamic analysis of IDs is made up through an agent-based computational approach.
2.3
Some recent researches proposed the agent-based approach to the analysis of IDs, firm's networks and supply chains (Boero and
Squazzoni 2001; Strader, Lin and Shaw 1998; Péli and Nooteboom 1997). Such applications show a similar architectural platform.
This platform is based on five conceptual blocks:
classes of agents (entities);
agents, in terms of objectives and rules of evaluations and decisions;
environment structure;
interaction context;
structure of the information flow.
Table 1 summarizes the characterization of these blocks with respect to some relevant recent applications selected as examples.
Table 1: Examples of simulation models applied to IDs, firms' networks and supply chains
Authors
Boero, Squazzoni (2001) Péli, Nootebom (1997) Strader et al. (1998)
Building blocks
1. Entities Firm's classes (final firms and
sub-contracting firms) of an industrial
district
User firms and supplier firms Supplier, manufacturers, assemblers,
distributors, and customers
2. Agents
(algorithms and
goals)
Technology absorption, learning
dynamics
Learning dynamics: learning
by doing and learning by
interacting
Decision making rules. Demand
management policies (MTO, ATO,
MTS)
3. Environment
structure
(exogenous laws)
Technology and market as selection
mechanism
Technology and market
structure
Demand structure
4. Interaction
context
Production chain, spatial and
organisational proximity
Supply partnership Divergent assembly supply
network
5. Information
flow
External environment/agents;
agents/agents
External environment/agents;
agents/agents
External environment/agents;
agents/agents
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