Over the last few decades a series of studies shed light on the mechanisms of knowledge diffusion and network characteristics affecting its efficiency. However, economic agents typically exchange knowledge not for the sake of merely accumulating all knowledge pieces but in search for the knowledge complementary to their knowledge stock to recombine those in new ways and create new value added. This process of knowledge recombination has been widely acknowledged since Schumpeter (1912) and intensely discussed both from the theoretical (e.g., Weitzman 1998) and empirical (Youn et al 2015, Savino et al 2017) perspectives. Alas, little is known on characteristics of R&D industrial networks beneficial for this process. The present model is designed to bridge this gap. Given the similarity with the network of words in their hierarchical structure, knowledge in our model is represented by words, which can be recombined with each other to produce longer words and which value is proxied by their frequency of use. We proceed by modeling words’ recombination in a population of agents situated on a network. Originally agents are equipped by a subset of letters and “noisy idea” on few words they may construct. Interacting over direct ties agents can jointly produce new words and share those ideas. Based on the words discovered, agents can generate further ideas on the words to be constructed thereof. As long as the ideas are diffused locally, we find that scale-free networks to perform best in recombining new knowledge. This is due to the accumulated stock of knowledge of star agents enabling them to receive and distribute ideas belonging to different parts of the knowledge network. If, in contrast, the ideas are immobile, the scale free networks lose their performance superiority. At the individual level, central network locations, logically, pay off more in the former than the latter case.
About the speaker
Ivan Savin is a postdoctoral research fellow within the KIT-BETA project devoted to general purpose technologies, creativity and sustainability. Before that, he was a postdoc for four years at the Friedrich Schiller University. He received his PhD from the Department of Economics of the Justus Liebig University of Giessen. His research interests include networks, economics of innovation and optimization methods.
Venue: Conference room (room 0.16 & 0.17)
Date: 23 March 2017
Time: 12:00 - 13:00