dimetic 2011 - maastricht floortje...

Lectures 7 and 8: Networks and complexity


Dimetic 2011 - Maastricht

Floortje Alkemade



Outline

Lecture 7: Models of innovation in networks

networks of innovators

networks of technologies

networks of products

Lecture 8: The NK-model and its application to technological systems

Technology choice when technologies are complex adaptivesystems

The relation between technological design complexity andtechnological performance



Networks of innovators

R. Cowan and N. Jonard. Network structure and the diffusion ofknowledge. Journal of Economic Dynamics and Control 28:1557-1575,2004



Questions

How do markets process information?

If the network structure is exogenous, how do the structuralproperties of the network affect aggregate outcomes?

If network formation is endogenous, what structures are likely toemerge?



Assumptions/Starting points

Tacit knowledge and the importance of face-to-face interactions

Knowledge exchange in industry networks in barter arrangements



Properties of real world networks

Local clustering but yet - information travels fast!



Agents on a network

The transition from a locally ordered structure to a disordered one viaa small world



Model steps

An agent i is characterized by a knowledge vector vi = (vi,c)Agents i and j interact (if connected and win-win)Agents gain part of the knowledge of the partner:2 knowledge categories, agent i dominates in c2, j in c1, thenvi,c1(t+ 1) = vi,c1(t) + α[vj,c1(t)− vi,c1(t)]vj,c1(t+ 1) = vj,c1(t)and similar for c2 Simulation: 500 agents, 10 links per agent, 25experts



Model outcomes - What do you think?

Highest knowledge levels - why?

Highest knowledge variance - why?



Knowledge levels

The economy-wide steady-state average knowledge level µ as afunction of the rewiring probability p.



Knowledge heterogeneity

Agent heterogeneity as measured by the steady-state knowledgevariance σ2 and the steady-state coefficient of variation of knowledgeσ µ



Implications

So what?



Burt versus Coleman

Closure: trust, intermediaries, reliable informationStructural holes: informational advantages, brokeringopportunities

Severalauthors have investigated which network structures are stable underthe assumptions that actor(s) strive for structural holes. But theprocesses guiding network formation may well be context dependent.



... from a dynamic perspective more and less integratedstructures, with stronger or weaker linkages betweenactivities, are complements rather than substitutes: theyhave comparative advantages in different stages of theinnovation process (Nooteboom 1999 - Research Policy)



Show model



Networks of technologies

G. Silverberg and B. Verspagen. A percolation model of innovation incomplex technology spaces. Journal of economic dynamics and control29:225-244, 2005



Stylized facts

Technical change is cumulative

Agents tend to search locally for new technologies

Technical change follows relatively ordered pathways

Highly skewed size distribution of innovation



The model

A lattice of technological nichesA lattice site aij can be in one of four states:

0 or technologically excluded by nature

1 or possible but not yet discovered

2 discovered but not yet viable

3 discovered and viable

A discovered technology only becomes viable when it can draw onan unbroken chain of supporting technologies already in use



The simulation

1 Initiate lattice with 0’s and 1’s (percolation model)

2 BPF (t) = (i, j(i)) where j(i) = maxj for which a(i,j) = 3

3 R&D, search (with some probability) with radius m around viablestates on BPF.



Technology performance lattice, discovered sites in red, viable sites lieon the path connected to the baseline



Diverging paths (left) converging paths (middle) and incrementalinnovation (right)



Clusters of innovations occur when disconnected islands of inventionsare joined to the BPF by cornerstone innovations



Screen shot of the simulation



Networks of products

C.A. Hidalgo, B. Klinger, A.-L. Barabasi, R. Hausmann. The productspace conditions the development of nations. Science 317:482-487,2007.



Some intuition

Economies grow by upgrading the products they produce andexport

The ability of a country to produce a product depends on itsability to produce other products

If two goods are related because they require similar institutions,infrastructure, physical factors, technology, or some combinationthereof, they will tend to be produced in tandem, whereasdissimilar goods are less likely to be produced together.



