This paper attempts to generalise some results obtained in previous work showing the conditions under which paradigm setters emerge. We distinguish two different but definitely complementary and overlapping ways through which searching and learning occur. The first exploits the spillover potential that lies in a firm's network and thanks to which gathering innovation-useful information is actually possible. The second rests with the autonomous capacity that a firm possesses in order to carry out in-house innovative search. While these two searching processes not only coexist but are also reciprocally sustaining, we find it expedient to separate them by integrating a knowledge diffusion mechanism that propagates technological capabilities with an independent stochastic process capturing innovation arrivals due to internal R.&D. A network's evolution depends on how firms assess their performance in terms of innovation-enabling spillovers. In a bounded rationality framework, firms normally explore a limited part of the firms' space and require a protocol to target their information gathering efforts. The paper addresses this issue by designing a routinised behaviour according to which firms periodically reshape the neighbourhood that they observe to glean information by reassessing other firms' contributions to their own capability. The way the specific neighbour-choosing routine is accordingly organised determines in a significant way firms' average innovative capability. This feature is modelled by changing the span of network observation from a very broad setting, the whole economy, to a very narrow one, namely the most proximate neighbourhood membership. The economy is further portrayed as a collection of cognitively heterogeneous agents possessing firm specific knowledge and, thus, firm specific innovative capability. We also find it expedient to classify this assumed population according to their capability to capture broadcast information. This procedure implies viewing the economy as an ensemble of areas of cognitive exchange within which knowledge spillovers flow with equal ease. This approach to modelling interaction bears an important implication: the choice of new neighbours poses the problem of a trade-off between easily obtainable information, yet carrying low innovation empowering content, and hard to acquire, because cognitively distant, information but possibly conveying high capability contributions. To keep the model mathematically tractable, we formalise the features stated above by means of a linear system in which technological capabilities are made to depend on a matrix of interaction with evolving neighbours as well as on a vector of in-house generated knowledge. The model is then simulated to determine the emergent properties of neighbourhood formation and stability together with average capability
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Massimo Egidi & Massimo Ricottilli, 1997.
"Co-ordination and Specialization,"
CEEL Working Papers
9703, Computable and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
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