#### Transcript The role of models in evolutionary economics and

Applications of the NK-model Koen Frenken URU, Utrecht University, The Netherlands ETIC and SIME 2 April 2004 Structure of the lecture 1. Appreciative theorising and formal modelling 2. A short review of evolutionary models and their use 3. NK-model (Kauffman 1993) 4. Theoretical applications of the NK-model 5. Empirical applications of the NK-model 6. Discussion 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 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) Useful core models for TAPAS strategy • Game theory to model strategic interaction • Replicator dynamics to model competition and industrial dynamics • Polya urn models, hypercycles, CA and NN to model diffusion • Graph/network theory to model the functioning and dynamics of networks • NK-model to model problem-solving in complex systems NK-model (Kauffman 1993; Levinthal 1997) 000: 001: 010: 011: 100: 101: 110: 111: w1 w2 w3 W 0.5 0.2 0.7 0.6 0.9 0.2 0.5 0.4 0.1 0.2 0.8 0.5 0.5 0.3 0.9 0.8 0.7 0.8 0.6 0.3 0.8 0.4 0.4 0.1 0.43 0.40 0.70 0.47 0.73 0.30 0.60 0.43 010 (0.70) 011 (0.47) 110 (0.60) 111 (0.43) 000 (0.43) 001 (0.40) 100 (0.73) 101 (0.30) 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 (Kauffman’s complexity catastrophe) 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. 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 coexist (reflecting local optima). Demythologises simple linear succession history 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/Working%20Papers/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.