Recombinant knowledge and innovation

Abstract

It has long been acknowledged that knowledge is key for economic growth and innovation. Over the last decades, the perception of what knowledge is and how it can be characterized, however, has changed drastically. Today, in particular the neo-Schumpeterian perspective highlights that knowledge cannot longer be considered as a pure public good but stresses it as something individual, local, tacit, firm specific and complex (Hanusch and Pyka, 2010). So for example, we know from literature that the process of knowledge discovery and creation (and thus the process of innovation) is a recombinatorial one (see e.g. Fleming, 2001). In fact, already Schumpeter (1942) addressed innovations as new combinations and also Weitzman (1998, p. 209) states: "when research is applied, new ideas arise out of existing ideas in some kind of cumulative interactive process that intuitively has a different feel from prospecting for petroleum". Against this backdrop, it is striking that that knowledge in agent-based simulation models is still often oversimplified. Consequently, the discovery or generation of new knowledge in many cases is modelled as a random process similar to the discovery of new oil fields (Antonelli, Krafft and Quatraro, 2010). At the same time, some models feature more sophisticated concepts and ideas, each with a particular focus and objective. Approaches from the literature range from simple binary representations that consider knowledge as an (or more) isolated piece(s) of information. In this setting, knowledge diffusion, for example, boils down to the process similar to the contagious transmission of a disease. Other contributions consider knowledge to be linked knowledge pieces that form a network of knowledge (see for example Morone and Taylor, 2010; Schlaile, Zeman and Mueller, 2018; Müller, Kudic and Vermeulen, 2020) which allows for the analysis of more nuanced analyses of knowledge discovery, creation and diffusion. The overarching goal of this chapter is to show how knowledge and hence knowledge creation and knowledge diffusion processes are modelled in agent-based models in the literature. This is done to analyse how we can capture the complex nature of knowledge especially against the backdrop of radical transition processes such as the bioeconomy and the energy transition.