Methodology

1 Calculate a measure for proximity between goods

2 Compute Revealed Comparative Advantage for countries (usingtrade data)

3 Compute proximity measure (using RCAs)

4 Construct proximity matrix

5 Construct product space: a network where related goods are linked

6 Test whether countries do indeed diversify into related goods



Revealed Comparative Advantage (RCA)

φi,j = minP (RCAxi |RCAxj ), P (RCAxj |RCAxi),

RCAc,i =x(c, i)/

∑i x(c, i)∑

c x(c, i)/∑

c,i x(c, i)



Black squares: RCA > 1



Do countries develop RCA preferably in nearby goods?



Simulation results



Technology choice when technologies are complex adaptivesystems

F. Alkemade, K. Frenken, M. Hekkert, M. Schwoon. A complexsystems methodology to transition management. EvolutionaryEconomics 19:527-543, 2009



Motivaton

Major technological transitions are required for sustainabledevelopmentButComplexity of the domain prevents top-down steeringAim: To present a technology assessment methodology that takes intoaccount the complexity of the domain



Technological transitions

Desirability of alternative technological optionsActual strategy dictated by most preferred optionProblems:

Future performance often uncertain

Decisions now may cut-off future alternatives



Flexible strategies

Initial steps should be robust regarding changing evidenceInitial steps should be robust regarding changing preferences



NK-Fitness landscapes

Interdependent subsystems

Fitness of the overall system depends on combination ofsubsystems

Design space

Technological change is a search process in this design space

N subsystems

K interdependencies



Fig. 1 A design space withN = 3, K = 2 and fitnessfunction f



Assumptions

Only one subsystem is allowed to change at each time stepEach single transition step needs to be (weakly) fitness improving



Flexibility in technological transitions

Changing evidence concerning optima:Goal flexibility: the number of different optima that can be reachedafter the initial transition step has taken placeChanging evidence concerning non-optimal designs:Path flexibility: the number of different paths that lead to anoptimum given an initial transition stepChanging preferences:Preference flexibility: a step that meets both sets of preferences atthe same time



Fig. 2 An alternative fitnessfunction g



The transition towards a sustainable transport system

Complexity: system consists of subsystems, which functioninterdependently but can be changed independently

Uncertainty: the fitness of alternative designs can be assessed inprinciple ex ante, but is highly uncertain

Myopia: any technological transition will most likely occur in aseries of myopic rather than coordinated transition steps insubsystems, because any change in a sub-system is a veryexpensive and lengthy process

Multiple preferences: different social groups apply differentevaluation criteria to assess the desirability of alternative options



Methodology

Construct design space

Assign fitness values (economic as well as environmentalperformance)

Derive local and global optima

Compute flexibility measures

Draw conclusion regarding desirability of transition steps



1. Crude Oil

2. Coal

3. Natural Gas

4. Fossil Fuels Based Electricity

5. Non Biogenic Waste

6. Biomass

7. Wind Power

Carbon capture and

sequestration (CCS)

1. Yes

2. No

Process scale, process location,

and distribution to filling station

1. LCP (large, centralized pipeline)

2. LCG (large, centralized, gas-pipeline

3. LCT (large, centralized, truck)

4. MLP (medium, local, pipeline)

5. MLG (medium, local, gas-pipeline)

6. MLT (medium, local, truck)

7. SO (small, onsite)

Car fuel

1. Gasoline

2. (Synthetic) Diesel

3. CNG/CBG (compressed nat./biogas)

4. LPG (liquefied petroleum gas)

5. DME (dimethyl ether)

6. Ethanol

7. Methanol

8. LH2 (liquefied hydrogen)

9. CGH2 (compressed gaseous hydrogen)

Vehicle type

1. ICEV (internal combustion engine)

2. Hybrid-ICEV

3. (Reformer) FCV (Fuel cell vehicle)

Energy sources

1. Crude Oil

2. Coal

3. Natural Gas

4. Fossil Fuels Based Electricity

5. Non Biogenic Waste

6. Biomass

7. Wind Power

Carbon capture and

sequestration (CCS)

