Martina Iori is an assistant professor in economics at Sant’Anna School of Advanced Studies in Pisa, Italy. She holds a PhD in economics — curriculum economics and complexity — from the University of Turin and Collegio Carlo Alberto. Before that, she received a master’s degree in theoretical physics from the University of Turin and a master of arts in economics and complexity from Collegio Carlo Alberto. During her PhD, she was visiting scholar at the Department of Network and Data Science of the Central European University, where she worked with Professor Roberta Sinatra.
Martina’s research exploits advanced statistical and computational tools to improve our understanding of the processes that lead to innovations and scientific discoveries. It involves the analysis of large datasets of patents and scientific articles and is characterized by an intrinsically interdisciplinary approach. Specifically, Martina exploits network science and text-mining techniques to analyse large citation networks and develop new indicators to capture innovation and science dynamics.
Her research on the role of interdisciplinary in science and its interplay with novelty appeared in Research Policy, and her project on the development of new ‘novelty’ indicators in science and technology was presented at the NBER Innovation Information Initiative technical meeting, whose aim is to gather experts in the economics of innovation field to develop and discuss new patent indicators, and was awarded the seal of excellence by the European Commission.
Applied network science and big data techniques have also been used by Martina and co-authors to track technological trajectories and detect the long-term effects of public funding in the development of Artificial Intelligence. This paper, by defining an indicator of patent influence on all possible sequences of follow-on innovations, shows that government-backed patents had a profound effect on the development of AI, especially in the early phases of the technology.
More recently, Martina’s work on the role of knowledge transmission in history and the positive impact caused by the circulation of Hindu-Arabic numerals on economic growth in pre-modern Europe received the DRIUD (Danish Research Unit for Industrial Dynamics) – one of the most prestigious venues for studies on innovation and industrial dynamics – best paper award.
- Fleming, L. (2001), “Recombinant uncertainty in technological search,” Management Science, 47 (1).
- Wang, J., R. Veugelers, and P. Stephan (2017), “Bias against novelty in science: A cautionary tale for users of bibliometric indicators,” Research Policy, 46 (8).
- Fontana, M., M. Iori, F. Montobbio, and R. Sinatra (2020), “New and atypical combinations: An assessment of novelty and interdisciplinarity,” Research Policy, 49 (7).
- Kelly, B., D. Papanikolaou, A. Seru, and M. Taddy (2021), “Measuring technological innovation over the long run,” American Economic Review: Insights, 3 (3).