This NSF-funded, agenda-setting conference will review opportunities and challenges associated with the usage of mathematical, statistical, and computational models in science, technology, and innovation (STI) in decision making. Among others, STI models can be employed 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.
Global operation rooms that provide visualizations of current data and predictions of possible futures are commonplace in meteorology, finance, epidemiology, or defense. Computer games such as SimCity let general audiences design and experience alternative futures. However, much STI decision making does not yet benefit from the power of big data or from computational models.
Advances in computational power combined with the availability of relevant data (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 future developments and to pick desirable futures.
This conference brings together leading experts from economics, social science, scientometrics and bibliometrics, information science, physics, and science policy that develop mathematical, statistical, and computational models of different types (stochastic, agent-based, epidemics, game-theoretic, network. etc.). It also features talks and panel discussions by representatives from government agencies, university administrators, and other science policy makers on current and future STI model needs. Collectively, participants will identify grand challenges for future STI modelling research and development.
The conference is supported in part by the National Science Foundation’s Science of Science and Innovation Policy program under Grant No. SBE-1546824, Nete Federal IT, Thomson Reuters, Indiana University Network Science Institute, and the Cyberinfrastructure for Network Science Center at Indiana University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.