Science forecasts: Modeling and communicating developments in science, technology, and innovation

Abstract

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 [6.1, 6.2]. 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 [6.3], but it has not invested significant efforts into mathematical, statistical, and computational models that can predict future developments in science, technology, and innovation (science, technology, and innovation (STI)) in support of data-driven decision making. Recent advances in computational power combined with the unprecedented volume and variety of data concerning science and technology developments (e. g., publications, patents, funding, clinical trials, and stock market and social media data) yielded ideal conditions for the advancement of computational modeling approaches that can be not only empirically validated, but used to simulate and understand the structure and dynamics of STI in support of improved human decision making. In this chapter, we review and demonstrate the power of computational models for simulating and predicting possible STI developments and futures. In addition, we discuss novel means to visualize and broadcast STI forecasts to make them more accessible to general audiences.