Formal Models of Learning, Innovation, and Diffusion

Abstract This chapter presents a concise guide to modelling the dynamics of learning, innovation, and diffusion. A first ensemble of models, with their roots in the seminal contributions of Herbert Simon, attempts to formally grasp the procedural aspects of knowledge together with the combinatorial nature of organizational activities and learning. Many models of organizational ‘cognition’, memory, adaptation, and learning refine on the formalism of so-called ‘NK models’, while others build on ‘classifier systems’. A second, more blackboxed approach to technological knowledge represents learning in the space of input–output relations, generally as stochastic dynamics, made possible by expensive search efforts and with uncertain probabilities of success, depending also on the nature of innovative opportunities, discussed in Chapter 3 and organizational capabilities, as in Chapter 4. Finally, third, we address models of innovation along paths often characterized by various forms of increasing returns, network externalities, social adaptation, co-evolution between the features of supplied goods and demand, and path-dependencies.