Goals
Event causality, prediction and
simulation
•Observing a sequence of innovation events A, B and C, to predict a future event D
•What kind of event(s) could lead towards a future event E (‘What-If’ question)?
•Finding one or more causal reasons for event sequences to evolve in different ways
Cross data influence
•Observing trends and innovations in science to predict influence on job market
•Connecting policy events with increased or decreased trends in innovations
•Observing political sentiment in a society, observing influence on the field of study
Spotting (un)known phenomena and anomalies in data-streams
•Spotting changes on jobs & skills market due to the introduction of AI technologies
•Spotting a new statistically significant, previously unknown developments in ecosystem
About CauseFinder
(1) An innovation typically appears in the academic world; (2) projects are started around the innovation; (3) the innovation gets possibly patented; (4) companies are established around the innovation; (5) companies get investments, possibly in several rounds; (6) investments influence the job market; (7) market reacts to the quality and possible impact of the innovation; (8) public and expert perception gets formed; (9) media starts publishing about the innovation and companies; (10) educational institutions integrate innovation in their curricula, (11) policy makers regulate the innovation; and (12) to close the cycle, funding agencies start creating new funding opportunities to create space for follow-up innovations.
Team and collaborators
The project officially started in august 2023 and the team consists of experts accross different fields.
Giani GRĂDINARU
Professor
Vasile Alecsandru STRAT
Dean of MBA