Simulating Innovation

Simulating Innovation

Computer-based Tools for Rethinking Innovation

Christopher Watts and Nigel Gilbert

Christopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations.

Chapter 2: The variability and variety of diffusion models

Christopher Watts and Nigel Gilbert

Subjects: business and management, organisational innovation, economics and finance, economics of innovation, evolutionary economics, innovation and technology, economics of innovation, organisational innovation, social policy and sociology, sociology and sociological theory


Once someone has invented a new product or technology or thought up a new idea, how do others come to adopt it? When someone introduces a novel practice to a group, how does it spread within that group? This chapter covers some of the modelling tools that have been developed to help us think about the diffusion of innovation - the patterns observed as ever more people in some population adopt an innovation. Two such patterns can be seen in Figure 2.1, based on data from the classic study by Ryan and Gross (1943). Shown on the left are the numbers of those who have adopted hybrid seed corn each year, from a population of 259 farmers in the mid-west United States (minus two farmers who did not adopt during the study). The numbers go up in an S-shaped curve that can be divided into phases: a lag period, during which adoption has yet to take off; a phase of rapid (near-exponential) growth; and a phase of ever slowing growth, as the population saturates with adopters. The chart on the right shows the corresponding rate of adoption - the numbers of new adopters in each year - with a characteristic initial rapid growth, rise to a peak and fall back to zero.

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