1. Yes

2. No

Process scale, process location,

and distribution to filling station

1. LCP (large, centralized pipeline)

2. LCG (large, centralized, gas-pipeline

3. LCT (large, centralized, truck)

4. MLP (medium, local, pipeline)

5. MLG (medium, local, gas-pipeline)

6. MLT (medium, local, truck)

7. SO (small, onsite)

Car fuel

1. Gasoline

2. (Synthetic) Diesel

3. CNG/CBG (compressed nat./biogas)

4. LPG (liquefied petroleum gas)

5. DME (dimethyl ether)

6. Ethanol

7. Methanol

8. LH2 (liquefied hydrogen)

9. CGH2 (compressed gaseous hydrogen)

Vehicle type

1. ICEV (internal combustion engine)

2. Hybrid-ICEV

3. (Reformer) FCV (Fuel cell vehicle)

Fig. 3 Subsystems of the WTW system



Fitness landscapes

Table 1 Optima of WTW systems

Current Global optimum Local optimum Local optimum Global optimumsystem with regard to A with regard B with regard with regard to

efficiency to efficiency to efficiency environment

Energy source Crude Oil Crude Oil Wind power NG BiomassCCS No No No No YesDistribution LCT LCG or LCT MLT LCG or LCT or LCT

MLG or MLTor SO

Fuel gasoline CGH2 LH2 CNG LH2Vehicle ICEV FCV FCV Hybrid-ICEV ICEV or

Hybrid-ICEVor FCV



Results

Economic performance:

Goal flexibility: all 4 steps remain flexible

Path flexibility: large differences, FCV most flexible

Preference flexibility: LCP, diesel, hybrid, FCV

Environmental performance:

Goal flexibility: irrelevant

Path flexibility: large differences, diesel most flexible

Preference flexibility: LCP, diesel, hybrid, FCV



Conclusions

Overall, it seems that changes in the vehicle technology towardseither a hybrid car or a fuel cell car is most desirable because it isfairly flexible in all the four flexibility dimensions discussed.

The methodology developed in this paper can lead to usefulinsights regarding optimal transition strategies.



The relation between technological design complexity andtechnological performance

J. McNerney, J.D. Farmer, S. Redner, and J.E. Trancik. Role of designcomplexity in technology improvement. PNAS 108(22):9008-9013



Four empirical performance curves



Motivation

The potential for exploiting performance curves has so far notbeen fully realized

Why do performance curves tend to look like power laws, asopposed to some other functional form?

What factors determine the exponent α, which governs thelong-term rate of improvement?



The model

A production design consists of n components

Total cost κ is the sum of component costs ci

ci changes if component changes

links between components given by Design Structure Matrix(DSM)



Fig. 2. Example design structure matrices (DSMs) with n ¼ 13 components.Black squares represent links. The DSM on the left was randomly generatedto have fixed out-degree for each component. The DSM on the rightrepresents the design of an automobile brake system (31). All diagonal ele-ments are present because a component always affects its own cost.

Relation between α and out-degree of a componentA component with higher out-degree (greater connectivity) is lesslikely to be improved when chosen.Overall improvement determined by slowest-improvementcomponent, governed by design complexity.



The simulation

1 Pick a random component i

2 Use the DSM to identify the set of components Ai = j whosecosts depend on i (the outset of i) j ∈ Ai from a fixed probabilitydistribution f

3 Determine a new cost c′j for each component

4 If total costs improve change



Results

j

i

Fig. 4. A component i (shaded circle), together with the componentsAi thatare affected by i (dotted ellipse) and the components that affect i (dashedellipse). The arrow from j to i indicates that a change in cost of component jaffects the cost of i.



A B C



Thank you



Alkemade and Nooteboom



Assumptions

Links are non-reflexive

Links are costly

Links are directed (only the initiator pays)

agents are boundedly rational (they cannot see entire network)

Innovative performance = absorptive capacity × novelty potential(see Nooteboom RP 2007)

Novelty potential = a1(1−ci)+b2(ndc)2 (Burt)

Absorptive capacity = b1(ndc)+a2(1−ci)2

ci ≡∑j 6=i

(pij +∑

k 6=i,k 6=j

pikpkj)2

where,p(i,j) is 1/di is the proportion of time that i has invested in contact j(as in Burt (1992) and Buskens and van de Rijt (AJS, 2009)).



Model steps

Each period an agent:

1 ranks his links (based on marginal innovative performance)

2 replaces a small percentage of its low ranking links by new links(these new links are chosen by sampling the population)

3 updates innovative performance

In the model there is entry and exit, agents with very low innovativeperformance have a small probabability of being replaced by a newagent.


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