The emergence of complex firms’ networks in Industrial Districts
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The emergence of complex firms’ networks in Industrial Districts Francesca Borrelli, Luca Iandoli, Cristina Ponsiglione , Giuseppe Zollo Workshop on Complexity and Management OXFORD, June 19-20, 2006 CLOE Computational Laboratory of Organizational Engineering University of Naples Federico II Department of Business and Managerial Engineering 1
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Workshop on Complexity and Management OXFORD, June 19-20, 2006. 1. The emergence of complex firms’ networks in Industrial Districts. Francesca Borrelli, Luca Iandoli, Cristina Ponsiglione , Giuseppe Zollo. CLOE Computational Laboratory of Organizational Engineering - PowerPoint PPT Presentation
Transcript of The emergence of complex firms’ networks in Industrial Districts
The emergence of complex
firms’ networks in Industrial Districts
Francesca Borrelli, Luca Iandoli, Cristina Ponsiglione, Giuseppe Zollo
Workshop on Complexity and ManagementOXFORD, June 19-20, 2006
CLOE Computational Laboratory of Organizational Engineering
University of Naples Federico IIDepartment of Business and Managerial Engineering
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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.
Abstract2
IDs as Complex Adaptive Systems
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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?
…a possible answer through Collective Memory
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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
Collective memory provides individuals and organizations with a stable set of meanings, supporting their inter-actions within the network
Repository of knowledge
(Penrose, 1956; Nelson and Winter, 1982; Walshand Ungson, 1991)
Evolving through collettive learning
(Herriot et al., 1988; Argyris and SchÖn, 1978)
Based on shared values
(Schein, 1985)
Socially constructed
(Berger and Luckmann, 1966)
Research Step 1: Conceptual model
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Messages from the environment
Messages to other agents
Environment
Environment Laws (EL)
Agent State (AS)
Agent-Agent Relationships (AAR)Agent-Environment Relationships (AER)
Messages from Collective Memory
Collective Memory provides frames to fill gaps of agents’ rules
Evaluation Rules (EV)_ _ _ _ o _ _ _ _ o _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _o _
Agents rules
Decision rules (DR)_ _ _ _ o _ _ _ _ o _ _ _ _
_ _ _ o_ _ _o _ _ _ _ _ _ _
gaps
Research step 1: computational model
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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 (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).
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)
Research step 1: the events of simulation
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The principal agent dies
NO
Livelli Target
Verifica dello stato Interno
Confronto tra i propri Livelli di competenza
e quelli target
Decisioni sulle competenze
da migliorare
Processidi miglioramento
Bdg<0
Decisions Results
YES
NO
Target Levels
Partner proximity
Market requests
Firms traces
Internal state check
Evaluation of competences gaps
Profit
Partner search
Bdg<0
Chain building
Evaluations Results
Chainbreak
YESNO
YES
NO
NO
Decisions about improvement strategies
improvement strategies
Research step 1: experimental sets8B
e ha v
iour
Memory
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.
Co o
pera
tive
No t
Co o
pera
t ive
Weak Strong
1. Stable Market
2. Turbulent Market
5. Stable Market
3. Stable Market
4. Turbulent Market
6. Turbulent Market
7. Stable Market
8. Turbulent Market
Results: Not-Cooperative Behaviour
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
0
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0 15 30 45 60 75 90 105 120 135PN
0
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0 15 30 45 60 75 90 105 120 135PN
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N = average number of survived firmsP = profit
Tes
t 7T
est
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Stable TurbulentN P N P
Weak 11.96 79.25 7.40 31.03Strong 11.34 66.86 6.42 19.10
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Tes
t 6
Tes
t 8
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.
0
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0 15 30 45 60 75 90 105 120 135P
N
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0 15 30 45 60 75 90 105 120 135P
N
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Tes
t 3
Stable TurbulentN P N P
Weak 11.66 79.78 8.20 40.42Strong 10.94 88.57 6.98 33.63
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Tes
t 1
Tes
t 2T
est 4
Questions and answers related to the model of step 1
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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
Framework of research step 212Firm
Is a set of
Actors
• whole organization• functions
• groups• individuals
Competences
Are sets of • strategic
• financial• marketing• technological
• productive•operative
Swarms of agents
Are• move• communicate messages
• interpret message
Set of fuzzy rules
Are• evaluation rules
• decisional rules
