Using associative networks to represent adopters' beliefs in a multiagent model of innovation diffusion

A lot of agent-based models were built to study diffusion of innovations. In most of these models, beliefs of individuals about the innovation were not represented at all, or in a highly simplified way. In this paper, we argue that representing beliefs could help to tackle problematics identified for diffusion of innovations, like misunderstanding of information, which can lead to diffusion failure, or diffusion of linked inventions. We propose a formalization of beliefs and messages as associative networks. This representation allows one to study the social representations of innovations and to validate diffusion models against real data. It could also make models usable to analyze diffusion prior to the product launch. Our approach is illustrated by a simulation of iPod (TM) diffusion.