Multidisciplinarity and Interdisciplinarity are considered key factors for success in science. However, empirical evidence is hard to find, also because many measures to assess success and impact are based on monodisciplinary tradition. We at the Arc2s group believe very much in interdisciplinary novelty, and so we are particularly proud to host Prof. Magda Fontana and Dr. Martina Iori for their talk on May 7th at “sala seminari”, first floor, department of Computer Science.
post event edit: slides of the event are available on request. Just send an email to magda[dot]fontana[at]unito[dot]it
The importance of novelty in fostering economic growth and technological development (Romer, 1991; EU Lisbon Strategy, 2000; Nelson et al., 2014; Aharonson and Schilling, 2016) makes its measurement a crucial issue. The main approach developed by evolutionary (March, 1991; Nelson and Winter, 1982; Schumpeter, 1939) and complexity economics (Arthur and Polak, 2006) and by the economics of science (Wang et al., 2017) considers novelty as emerging out of recombination of existing knowledge. The approach is very useful to identify the requisites of the innovative step that combines and transforms the existing knowledge in novel ideas and artifacts and to describe the continuum of novelties that stem from the knowledge pool of societies. As nature in its operations does not make jumps, so human artifacts do not appear out of thin air, but are concocted from what is already known. However, translating the idea into a methodology that empirically detects novelty in research and that characterizes novelty determinants is another question entirely. The conundrum here is the interpretation of the term ‘recombination’. The paper introduces a new measure of novelty as recombination of concepts in texts and compares it with the existing novelty indicators that rely on recombination of knowledge inputs. Results show that measures based on distant and atypical combinations of inputs oversee mono-disciplinary novelty.
Magda Fontana is assistant professor of public finance at the Department of Economics and Statistics of the University of Turin and Director of the Master in Data Science for Complex Economic Systems at the Collegio Carlo Alberto. She works in complexity economics, science of science, and agent-based modeling.
Martina Iori is postdoctoral fellow at the Department of Economics and Statistics of the University of Turin. She received a PhD in Economics at the University of Turin and Collegio Carlo Alberto. She has been visiting PhD student at the Department of Network and Data Science of the Central European University. She holds a Master’s Degree in Physics at the University of Turin. Her research interests are in the areas of economics of innovation and knowledge, science of science, and energy economics.
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