Investment in innovation is a crucial determinant of economic development. However, the innovation policy community lacks a robust empirical framework to describe the long-term emergence of technologies. At present, technological progress is often measured quantitatively as the first derivative of incomplete performance metrics as a function of time or interpreted based on qualitative observations at the firm or industry level. There is a longstanding absence of approaches that deterministically trace innovations from their nascent phase – when future applications and potential competitive opportunities may still be unknown – to more applied research, development, and subsequent technological outcomes, such as commercialization. This gap implies the formulation of innovation policy may be blinded by insufficient insights or foresight.

The capability to measure historical emergence of technologies would refine the characterization of long-term innovation processes and anticipate how to intervene in these processes. This capability is particularly relevant to “mission-oriented” innovations with predefined technological goals but uncertain innovative intermediaries. This thesis addresses this measurement issue by employing methods from network science and statistics. It posits that various digital records, ranging from patents to clinical trials, and the signals among them, approximate innovation at differing maturities; connecting these records at scale would portray technological emergence more completely.

This thesis begins by synthesizing views about innovation “trajectories” from the systems engineering, scientometrics, evolutionary economics, operations research, and political economics literatures. Using these views as a baseline, this thesis derives proof-of-concept methods to trace technological evolution through complex networks from first principles, elucidating sequences of events that represent innovation bottlenecks of vaccines, small molecule drugs, nucleic acid sequencers, large language models, and quantum computers. After using these new methods, this thesis quantifies the position and proximity of documents and funders to long-term innovation events and dynamics behind these emerging technologies. In addition, the new methods are used as a statistical tool to revisit and represent the dynamics of enduring innovation models such as the linear, chain-linked, and S-curve models with empirical data.

The most interesting empirical findings arising from the proof-of-concept methods are in terms of the: (i) Order and prevalence of innovation maturities – this thesis observes an overall clear progression of basic research to applied research to development and deployment and that the translation of knowledge is mostly between basic and applied researches and between research and product developments (52% and 36% respectively in this thesis’ dataset); (ii) Division of innovation labor among funding agencies – most large public funding agencies are located 10-27 years from the commercialization of new vaccines while most mission-oriented agencies and pharmaceuticals fund research 2-19 years from commercialization. In addition, most large public funding agencies are similarly involved in early-stage translations, whereas pharmaceuticals and mission-oriented agencies are spread across more diverse innovation maturities, and each leaves different translation footprints. In addition, this thesis develops a (iii) New technique to identify innovations that are rate-limiters to technological emergence.

Given the complexity of innovation regularly surpasses the ability of prescriptive models to inform policy decisions holistically, the exploratory methods developed in this thesis present a new and more systematic way to observe and elucidate technological emergence. This work contributes to practice by providing a more objective understanding of innovation “trajectories”, allowing for more informed R&D interventions and policy evaluations.