11

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

studystudy

Vito Albino, Vito Albino, Nunzia CarbonaraNunzia Carbonara, Ilaria Giannoccaro , Ilaria Giannoccaro Politecnico di BariPolitecnico di Bari

Bari, ItalyBari, Italy

EIASM Workshop on Complexity and ManagementOxford 19-20 June 2006

22

OutlineOutline Context & Theoretical BackgroundContext & Theoretical Background

Geographical clusters (GCs)Geographical clusters (GCs) Agglomeration external economiesAgglomeration external economies New source of GC competitive advantageNew source of GC competitive advantage

Paper’s objectivePaper’s objective Investigate the effects of knowledge externalities on GCsInvestigate the effects of knowledge externalities on GCs

Knowledge externalities & proximityKnowledge externalities & proximity MethodologyMethodology

Agent-based simulationAgent-based simulation The Agent-based modelThe Agent-based model Simulation Simulation Simulation Results Simulation Results ConclusionConclusion

33

Geographical clustersGeographical clusters

Geographical Clusters (GCs)Geographical Clusters (GCs) Geographically defined production systemsGeographically defined production systems

Large number of SMEsLarge number of SMEs

Labor divisionLabor division

SpecializationSpecialization

Dense network of inter-firm relationshipsDense network of inter-firm relationships

44

Geographical clustersGeographical clusters Literature on GCsLiterature on GCs

Different stream of studiesDifferent stream of studies• social sciences, economic geography, regional economics, political social sciences, economic geography, regional economics, political

economy, industrial organizationeconomy, industrial organization Research methodologiesResearch methodologies

• Case studiesCase studies• SurveysSurveys• Econometric analysisEconometric analysis

Theoretical frameworks used to study the reasons of the Theoretical frameworks used to study the reasons of the GC competitivenessGC competitiveness

• flexible specialization model (Piore and Sabel, 1984), localized flexible specialization model (Piore and Sabel, 1984), localized external economies (Marshall, 1920, Krugman, 1991), industrial external economies (Marshall, 1920, Krugman, 1991), industrial atmosphere (Marshall, 1919), innovative milieux (Maillat et al., atmosphere (Marshall, 1919), innovative milieux (Maillat et al., 1995)1995)

55

Agglomeration external economiesAgglomeration 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 to the 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

66

Source of GC competitive Source of GC competitive advantageadvantage

MAIN PROBLEMSMAIN 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.

77

Paper’s objectivePaper’s objective

Identify new sources of GC competitive advantagesIdentify new sources of GC competitive advantages

Investigate whether knowledge-based externalities Investigate whether knowledge-based externalities

can drive the geographical clustering of firmscan drive the geographical clustering of firms

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New source of GC competitive New source of GC competitive advantageadvantage

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

99

Knowledge externalities & learning Knowledge externalities & learning processesprocesses

Learning by

imitationLearning by

interaction

Knowledge externalities

1010

Knowledge externalities & Knowledge externalities & proximityproximity

Geographical proximity

Cognitive proximity

Organizational proximity

Learning processes

Knowledge Externalities

1111

ProximityProximity

Geographical proximityGeographical proximity Spatial or physical distance between two firmsSpatial or physical distance between two firms

Cognitive proximityCognitive proximity Similarity of the knowledge stocks of two firmsSimilarity of the knowledge stocks of two firms

Organizational proximity Organizational proximity Extent to which relations are shared in an Extent to which relations are shared in an

inter-organizational and intra-organizational inter-organizational and intra-organizational arrangements.arrangements.

1212

ProximityProximity

Impact on learning processes:Impact on learning processes: To much proximity generate lock-in To much proximity generate lock-in To little proximity is not beneficialTo little proximity is not beneficial

1313

Research Methodology (1/2)Research Methodology (1/2) Agent-based simulation (ABS) Agent-based simulation (ABS)

Simulation methodology used to study complex Simulation methodology used to study complex

adaptive systemsadaptive systems

ABS analyzes the system behavior as the ABS analyzes the system behavior as the

spontaneously result of the local interactions among spontaneously result of the local interactions among

heterogeneous and independent components. heterogeneous and independent components.

ABS studies the system dynamics by adopting a ABS studies the system dynamics by adopting a

bottom-up approach rather than a top-down onebottom-up approach rather than a top-down one

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Research Methodology (2/2)Research Methodology (2/2)

Agent-based simulation (ABS) Agent-based simulation (ABS) ABS permits examination of the behavior of ‘ABS permits examination of the behavior of ‘real worldreal world’ systems ’ systems

by developing simplified analogous models of real systems. by developing simplified analogous models of real systems.

