Parametric interdependence, learning-by-doing, and industrial structure
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Parametric interdependence, learning-by-doing, and industrial structure William Martin Tracy · M.V. Shyam Kumar · William Paczkowski
Published online: 24 November 2012 © The Author(s) 2012. This article is published with open access at Springerlink.com
Abstract We explore the proposition that parametric interdependence makes learning-by-doing a nondeterministic, path-dependent process. The implications of our model challenge two conventional beliefs about the relationships between industrial structure, spillovers, and learning-by-doing. First, we challenge the belief that the monopolistic industrial structure always maximizes learning-by-doing gains when there are no spillovers. Second, we challenge the belief that increasing spillovers unambiguously increases welfare when learning-by-doing drives innovation. Keywords Learning-by-doing · Industrial structure · Spillovers · NK landscape · Parametric interdependencies
1 Introduction Parametric interdependency exists when the optimal setting of one parameter is impacted by the current setting of other parameters. For example, what is the optimal arrangement of magnets in an electric motor? The answer depends in part on whether the motor will run on AC or DC current. A magnet setting that is optimal on an AC motor might not work at all on a DC motor. Hence, there is an interdependency between the magnet placement and motor’s current; the optimal magnet placement is dependent on the current. W.M. Tracy () · M.V.S. Kumar · W. Paczkowski Lally School of Management and Technology, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA e-mail: [email protected] M.V.S. Kumar e-mail: [email protected] W. Paczkowski e-mail: [email protected]
Parametric interdependence, learning-by-doing, and industrial
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There is evidence that such interdependencies between technology parameters are widespread. Fleming and Sorenson (2001) proposed a technique for estimating the level of interdependence in a technology covered by a patent. In their analysis, they found evidence of parametric interdependence in all of the 17,264 patents they analyzed. Given the prevalence of parametric interdependence, it is important to consider the impact of parametric interdependencies on models of innovation and learning. The extent of interdependence among components or parameters of a technology is understood to impact that technology’s development (Kauffman et al. 2000). There has been prior work considering the impact of parametric interdependencies on learning-by-doing (cf. Auerswald 2010; Auerswald et al. 2000) and incremental innovation (Zhang and Gao 2010). However, prior work in this area has not addressed the socially optimal industrial structure, nor has it addressed the optimal level of spillovers. We argue that parametric interdependencies can moderate the received wisdom in both of these areas. Our arguments are predicated on the assertion that parametric interdependencies make technological innovation a nondeterministic, path-dependent process. To illustrate the intuition behind
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