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Imperfect Local Search Strategies on Technology Landscapes: Satisficing, Deliberate Experimentation and Memory Dependence

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  • Karén Hovhannisian

    (DISA, University of Trento)

Abstract

This paper contributes to the recent stream of literature on NK Model’s applications to the field of technological evolution. It is argued that while the model has a great explanatory potential in economics proper, its behavioral foundations are still maladapted for treatment of purportive decision-making strategies for technological innovation. Concentrating on the decision rule for accepting novelties, we first analyze the consequences of intentional and unintentional imprecision in following hill-climbing strategy, highlighting the interplay between rigidity and deliberate experimentation. Building on Simon’s insights on satisficing behavior and designing without final goals we build a simulative model that provides a possibility to compare strategies differing in the desired level of imprecision. Secondly, we shift our attention to the question of organizational memory, analyzing in a simulation setting a fully memory dependent and a fully memory independent innovation-related strategies. The results confirm that from the one hand up to a certain level “imperfection” of rule-following behavior is a virtue rather than a threat, while from the other, that past successes can preclude adaptability of the firm, while disregarding such successes can be very risky.

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File URL: http://128.118.178.162/eps/comp/papers/0405/0405009.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Computational Economics with number 0405009.

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Length: 39 pages
Date of creation: 10 May 2004
Date of revision:
Handle: RePEc:wpa:wuwpco:0405009

Note: Type of Document - pdf; pages: 39
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Web page: http://128.118.178.162

Related research

Keywords: NK Model; Technology Landscape; Satisficing; Local Search; Simulation Analysis;

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References

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  1. Koen Frenken & Luigi Marengo & Marco Valente, 1999. "Interdependencies, nearly-decomposability and adaption," CEEL Working Papers 9903, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
  2. M. L. Weitzman & K. Roberts, 1979. "Funding Criteria for Research, Development and Exploration Projects," Working papers 234, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. Jose Lobo & William G. Macready, 1999. "Landscapes: A Natural Extension of Search Theory," Working Papers 99-05-037, Santa Fe Institute.
  4. Bennett Levitan & Stuart Kauffman, 1995. "Adaptive Walks with Noisy Fitness Measurements," Working Papers 95-04-039, Santa Fe Institute.
  5. Kauffman, Stuart & Lobo, Jose & Macready, William G., 2000. "Optimal search on a technology landscape," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 141-166, October.
  6. Vishwanath, Tara, 1992. "Parallel Search for the Best Alternative," Economic Theory, Springer, vol. 2(4), pages 495-507, October.
  7. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-54, May.
  8. Levinthal, Daniel & March, James G., 1981. "A model of adaptive organizational search," Journal of Economic Behavior & Organization, Elsevier, vol. 2(4), pages 307-333, December.
  9. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, December.
  10. Teece, David J., 1993. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," Research Policy, Elsevier, vol. 22(2), pages 112-113, April.
  11. Jan W. Rivkin & Nicolaj Siggelkow, 2003. "Balancing Search and Stability: Interdependencies Among Elements of Organizational Design," Management Science, INFORMS, vol. 49(3), pages 290-311, March.
  12. Stuart Kauffman & William G. Macready & Emily Dickinson, 1994. "Divide to Coordinate: Coevolutionary Problem Solving," Working Papers 94-06-031, Santa Fe Institute.
  13. Daniel A. Levinthal, 1997. "Adaptation on Rugged Landscapes," Management Science, INFORMS, vol. 43(7), pages 934-950, July.
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Citations

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Cited by:
  1. Karén Hovhannisian & Marco Valente, 2005. "Modeling Directed Local Search Strategies on Technology," Computational Economics 0507001, EconWPA.
  2. Valente Houhannisian, 2004. "Modeling Directod Local Search Strategies on Technology Landscapes and Breadth," Quaderni DISA 091, Department of Computer and Management Sciences, University of Trento, Italy, revised 17 Jun 2008.
  3. Karén Hovhannissian & Marco Valente, 2004. "Modeling Directed Local Search Strategies on Technology Landscapes: Depth and Breadth," ROCK Working Papers 028, Department of Computer and Management Sciences, University of Trento, Italy, revised 17 Jun 2008.

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