دانلود مقاله ISI انگلیسی شماره 20249
عنوان فارسی مقاله

بهره برداری، اکتشاف و نوآوری در یک مدل رشد درون زا با عوامل محلی تعامل

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
20249 2003 37 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Structural Change and Economic Dynamics, Volume 14, Issue 3, September 2003, Pages 237–273

کلمات کلیدی
نوآوری - رشد درون زا - تعاملات محلی - اکتشاف در مقابل بهره برداری
پیش نمایش مقاله
پیش نمایش مقاله بهره برداری، اکتشاف و نوآوری در یک مدل رشد درون زا با عوامل محلی تعامل

چکیده انگلیسی

The paper presents a model of endogenous growth in which firms are modeled as boundedly-rational, locally interacting, agents. Firms produce a homogeneous good employing technologies located in an open-ended technological space and are allowed to either imitate existing similar practices or to locally explore the technological space to find new, more productive, techniques. We first identify sufficient conditions for the emergence of empirically plausible GDP time-series characterized by self-sustained growth. Then, we study the trade-off between individual rationality and collective outcomes by providing an example in which more rational agents systematically perform worse than less rational ones.

مقدمه انگلیسی

The analysis of the determinants of self-sustained processes of economic growth fueled by technological advances has received an increasing attention in the past few years. On the theoretical side, ‘Endogenous Growth’ and ‘Evolutionary’ models have been trying to explain how positive feedbacks in knowledge accumulation affect per-capita income growth (Romer, 1990, Grossman and Helpman, 1991, Nelson and Winter, 1982, Verspagen, 1993 and Silverberg and Verspagen, 1994). On the empirical side, a rapidly expanding literature on the economics of technological change has been instead exploring the drivers of innovation and diffusion at the levels of firms, sectors and whole Countries (see, among others, Freeman, 1994, Rosenberg, 1994, Nelson, 1995 and Stoneman, 1995). Notwithstanding this great effort, many scholars have recently spelled out a negative assessment on the extent to which ‘neoclassical’, ‘endogenous’ and ‘evolutionary’ growth theories have been able to match ‘old’ and ‘new’ growth ‘stylized facts’ and to provide ‘fresh’ testable implications (cf. Durlauf and Quah, 1998, McGrattan and Schmitz, 1998 and Silverberg and Verspagen, 1996). As argued by a large body of literature (cf. e.g. Nelson, 1998 and Dosi et al., 1994), these difficulties are mainly due to the large gap still existing between what we historically know about the microeconomics of technical change, innovation and technological diffusion, and the ways we represent that knowledge in formal models. For example, economic growth models do not usually account for both systematic heterogeneity observed in technological competencies and the fine details of the mechanisms governing the dynamics of interactions among economic agents. However, microeconomic diversity and institutional settings have been shown to affect in non trivial ways the properties of aggregate dynamics. Hence, any ‘representative agent’ reduction employed by a good deal of contemporary literature might turn out to be misleading whenever heterogeneity and interactions are important factors in explaining economic growth (see Kirman, 1992 and Kirman, 1998). Furthermore, technological advances typically involve business firms whose R&D activity is characterized by routinized decisions, trial-and-error, mistakes and unexpected discoveries (cf. Dosi and Lovallo, 1998). Consequently, forward-looking rationality typically imputed to agents in standard models of growth might not be a good proxy, especially when firms face complicated environments where novelty endogenously emerges as the outcome of others’ behaviors (cf. Conlisk, 1996 and Dosi et al., 2003). In economies populated by heterogeneous agents (e.g. firms) who repeatedly interact, innovate and adaptively learn about the world where they live in, observed aggregate regularities can hardly be understood as equilibrium paths (Silverberg and Verspagen, 1997). Empirically observed properties of macroeconomic time-series might be instead more fruitfully interpreted as ‘metastable’ regularities emerging in a complex evolving system. For example, the observed regularities displayed by the patterns of self-sustained GDP aggregate growth may be described as emergent properties of an economy composed of many heterogeneous simple firms interacting in some properly defined technological space (cf. Lane, 1993a and Lane, 1993b). Following this intuition, we present a computer-simulated model of endogenous growth in which simple, boundedly-rational firms produce a homogeneous good employing technologies located in an open-ended (i.e. without boundaries) productivity space. Technologies located close to each other have similar productivities, while more distant technologies perform better on average. Entrepreneurs can either imitate existing practices (similar to the one they currently master) or locally explore the technological space to find new and more productive techniques (i.e. innovate). We ask whether (and, if so, in which technological regimes) such an economy is able to generate self-sustaining patterns of aggregate growth with statistical properties similar to those displayed by empirically observed time-series. The paper is organized as follows. In Section 2, we outline in more detail the building blocks and theoretical conjectures supporting the model described in Section 3. Next, in 4, 5 and 6, we present an extensive analysis of computer simulations. Section 7 discusses some econometric properties of the simulated time-series. The tension arising in the model between individual rationality and collective performance is illustrated with a simple example in Section 8. Finally, Section 9 draws some conclusions and flags research developments ahead.

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