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

Karén Hovhannisian

Computational Economics from University Library of Munich, Germany

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.

Keywords: NK Model; Technology Landscape; Satisficing; Local Search; Simulation Analysis (search for similar items in EconPapers)
JEL-codes: C8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe, nep-evo and nep-exp
Date: 2004-05-10
Note: Type of Document - pdf; pages: 39
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