The emergence of complex firms’ networks in Industrial Districts

1. 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 University of Naples Federico II Department of Business and Managerial Engineering

2. 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.

3. 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

4. …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 Based on shared values (Schein, 1985) Repository of knowledge (Penrose, 1956; Nelson and Winter, 1982; Walsh and Ungson, 1991) Evolving through collettive learning (Herriot et al., 1988; Argyris and SchÖn, 1978) 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 4

5. Collective Memory provides frames to fill gaps of agents’ rules Messages from Collective Memory Agent State (AS) Agent-Agent Relationships (AAR) Agent-Environment Relationships (AER) Environment Laws (EL) Environment 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 5

6. 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 (1=low, 2= medium, 3= high) • oppiis 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). • riskiis firm’s Risk Propensity • Indicates agent inclination to carry out risky investments (0=low, 1= high). • bdgiThe 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) 6

7. Research step 1: the events of simulation Verifica Internal state check dello stato Interno YES The principal agent dies Bdg<0 Bdg<0 NO NO Confronto tra i propri Evaluation of competences gaps Livelli di competenza Target Levels Livelli Target e quelli target Evaluations Results Decisioni Decisions about improvement strategies sulle competenze da migliorare Decisions Results improvement strategies Processi di miglioramento Partner search Firms traces Chain building Partner proximity NO NO YES Chain Profit 7 Market requests break NO YES

8. 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 Weak Strong 1. Stable Market 3. Stable Market Cooperative 2. Turbulent Market 4. Turbulent Market Behaviour 7. Stable Market 5. Stable Market Not Cooperative 6. Turbulent Market 8. Turbulent Market

9. 9 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 Test 7 Test 8 Test 6 Test 5 N = average number of survived firms P = profit

10. 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. Test4 Test3 Test1 Test2 10

11. 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. 12 Framework of research step 2 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