Imperfect Local Search Strategies on Technology Landscapes: Satisficing, Deliberate Experimentation and Memory Dependence
Computational Economics from EconWPA
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
Note: Type of Document - pdf; pages: 39
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpco:0405009
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