ABS can be used to explore the behavior of ‘ABS can be used to explore the behavior of ‘artificialartificial’ systems in ’ systems in

order to predict what might happen in the real world.order to predict what might happen in the real world.

ABS allows to study processes in ways nature prohibits, given ABS allows to study processes in ways nature prohibits, given

that it can be run many times with the values of the model that it can be run many times with the values of the model

parameters modified in each run and changes observed in parameters modified in each run and changes observed in

outputs. outputs.

1515

Agent Based SimulationAgent Based Simulation

Uses Uses Test-bed for new ideasTest-bed for new ideas Development of new theoriesDevelopment of new theories Decision-making aidsDecision-making aids What-ifWhat-if training tools training tools Hypotheses generatorsHypotheses generators

1616

ABS: Application to GCs (1/2)ABS: Application to GCs (1/2)

Boero and Squazzoni (2002) suggest an agent-based Boero and Squazzoni (2002) suggest an agent-based

computational approach to describe the adaptation of GCs to the computational approach to describe the adaptation of GCs to the

evolution of market and technology environmentsevolution of market and technology environments

Brenner (2001) develops a cellular automata model of the spatial Brenner (2001) develops a cellular automata model of the spatial

dynamics of entry, exit, and growth of firms within a regiondynamics of entry, exit, and growth of firms within a region

Zhang (2002) studies the formation of the high-tech industrial Zhang (2002) studies the formation of the high-tech industrial

clusters clusters

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ABS: Application to GCs (2/2)ABS: Application to GCs (2/2) Brusco et al. (2002) develop a 3-dimension cellular automation model of Brusco et al. (2002) develop a 3-dimension cellular automation model of

a GC, which represents how GC firms share information about a GC, which represents how GC firms share information about

technology, markets, and products. In the model, different scenarios technology, markets, and products. In the model, different scenarios

characterized by different degrees of information sharing among firms characterized by different degrees of information sharing among firms

are comparedare compared

Fioretti (2001) develops a spatial agent-based computational model to Fioretti (2001) develops a spatial agent-based computational model to

study the formation and the evolution of the Prato industrial district study the formation and the evolution of the Prato industrial district

Albino et al. (2006) propose a multi-agent system model to study Albino et al. (2006) propose a multi-agent system model to study

cooperation and competition in the supply chain of a GC. cooperation and competition in the supply chain of a GC.

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The agent-based model (1/2)The agent-based model (1/2) Agents are involved in location choices. Agents are involved in location choices. The location decision only depends on the incentives caused by The location decision only depends on the incentives caused by

agglomeration externality. agglomeration externality. Agglomeration economies are only based on Agglomeration economies are only based on knowledge knowledge

externalities. externalities. The benefits of The benefits of knowledge knowledge externalities consist in the development externalities consist in the development

of knowledge stock due to two learning processes, namely learning of knowledge stock due to two learning processes, namely learning by imitation and learning by interaction. by imitation and learning by interaction.

Learning processes are affected by three different dimensions of Learning processes are affected by three different dimensions of proximity, namely geographical, cognitive, and organizational proximity, namely geographical, cognitive, and organizational proximities. proximities.

The firm competitive success is directly proportional to the The firm competitive success is directly proportional to the knowledge stock developed by the firms. knowledge stock developed by the firms.

SoftwareSoftware NetLogoNetLogo

1919

The agent-based model (2/2)The agent-based model (2/2)

AgentsAgents Agents’ ActionsAgents’ Actions MeasuresMeasures Model’s DynamicsModel’s Dynamics

2020

AgentsAgents

Agents Agents firms firms

Agents’ attributesAgents’ attributes Position on the grid Position on the grid Pi,tPi,t ( (xx,,yy), ), Knowledge stock Knowledge stock Ki,t.Ki,t.

Agents’ goal Agents’ goal maximize the fitness maximize the fitness maximize the competitive maximize the competitive

advantage advantage maximize the knowledge stock maximize the knowledge stock

Agents’ mental model Agents’ mental model knowledge about the other agents knowledge about the other agents

Agents’ dynamic Agents’ dynamic movement into the grid looking for new position movement into the grid looking for new position

with higher fitnesswith higher fitness

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ActionsActions

LearningLearning Developing knowledge stockDeveloping knowledge stock Choosing the new positionChoosing the new position Moving in the selected positionMoving in the selected position

2222

Action: Learning (1/6)Action: Learning (1/6)

Learning by imitation Learning by imitation Kij,imitationKij,imitation Learning by interaction Learning by interaction Kij,interactionKij,interaction

The effectiveness of learning processes is The effectiveness of learning processes is influenced by geographical, cognitive, and influenced by geographical, cognitive, and

organizational proximity.organizational proximity.

