Workshop on Complexity and Management
OXFORD, June 19-20, 2006
The emergence of
complex
firms’ networks in
Industrial Districts
Francesca Borrelli, Luca Iandoli,
Cristina Ponsiglione, Giuseppe Zollo
CLOE Computational Laboratory of Organizational Engineering
University of Naples Federico II
Department of Business and Managerial Engineering
1
Abstract
2
The aim is to analyse the role of the Collective
Memory on the organization of an Industrial District
(ID).
Two different stages of an agent-based computational
research project are proposed.
IDs as Complex Adaptive Systems

ID is a network of autonomous and heterogeneous agents (Rullani, 1992)

ID’s coordination occurs by informal institutional mechanisms, such as
reputation, trust, mutual learning, cooperation, etc (Becattini, 2000; Camagni,
1989; Rullani, 1989; Uzzi, 1997)

ID’s competitiveness is related to socio-cognitive coordination mechanisms
(Aydalot, 1986; Becattini, 1989; Camagni, 1989)

ID is a Complex Adaptive System (Arthur, Durlauf and Lane, 1997; Boero and
Squazzoni, 2001)

Agent-based models of firms cluster are mainly focused on operations
management (Boero and Squazzoni, 2001; Strader, Lin and Shaw, 1998; Pèli
and Nooteboom, 1997)
How to translate the socio-cognitive coordination mechanisms into an
operational construct that can be implemented through an agent-based
model?
3
…a possible answer through
Collective Memory
Collective memory provides individuals and organizations with a stable set of
meanings, supporting their inter-actions within the network
Socially
constructed
(Berger and Luckmann,
1966)
Based on shared
values
Repository of
knowledge
(Penrose, 1956; Nelson and
Winter, 1982; Walsh
and Ungson, 1991)
(Schein, 1985)
Evolving through
collettive learning
(Herriot et al., 1988; Argyris
and SchÖn, 1978)
The Collective Memory is fuzzy:
-rules and values contained in the collective memory are ambiguous
and partially conflicting;
- each network agent has a different degree of membership to the
collective memory
4
Research Step 1: Conceptual model
Collective Memory provides frames to fill gaps of agents’ rules
Agents rules
Messages from
Collective Memory
Messages from the
environment
gaps
Evaluation Rules (EV)
____o____o____
_ _ _ _ _ _ _ _ _ _ _ _ _o _
Decision rules (DR)
____o____o____
_ _ _ o_ _ _o _ _ _ _ _ _ _
Messages to
other agents
Agent State (AS)
Agent-Agent Relationships (AAR)
Agent-Environment Relationships (AER)
Environment Laws (EL)
Environment
5
Research step 1: computational model
Three classes of Agents:
 final firms (fin)
subcontracting firms (sub)
production chains (Pch)
Internal state variables
IS (Si) = f (mi, ti , pi , oppi , riski , bdgi)
 mi, ti , pi
Represent the levels of market, technological and production competences of the
firm at cycle i (1=low, 2= medium, 3= high)
 oppi is firm’s Degree of Opportunism .
For final and subcontracting firms opp. influences their attitude in building up a
production chain; while, for production chain in breaking up the chain (0=low, 1= high).
 riski is firm’s Risk Propensity
Indicates agent inclination to carry out risky investments (0=low, 1= high).
6
bdgi The budget function
It computes the amount of economic resources of the firm. For each cycle, the value
increases or decreases according to firms choices.
Research step 1: the events of simulation
Verifica
Internal
state check
dello stato Interno
YES
The principal agent dies
Bdg<0
NO
Livelli Levels
Target
Target
Confronto tra i propri
Evaluation of
Livelli di competenza
competences gaps
e quelli target
Evaluations Results
Decisioni
Decisions about
sulle
competenze
improvement strategies
da migliorare
Decisions Results
improvement
Processi
strategies
di miglioramento
Partner search
Firms traces
Chain
building
Partner proximity
NO
Market requests
7
YES
Chain
break
Profit
NO
YES
8 Research step 1: experimental sets
Hypothesis: Collective memory has a moderating effect between ID
performances and environmental changes; i.e. ID performances in turbulent
rather than in stable scenario depends on the contents of collective memory.
Memory
1. Stable Market
3. Stable Market
2. Turbulent Market
4. Turbulent Market
5. Stable Market
6. Turbulent Market
7. Stable Market
8. Turbulent Market
Results: Not-Cooperative Behaviour 9
20
20
16
16
12
12
8
8
4
4
N 0
P 0
N 0
P 0
15
30 45 60
75 90 105 120 135
20
20
16
16
12
12
8
8
4
4
N 0
P 0
N 0
P0
15 30 45 60 75 90 105 120 135
N = average number of survived firms
P = profit
Weak vs Strong:
15
30
45
60
75
90
105 120 135
Increasing variety
leads to a growth in
profit (P) and in the
number of survived
firms (N) in both
stable and turbulent
cases.
In turbulent cases
increase in diversity
is rewarded more
than in the stable
case in terms of
profits
15
30
45
60
75
90 105 120 135
Stable
Weak
Strong
N
11.96
11.34
P
79.25
66.86
Turbulent
N
P
7.40 31.03
6.42 19.10
Results: Cooperative Behaviour
N
20
20
16
16
12
12
8
8
4
4
0
P
N
0
15
30
45
60
75
N0
P 0
90 105 120 135
20
20
16
16
12
12
8
8
4
4
0
N 0
P 0
P 0
15
30
45
60
75
90 105 120 135
Stable
Weak
Strong
N
11.66
10.94
P
79.78
88.57
Weak vs. Strong
15
30
45
60
75
90
105 120 135
Increasing variety
among agents of
the starting
population raises
the average number
of survived firms
even if this means
decreasing
cooperation levels.
Only in turbulent
scenarios the
increase in diversity
is rewarded.
15
30
45
60
75
Turbulent
N
P
8.20
40.42
6.98
33.63
90
105 120 135
10
Questions and answers related to the model of step 1
Q1) Messages are not fuzzy
A1) Fuzziness is important to foster organizational learning
Q2) Memory is not fuzzy
A2) The fuzziness is determinant to foster organizational learning
Q3) Internal structure of firm-agents is underestimated
A3) The firm is a set of actors;
each actor is a set of competencies;
each competence is a set of fuzzy rules determining the action
Q4) The model lacks of realism
A4) Development of an empirical methodology to study a real ID
11
12
Framework of research step 2
Firm
Is a set of
• whole organization
• functions
Actors
Are sets of
• groups
• individuals
• strategic
• financial
Competences
• marketing
• technological
• productive
Are
•operative
• move
Swarms of agents
• communicate messages
• interpret message
Are
Set of fuzzy rules
• evaluation rules
• decisional rules