Applications of the NKmodel

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Title: Applications of the NKmodel

1
Applications of the NK-model

2
Structure of the lecture

3
Appreciative theorising and formal modelling

Nelson and Winter (1982)
The evolutionary paradigm as opposed to
neoclassical and institutional economics
The need to combine appreciative theorising and
formal modelling, i.e. deductive and inductive
reasoning

4
Three main objectives of simulation

1. To aid intuition i.e. to come up with
explanations for stylised facts and appreciative
theories (Axelrod 1997)
2. To generate empirical hypotheses that can be
put to the test
3. To be friendly to history (and unfriendly to
the future)
Two principles KISS (keep it simple and stupid)
and TAPAS (take a previous model and add
something)

5
Useful core models for TAPAS strategy

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NK-model (Kauffman 1993 Levinthal 1997)

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Main properties of the NK-model

The number of local optima is positively related
to the complexity (K) of a system
Even for small K, finding the global optimum
requires exhaustive search also called global
search (2N)
The fitness of local optima tends towards the
mean for large N and K values (Kauffmans
complexity catastrophe)

8
Theoretical NK contributions

Bounded rationality (Frenken, Marengo, Valente
1999)
In evolutionary worlds survival depends on short
run profits. Local search (leading to local
optima) performs better than global search
(required to find the global optimum). Supports
the assumption of local search.
Imitation of complex strategies (Rivkin 2000)
The more complex a technology / business, the
more firms need to rely on innovation rather than
on imitation. Supports Resource-based/competence
view.
Technological paradigm (Altenberg 1995 Frenken
2004)
As the dimensionality of technology grows over
time, early developed components become rigid
(e.g., gasoline engine). Alternative notion of
lock-in.

9
Empirical NK contributions

Recombinant search (Fleming Sorenson 2001)
K is made operational by a measure of how often
two patent classes have been combines previously.
The more often, the lower the complexity. They
then test whether a large K means large
dispersion in success rate (as measured by
citation rate). Radical innovation can now be
understood as innovation by recombining
previously unconnected technologies.
Steam engine designs (Frenken Nuvolari 2004)
They reconstruct the design space, and then
analyse whether the competing technologies
substitute or co-exist (reflecting local optima).
Demythologises simple linear succession history

10
References

Altenberg, L. (1995), Genome Growth and the
Evolution of the Genotype-Phenotype Map, in W.
Banzhaf and F. H. Eckman (eds.), Evolution and
Biocomputation. Springer-Verlag Berlin
Heidelberg, pp. 205-259.
Axelrod, R., 1997, The Complexity of Cooperation.
Agent-Based Models of Competition and
Collaboration (Princeton University Press,
Princeton).
Fleming L, Sorenson O, 2001, Technology as a
complex adaptive system evidence from patent
data, Research Policy 30 (7) 1019-1039
Frenken, K. (2004). Innovation, Evolution and
Complexity Theory (Cheltenham UK and Northampton
MA Edward Elgar), forthcoming.
Frenken, K., L. Marengo, M. Valente, 1999,
Interdependencies, nearly-decomposability and
adaptation, in T. Brenner (Editor),
Computational Techniques to Model Learning in
Economics (Kluwer, Boston etc.), forthcoming.
Frenken, K., Nuvolari, A. (2004). The early
development of the steam engine An evolutionary
interpretation using complexity theory,
Industrial and Corporate Change 13, forthcoming.
Download at http//www.tm.tue.nl/ecis/Working20P
apers/eciswp89.pdf
Kauffman, S.A., 1993, The Origins of Order.
Self-Organization and Selection in Evolution
(Oxford University Press, Oxford and New York).
Levinthal, D., 1997, Adaptation on rugged
landscapes, Management Science 43, 934-950.
Nelson, R.R., en Winter, S.G. (1982). An
Evolutionary Theory of Economic Change. Cambridge
Mass. Harvard University Press.
Rivkin, J.W. (2000). Imitation of complex
strategies. Management Science, 46, 824-844.