2323

Action: Learning (2/6)Action: Learning (2/6)

imitation

ddd

K ijimitationij

max

maxgeo._prox.

eraction

ddd

K ijeractionij

int

max

maxint

geo._prox.

0

0,2

0,4

0,6

0,8

1

1,2

0 10 20 30 40 50 60

geographical distance

deve

lope

d kn

owle

dge

2424

Action: Learning (3/6)Action: Learning (3/6)

ijimitationij KKK prox.cognitive_ if ji KK

0prox.cognitive_ imitationijK if ji KK

jieractionij KKK prox.cognitive_int

020406080

100120140160180200

0 20 40 60 80 100

cognitive distance

deve

lope

d kn

owle

dge

learning byimitation/learningby interaction

2525

Action: Learning (4/6)Action: Learning (4/6)

ijonalorganizatieractionimitationij pK 1org._prox.

int/

0

0,2

0,4

0,6

0,8

1

1,2

0 0,2 0,4 0,6 0,8 1 1,2

organizational proximity

deve

lope

d kn

owle

dge

learning by imitation/byinteraction

2626

Action: Learning (5/6)Action: Learning (5/6)

ij

imitation

onalorganizatiij

j

iijtotalimitationij p

ddd

KK

KKK

1

max

max,

if ji KK

0,

totalimitationijK if ji KK

ij

eraction

onalorganizatiij

ijijtotaleractionij pd

ddacKKK

1

int

max

max,int

ji KK ,

2727

Action: Learning (6/6)Action: Learning (6/6)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

0 20 40 60 80

distance

deve

lope

d kn

owle

dge

learning by imitation

learning by interaction

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Action: Action: Developing Developing knowledge stockknowledge stock

totalimitationijjij

totaleractionijii KKtKK,,int

max)()1t(

ij

totaleractionijK ,int

)(tK i 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

Where:

totalimitationijj

K,

max

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Action: Action: Choosing the new Choosing the new positionposition

Agent computes the Agent computes the CAiCAi for every nine adjacent positions for every nine adjacent positions

Agent selects the new position that maximizes the development of Agent selects the new position that maximizes the development of CAiCAi

Pi,t

1 2 3

4

567

8

ij

totalimitationjitotalimitationijjij

totaleractioniji KKKCA,,,int

max)t(

3030

Action: Action: Moving in the selected Moving in the selected positionposition

Pi,t

1 2 3

4

567

8

3131

MeasuresMeasures

Emergence of spatial clustersEmergence of spatial clusters Number of clustersNumber of clusters

Description of spatial clustersDescription of spatial clusters the average knowledge stock of cluster the average knowledge stock of cluster

((KaverageKaverage);); the highest knowledge stock of cluster the highest knowledge stock of cluster

((KhighestKhighest);); the lowest knowledge stock of cluster (the lowest knowledge stock of cluster (KlowestKlowest););

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Model’s dynamicsModel’s dynamicsa)a) Compute for the agent Compute for the agent ii the value of the value of CAiCAi for all for all

possible new positions included the current one;possible new positions included the current one;b)b) Choose the position that maximizes Choose the position that maximizes CAiCAi;;c)c) Move agent Move agent ii into the new position; into the new position;d)d) Update the value of Update the value of KiKi;;e)e) Repeat actions (a) through (d) until all agents Repeat actions (a) through (d) until all agents

have gone through that process;have gone through that process;f)f) Repeat steps (a) through (d) for as many Repeat steps (a) through (d) for as many

simulated time steps as specified;simulated time steps as specified;g)g) Compute the measures. Compute the measures.

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Simulation (1/3)Simulation (1/3) The base-line model The base-line model

Number of agents N = 30Number of agents N = 30 Mean of the starting knowledge stock Mean of the starting knowledge stock KiKi,0,0 = 100= 100 Standard deviation of the starting knowledge stock Standard deviation of the starting knowledge stock St.dev St.dev Ki,0Ki,0 = 5= 5 Propensity to create new knowledge through learning by interaction equal Propensity to create new knowledge through learning by interaction equal

to the propensity to create new knowledge through learning by imitation to the propensity to create new knowledge through learning by imitation αα //γγ = 1= 1

Influence of the geographical proximity on the learning by imitation Influence of the geographical proximity on the learning by imitation βimitationβimitation = 2.5= 2.5

Influence of the geographical proximity on the learning by interaction Influence of the geographical proximity on the learning by interaction ββinteractioninteraction = 2= 2

Percentage of the inter-organizational agreements Percentage of the inter-organizational agreements OrgOrg.agreements.agreements = 50%= 50%

