Multi-parameter models of innovation diffusion on complex networks

Abstract

A model, applicable to a range of innovation diffusion applications with a strong peer to peer component, is developed and studied, along with methods for its investigation and analysis. A particular application is to individual households deciding whether to install an energy efficiency measure in their home. The model represents these individuals as nodes on a network, each with a variable representing their current state of adoption of the innovation. The motivation to adopt is composed of three terms, representing personal preference, an average of each individual's network neighbours' states and a system average, which is a measure of the current social trend. The adoption state of a node changes if a weighted linear combination of these factors exceeds some threshold. Numerical simulations have been carried out, computing the average uptake after a sufficient number of time-steps over many realisations at a range of model parameter values, on various network topologies, including random (Erdos-Renyi), small world (Watts-Strogatz) and (Newman's) highly clustered, community-based networks. An analytical and probabilistic approach has been developed to account for the observed behaviour, which explains the results of the numerical calculations.

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• Statistical physics of social dynamics
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• Bond percolation on a class of clustered random networks
• Collective Decision Dynamics in the Presence of External Drivers
• The critical point of k-clique percolation in the Erdos-Renyi graph

Cited by in corpus (8)

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• Identifying the structure patterns to govern the performance of localization in regulating innovation diffusion
• Toward a new AI winter? How diffusion of technological innovation on networks leads to chaotic boom-bust cycles
• Non-equilibrium time-dependent solution to discrete choice with social interactions