A percolation model of innovation in complex technology spaces

Silverberg, Gerald; Verspagen, Bart

doi:10.1016/j.jedc.2003.05.005

Cited by 83 publications

(62 citation statements)

References 26 publications

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“…One way of modelling the innovation process that seems to generate exactly this kind of result has been proposed by Silverberg (2002) and explored theoretically and empirically by Verspagen (2002, Silverberg andVerspagen 2003b). Invoking percolation theory to represent a multidimensional technology space, this model shows how clustering can occur naturally both in the temporal and 'technospatial' domains without any explicit recourse to a long-wave argument.…”

Section: Schumpeter's Conceptual Framework: Clustering Of Innovationsmentioning

confidence: 99%

Long Waves: Conceptual, Empirical and Modelling Issues

Silverberg¹

2007

Elgar Companion to Neo-Schumpeterian Economics

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Section: Schumpeter's Conceptual Framework: Clustering Of Innovationsmentioning

confidence: 99%

Long Waves: Conceptual, Empirical and Modelling Issues

Silverberg¹

2007

Elgar Companion to Neo-Schumpeterian Economics

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“…Of course, the actual value of τ * is not determined by the above equations, so that in a system composed of a large number of local communities of agents with randomly distributed τ values a complex time evolution emerges, which is locally governed by Eqs. (8,9).…”

Section: Mean Field Versus Local Interactionmentioning

confidence: 99%

Cellular automata for the spreading of technologies in socio-economic systems

Kun

Kocsis

Farkas

2007

Physica A: Statistical Mechanics and its Applications

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We introduce an agent-based model for the spreading of technological developments in socio-economic systems where the technology is mainly used for the collaboration/interaction of agents. Agents use products of different technologies to collaborate with each other which induce costs proportional to the difference of technological levels. Additional costs arise when technologies of different providers are used. Agents can adopt technologies and providers of their interacting partners in order to reduce their costs leading to microscopic rearrangements of the system. Analytical calculations and computer simulations revealed that starting from a random configuration of different technological levels a complex time evolution emerges where the spreading of advanced technologies and the overall technological progress of the system are determined by the amount of advantages more advanced technologies provide, and by the structure of the social environment of agents. We show that agents tend to form clusters of identical technological level with a power law size distribution. When technological progress arises, the spreading of technologies in the system can be described by extreme order statistics.

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“…More recently Frenken has used the Auerswald model to interpret and address questions such as the efficacy of outsourcing (23,24). Other related models that use random search to model technological progress (but which do not directly address performance curves) are those of Silverberg and Verspagen (25,26) and Thurner et al (27).…”

mentioning

confidence: 99%

Role of design complexity in technology improvement

McNerney

Farmer

Redner

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

121

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We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d − 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.design structure matrix | experience curve | learning curve | performance curve

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A percolation model of innovation in complex technology spaces

Cited by 83 publications

References 26 publications

Long Waves: Conceptual, Empirical and Modelling Issues

Long Waves: Conceptual, Empirical and Modelling Issues

Cellular automata for the spreading of technologies in socio-economic systems

Role of design complexity in technology improvement

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