Network Structure and the Diffusion of Knowledge

By Robin Cowan, Nicolas Jonard. 2004

991 citations recorded by the source corpus.

This paper models knowledge diffusion as a barter process in which agents exchange different types of knowledge. This is intended to capture the observed practice of informal knowledge trading. Agents are located on a network and are directly connected with a small number of other agents. Agents repeatedly meet those with whom direct connections exist and trade if mutually profitable trades exist. In this way knowledge diffuses throughout the economy. We examine the relationship between network architecture and diffusion performance. We consider the space of structures that fall between, at one extreme, a network in which every agent is connected to n nearest neighbours, and at the other extreme a network with each agent being connected to, on average, n randomly chosen agents. We find that the performance of the system exhibits clear ‘small world’ properties, in that the steady-state level of average knowledge is maximal when the structure is a small world (that is, when most connections are local, but roughly 10 percent of them are long distance). The variance of knowledge levels among agents is maximal in the small world region, whereas the coefficient of variation is minimal. We explain these results as reflecting the dynamics of knowledge transmission as affected by the architecture of connections among agents.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/09310dc653e890d76a9fc1ecc428093cf63bb9fb

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “Diusion models have been studied for complex networks [Alkemade and Castaldi 2005; Cowan and Jonard 2004 ] and specically in the context of inuence maximization [Kempe et al. 2003; 2005] where the task is to identify key start nodes that can be used to eectiv ely propagate information through the n”
Proximity, networking and knowledge production in Europe: What lessons for innovation policy? (2013, background): “3 The only contributions which analyze different continents at the regional level are Crescenzi et al. (2007) for US and EU, with data coming from USPTO and EPO respectively, and Usai (2011) on OECD regions with homogenous information on Patent Cooperation Treaty applications.”
Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics (2009, background): “Explicit evolutionary models of open innovation are still to be developed, although some progress is being made in related topics such as open source models [95] and social network models [96].”
Knowledge flows and the absorptive capacity of regions (2015, background): “Networks are also critical means to diffuse knowledge and promote the cross-pollination of ideas (Katz and Martin, 1997), and again empirical evidence in support of networks as vehicles for the dissemination of ideas is large (Breschi and Lissoni, 2009; Cowan and Jonard, 2004; Gomes-Casseres et al.,”
From simplistic to complex systems in economics (2005, background): “Thus, as a product moves up a logistic diffusion curve we can predict that there will be a change in network structure associated with its production and sale and, correspondingly, we can measure this (see Cowan and Jonard (2004)).”
The Complementary Effects of Proximity Dimensions on Knowledge Spillovers (2014, background): “crucial distance of 600 km is found by Dettori et al. (2012). 16.”