Knowledge Externalities in geographical clusters: An agent-based simulation study

1. EIASM Workshop on Complexity and Management Oxford 19-20 June 2006 Knowledge Externalities in geographical clusters: An agent-based simulation study Vito Albino, Nunzia Carbonara, Ilaria Giannoccaro Politecnico di Bari Bari, Italy

2. Outline • Context & Theoretical Background • Geographical clusters (GCs) • Agglomeration external economies • New source of GC competitive advantage • Paper’s objective • Investigate the effects of knowledge externalities on GCs • Knowledge externalities & proximity • Methodology • Agent-based simulation • The Agent-based model • Simulation • Simulation Results • Conclusion

3. Geographical clusters • Geographical Clusters (GCs) • Geographically defined production systems • Large number of SMEs • Labor division • Specialization • Dense network of inter-firm relationships

4. Geographical clusters • Literature on GCs • Different stream of studies • social sciences, economic geography, regional economics, political economy, industrial organization • Research methodologies • Case studies • Surveys • Econometric analysis • Theoretical frameworks used to study the reasons of the GC competitiveness • flexible specialization model (Piore and Sabel, 1984), localized external economies (Marshall, 1920, Krugman, 1991), industrial atmosphere (Marshall, 1919), innovative milieux (Maillat et al., 1995)

5. Agglomeration external economies • Benefits that small firms can gain when they are agglomerated in a given area and belong to the same productive sector. • Sources of agglomeration external economies (Marshall,1920): • knowledge spill-over among competitors • specialized work forces with accumulation of technical competencies in the area enabling process productivity • existence in the area of specialized input providers • pooling of common factors of production (land, labour, capital, energy, transportation systems). • Economic advantages • reduction of production costs: due tothe high level of labor division and by the specialization of work force • reduction of transaction costs: due to the spatial and social proximity among firms

6. Source of GC competitive advantage MAIN PROBLEMS • In a knowledge-based economy the source of competitive advantage for firms is not more limited to a cost and differentiation advantages (static efficiency), but is linked to resources/competences that firms possess and their capabilities to create new knowledge (dynamic efficiency). • The long-term growth of organizations and thus of regions and nations depends on their ability to continually develop and produce innovative product and services.

7. Paper’s objective Identify new sources of GC competitive advantages Investigate whether knowledge-based externalities can drive the geographical clustering of firms

8. New source of GC competitive advantage • Positive externalities knowledge-based: knowledge externalities • Sources of knowledge externalities: • learning processes activated within GCs (collective learning, learning by interactions, learning by imitation) • Knowledge spill-over (intra-industry and inter-industry) – involuntary knowledge flows • Organized (voluntary) knowledge flows • Easy circulation of information and tacit knowledge • Presence of specialized work forces • Benefits of knowledge externalities: • High innovative capacity of firms • Ability to answer to the market changes • High flexibility • Reduction of time-response to customer demand

9. Knowledge externalities & learning processes Knowledge externalities Learning by imitation Learning by interaction

10. Learning processes Knowledge Externalities Geographical proximity Cognitive proximity Organizational proximity Knowledge externalities & proximity

11. Proximity • Geographical proximity • Spatial or physical distance between two firms • Cognitive proximity • Similarity of the knowledge stocks of two firms • Organizational proximity • Extent to which relations are shared in an inter-organizational and intra-organizational arrangements.

12. Proximity • Impact on learning processes: • To much proximity generate lock-in • To little proximity is not beneficial

13. Research Methodology (1/2) • Agent-based simulation (ABS) • Simulation methodology used to study complex adaptive systems • ABS analyzes the system behavior as the spontaneously result of the local interactions among heterogeneous and independent components. • ABS studies the system dynamics by adopting a bottom-up approach rather than a top-down one

14. Research Methodology (2/2) • Agent-based simulation (ABS) • ABS permits examination of the behavior of ‘real world’ systems by developing simplified analogous models of real systems. • ABS can be used to explore the behavior of ‘artificial’ systems in order to predict what might happen in the real world. • ABS allows to study processes in ways nature prohibits, given that it can be run many times with the values of the model parameters modified in each run and changes observed in outputs.

15. Agent Based Simulation • Uses • Test-bed for new ideas • Development of new theories • Decision-making aids • What-if training tools • Hypotheses generators

16. ABS: Application to GCs (1/2) • Boero and Squazzoni (2002) suggest an agent-based computational approach to describe the adaptation of GCs to the evolution of market and technology environments • Brenner (2001) develops a cellular automata model of the spatial dynamics of entry, exit, and growth of firms within a region • Zhang (2002) studies the formation of the high-tech industrial clusters

17. ABS: Application to GCs (2/2) • Brusco et al. (2002) develop a 3-dimension cellular automation model of a GC, which represents how GC firms share information about technology, markets, and products. In the model, different scenarios characterized by different degrees of information sharing among firms are compared • Fioretti (2001) develops a spatial agent-based computational model to study the formation and the evolution of the Prato industrial district • Albino et al. (2006) propose a multi-agent system model to study cooperation and competition in the supply chain of a GC.

