The Importance of Small Teams in the Big Science Era

In a knowledge-based economy, science and technology are omnipresent and their importance is undisputed. Equally evident is the need to allocate resources, both monetary and human, in an effective way to foster innovation (Ahrweiler et al., 2015; Watts & Nigel, 2014). In the preceding decades, science policy has embraced data mining and metrics to gain insights into the structure and evolution of science and to devise metrics and indicators (Hicks et al., 2015), but it has not invested significant efforts into mathematical, statistical and computational models that can predict future developments in science, technology, and innovation (STI) despite their power. STI models make it possible to simulate the diffusion of ideas and experts, to estimate the impact of population explosion and aging, to explore alternative funding schemas, or to communicate the probable outcomes of different policy decisions. Advances in computational power combined with the unprecedented volume and variety of data on science and technology developments (e.g., publications, patents, funding, clinical trials, stock market, social media data), create ideal conditions for the advancement of computational modeling approaches that can be empirically validated and used to simulate and understand the structure and dynamics of STI and to augment human decision making. An NSF-funded conference “Modelling Science, Technology, and Innovation” was held at the National Academy of Sciences Building in Washington DC in May 2016. More than 100 participants from 67 institutions and seven countries attended the conference; 62 experts from academia, government, and industry presented their work. This paper provides a brief introduction into mathematical, statistical, and computational STI models and then highlights key challenges, insights, and opportunities associated with their usage in decision making as well as key insights that resulted from the conference. The full conference report together with all presentation slides and recordings is available at http://modsti.cns.iu.edu.