Search and the introduction of improved technologies

Search and the introduction of improved technologies

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Article ID: iaor201112771
Volume: 58
Issue: 6
Start Page Number: 578
End Page Number: 594
Publication Date: Sep 2011
Journal: Naval Research Logistics (NRL)
Authors: , ,
Keywords: simulation: applications, markov processes, adaptive processes, optimization, learning
Abstract:

Modeling R&D as standard sequential search, we consider a monopolist who can implement a sequence of technological discoveries during the technology search process: he earns revenue on his installed technology while he engages in R&D to find improved technology. What is not standard is that he has a finite number of opportunities to introduce improved technology. We show that his optimal policy is characterized by thresholds ξi(x): introduce the newly found technology if and only if it exceeds ξi(x) when x is the state of the currently installed technology and i is the number of remaining introductions allowed. We also analyze a nonstationary learning-by-doing model in which the monopolist's experience in implementing new technologies imparts increased capability in generating new technologies. Because this nonstationary model is not in the class of monotone stopping problems, a number of surprising results hold and several seemingly obvious properties of the stationary model no longer hold.

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