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Simulation (2/3)Simulation (2/3) Sensitivity analysis to evaluate the Sensitivity analysis to evaluate the

influence on the emerging clusters’ influence on the emerging clusters’ characteristics of:characteristics of: Number of agentsNumber of agents Distribution of the starting knowledge stockDistribution of the starting knowledge stock Learning process by imitation and by Learning process by imitation and by

interactioninteraction The geographical proximityThe geographical proximity The organizational proximityThe organizational proximity

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Simulation Simulation (3/3)(3/3)Simulation planSimulation plan

30

100

5

1

2,5

2

50%

Parameter EX1

N

Ki,0

St.dev Ki,0

α/ γ

βimitation

βinteraction

Org. agreements

EX2 EX3 EX4 EX5 EX6 EX7 EX8 EX9 EX10 EX11

20

100

5

1

2,5

2

50%

40

100

5

1

2,5

2

50%

30

100

2,5

1

2,5

2

50%

30

100

10

1

2,5

2

50%

30

100

5

0

2,5

2

50%

30

100

5

0,5

2,5

2

50%

30

100

5

2

2,5

2

50%

30

100

5

1

5

4

50%

30

100

5

1

2,5

2

0%

30

100

5

1

2,5

2

100%

Simulation time = 100 stepsNumber of replications = 20

3636

Simulation resultsSimulation results

1Outcome EX1Number of

cluster

Kaverage

-Mean

-Std

EX2 EX3 EX4 EX5 EX6 EX7 EX8 EX9 EX10 EX11

1 1 1 1 0 1 1 1 11

114.8

2.4

108.6

2.1

126.0

5.2

107.2

0.8

128.8

8.0

99.8

0.4

105.6

1.1

162.7

16.7

112.6

1.8

108.0

1.9

120.0

2.5Khighest

-Mean

-Std

Klowest

-Mean

-Std

154.3

16.6

127.4

7.7

214.6

26.4

127.6

8.4

218.8

46.6

110.4

3.5

125.2

7.3

470.7

88.9

147.0

11.8

131.0

14.4

177.2

16.0

105.2

1.0

100.8

1.9

112.0

2.6

102.8

0.4

105.6

3.6

91.6

3.0

96.4

1.8

127.2

5.3

104.4

0.5

100.0

1.2

110.0

1.0

3737

Simulation results: discussionSimulation results: discussion

Knowledge externalities motivate firms to Knowledge externalities motivate firms to geographically clustergeographically cluster a geographical cluster of agents emerges in all the a geographical cluster of agents emerges in all the

experiments regardless in the experiment where the experiments regardless in the experiment where the firm’s propensity to create new knowledge through firm’s propensity to create new knowledge through learning by interaction is equal to zerolearning by interaction is equal to zero

A higher number of agents increases the A higher number of agents increases the average knowledge stock of the cluster average knowledge stock of the cluster

A greater cognitive heterogeneity of the GC A greater cognitive heterogeneity of the GC firms increases the average knowledge stock of firms increases the average knowledge stock of the cluster. the cluster.

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ConclusionsConclusions We have explored the concept of We have explored the concept of knowledge knowledge externalities externalities We have investigated whether the geographical We have investigated whether the geographical

clustering of firms can be driven by clustering of firms can be driven by knowledge knowledge externalities.externalities.

We have developed and Agent-based model to address We have developed and Agent-based model to address our research questionour research question

We have conducted We have conducted sensitivity analysis to test the modelsensitivity analysis to test the model Further research and research validation Further research and research validation

3939

Research validation (1/2)Research validation (1/2)

Comparison of ABS vs. other simulation Comparison of ABS vs. other simulation methodologiesmethodologies We have developed a Business Dynamics We have developed a Business Dynamics

model and we have conducted a model and we have conducted a Dynamic Dynamic Simulation Analysis (software: Vensim PLE)Simulation Analysis (software: Vensim PLE)

Results show that knowledge externalities Results show that knowledge externalities motivate firms to geographically clustermotivate firms to geographically cluster

4040

Research validation (2/2)Research validation (2/2) Empirical validationEmpirical validation

Analyzing the localization behaviors of firms (survey)Analyzing the localization behaviors of firms (survey)• Localization choice driven by static efficiencyLocalization choice driven by static efficiency• Localization choice driven by knowledge externalitiesLocalization choice driven by knowledge externalities

Measuring the level of attractiveness of different kinds Measuring the level of attractiveness of different kinds of geographical area (survey)of geographical area (survey)

• Geographical area with low level of knowledge externalities Geographical area with low level of knowledge externalities vs. Geographical area with high level of knowledge vs. Geographical area with high level of knowledge externalitiesexternalities

Comparing the performance of firms operating in Comparing the performance of firms operating in environments characterized by different levels of environments characterized by different levels of proximity (case study)proximity (case study)