18. The agent-based model (1/2) • Agents are involved in location choices. • The location decision only depends on the incentives caused by agglomeration externality. • Agglomeration economies are only based on knowledge externalities. • The benefits of knowledge externalities consist in the development of knowledge stock due to two learning processes, namely learning by imitation and learning by interaction. • Learning processes are affected by three different dimensions of proximity, namely geographical, cognitive, and organizational proximities. • The firm competitive success is directly proportional to the knowledge stock developed by the firms. • Software • NetLogo

19. The agent-based model (2/2) • Agents • Agents’ Actions • Measures • Model’s Dynamics

20. Agents • Agents  firms • Agents’ attributes • Position on the grid Pi,t (x,y), • Knowledge stock Ki,t. • Agents’ goal  maximize the fitness  maximize the competitive advantage  maximize the knowledge stock • Agents’ mental model  knowledge about the other agents • Agents’ dynamic  movement into the grid looking for new position with higher fitness

21. Actions • Learning • Developing knowledge stock • Choosing the new position • Moving in the selected position

22. Action: Learning (1/6) • Learning by imitation  Kij,imitation • Learning by interaction  Kij,interaction The effectiveness of learning processes is influenced by geographical, cognitive, and organizational proximity.

23. Action: Learning (2/6)

24. Action: Learning (3/6) if if

25. Action: Learning (4/6)

26. Action: Learning (5/6) if if

27. Action: Learning (6/6)

28. Action: Developing knowledge stock Where: the agent knowledge stock at step t the incoming knowledge flow due to the interaction with the other agents maximum value of knowledge that the agent can acquire through learning by imitation

29. 2 1 3 Pi,t 4 8 7 6 5 Action: Choosing the new position • Agent computes the CAi for every nine adjacent positions • Agent selects the new position that maximizes the development of CAi

30. 2 1 3 Pi,t 4 8 7 6 5 Action: Moving in the selected position

31. Measures • Emergence of spatial clusters • Number of clusters • Description of spatial clusters • the average knowledge stock of cluster (Kaverage); • the highest knowledge stock of cluster (Khighest); • the lowest knowledge stock of cluster (Klowest);

32. Model’s dynamics • Compute for the agent i the value of CAi for all possible new positions included the current one; • Choose the position that maximizes CAi; • Move agent i into the new position; • Update the value of Ki; • Repeat actions (a) through (d) until all agents have gone through that process; • Repeat steps (a) through (d) for as many simulated time steps as specified; • Compute the measures.

33. Simulation (1/3) • The base-line model • Number of agents N = 30 • Mean of the starting knowledge stock Ki,0= 100 • Standard deviation of the starting knowledge stock St.dev Ki,0= 5 • Propensity to create new knowledge through learning by interaction equal to the propensity to create new knowledge through learning by imitation α/γ= 1 • Influence of the geographical proximity on the learning by imitation βimitation= 2.5 • Influence of the geographical proximity on the learning by interaction βinteraction= 2 • Percentage of the inter-organizational agreements Org.agreements= 50%

34. Simulation (2/3) • Sensitivity analysis to evaluate the influence on the emerging clusters’ characteristics of: • Number of agents • Distribution of the starting knowledge stock • Learning process by imitation and by interaction • The geographical proximity • The organizational proximity

35. Simulation (3/3) Simulation plan Parameter EX1 EX2 EX3 EX4 EX5 EX6 EX7 EX8 EX9 EX10 EX11 30 30 30 20 40 30 30 30 30 30 30 N 100 100 100 100 100 100 100 100 100 100 100 Ki,0 St.dev Ki,0 5 5 5 5 5 2,5 10 5 5 5 5 1 1 1 1 1 1 1 0 0,5 2 1 α/ γ 5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 βimitation βinteraction 4 2 2 2 2 2 2 2 2 2 2 50% 100% 50% Org. agreements 50% 50% 50% 50% 50% 50% 50% 0% Simulation time = 100 steps Number of replications = 20

36. 112.6 147.0 104.4 120.0 110.0 177.2 131.0 100.0 108.0 154.3 114.8 105.2 108.6 100.8 127.4 126.0 214.6 112.0 107.2 127.6 102.8 218.8 105.6 128.8 110.4 91.6 99.8 105.6 96.4 125.2 162.7 470.7 127.2 11.8 0.5 1.8 14.4 1.2 1.9 2.4 1.0 16.6 2.1 1.9 7.7 2.6 5.2 26.4 0.8 0.4 8.4 3.6 8.0 46.6 3.5 0.4 3.0 7.3 1.1 1.8 5.3 88.9 16.7 16.0 1.0 2.5 Simulation results Outcome EX1 EX2 EX3 EX4 EX5 EX6 EX7 EX8 EX9 EX10 EX11 1 1 1 Number of cluster 1 1 1 1 0 1 1 1 • Kaverage • Mean • Std • Khighest • Mean • Std • Klowest • Mean • Std

37. Simulation results: discussion • Knowledge externalities motivate firms to geographically cluster • a geographical cluster of agents emerges in all the experiments regardless in the experiment where the firm’s propensity to create new knowledge through learning by interaction is equal to zero • A higher number of agents increases the average knowledge stock of the cluster • A greater cognitive heterogeneity of the GC firms increases the average knowledge stock of the cluster.

38. Conclusions • We have explored the concept of knowledge externalities • We have investigated whether the geographical clustering of firms can be driven by knowledge externalities. • We have developed and Agent-based model to address our research question • We have conducted sensitivity analysis to test the model • Further research and research validation

39. Research validation (1/2) • Comparison of ABS vs. other simulation methodologies • We have developed a Business Dynamics model and we have conducted a Dynamic Simulation Analysis (software: Vensim PLE) • Results show that knowledge externalities motivate firms to geographically cluster

40. Research validation (2/2) • Empirical validation • Analyzing the localization behaviors of firms (survey) • Localization choice driven by static efficiency • Localization choice driven by knowledge externalities • Measuring the level of attractiveness of different kinds of geographical area (survey) • Geographical area with low level of knowledge externalities vs. Geographical area with high level of knowledge externalities • Comparing the performance of firms operating in environments characterized by different levels of proximity (case study)