The emergence of complex

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Workshop on Complexity and Management OXFORD,
June 19-20, 2006
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• 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
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Abstract
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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.
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IDs as Complex Adaptive Systems

• ID is a network of autonomous and heterogeneous
  agents (Rullani, 1992)
• IDs coordination occurs by informal
  institutional mechanisms, such as reputation,
  trust, mutual learning, cooperation, etc
  (Becattini, 2000 Camagni, 1989 Rullani, 1989
  Uzzi, 1997)
• IDs 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?

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a possible answer through Collective Memory
Socially constructed (Berger and Luckmann, 1966)

• 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

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Research Step 1 Conceptual model
Agents rules
gaps
Evaluation Rules (EV) _ _ _ _ o _ _ _ _ o _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _o _
Decision rules (DR) _ _ _ _ o _ _ _ _ o _ _ _ _ _
_ _ o_ _ _o _ _ _ _ _ _ _
Messages from the environment
Messages to other agents
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Research step 1 computational model

• Three classes of Agents
• final firms (fin)
• subcontracting firms (sub)
• production chains (Pch)

• Internal state variables
• mi, ti , pi
• Represent the levels of market, technological
  and production competences of the firm at cycle
  i (1low, 2 medium, 3 high)
• oppi is firms 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 (0low, 1 high).
• riski is firms Risk Propensity
• Indicates agent inclination to carry out risky
  investments (0low, 1 high).
• 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.

IS (Si) f (mi, ti , pi , oppi , riski , bdgi)
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Research step 1 the events of simulation
Verifica
Internal state check
dello stato Interno
YES
The principal agent dies
Bdglt0
Bdglt0
NO
NO
Confronto tra i propri
Evaluation of competences gaps
Livelli di competenza
Livelli Target
Target Levels
e quelli target
Evaluations Results
Decisioni
Decisions about improvement strategies
sulle competenze
da migliorare
Decisions Results
Processi
improvement strategies
di miglioramento
Partner search
Firms traces
Chain building
Partner proximity
YES
NO
NO
Chain
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Profit
Market requests
break
YES
NO
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Research step 1 experimental sets
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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
Weak
Strong
1. Stable Market
3. Stable Market
Cooperative
2. Turbulent Market
4. Turbulent Market
Behaviour
7. Stable Market
Not Cooperative
5. Stable Market
6. Turbulent Market
8. Turbulent Market
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Results Not-Cooperative Behaviour
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Weak vs Strong 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
Test 7
Test 8
Test 6
Test 5
N average number of survived firms P profit
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Results Cooperative Behaviour
Weak vs. Strong 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.
Test 4
Test 3
Test 1
Test 2
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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
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Framework of research step 2
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Firm
Is a set of

• whole organization
• functions
• groups
• individuals

Actors
Are sets of

• strategic
• financial
• marketing
• technological
• productive
• operative

Competences
Are

• move
• communicate messages
• interpret message

Swarms of agents
Are
Set of fuzzy rules

• evaluation rules
• decisional rules