Computers do nothing but process logical statements. Mathematics consists of nothing but such statements. It would be reasonable to assume, then, that computers would be adept, perhaps uniquely, at reading, understanding, and cataloging the academic literature of mathematics. Not yet. People and machines, it turns out, speak different mathematical languages. If computers are to help manage mathematical knowledge, they need to be taught how to read math papers. The grant funds efforts by mathematician Thomas Hales to begin that instruction. Hales has raised an international army of graduate students and postdoctoral researchers, which he plans to unleash on the abstracts of thousands of mathematical papers. They will carefully translate the definitions and results that appear in these abstracts into formal programming language. These formalized abstracts—“fabstracts,” for short—can then be used to train machine learning algorithms to “read” textual mathematics.   

This grant supports the launch and operation of a new working group at the National Bureau of Economic Research (NBER) dedicated to studying the “economics of science.” Led by Paula Stephan, the group will bring together top flight economists to share existing work and findings, identify new areas for research, examine methodological and data issues, and commission new research. Topics include incentives in the current system, how the structure of grants and review systems affects scientific risk taking, the costs and efficiencies of different research funding models, how to judge scientific quality, and how to measure return on investment in basic and applied science. Along with four meetings of the working group, the grant will fund administrative and planning costs, support for small research grants, and partnerships between the working group and institutions like research universities or other science funders.

This grant supports the expansion of a popular seminar on computational social science, run by Matthew Salganik of Princeton University and Christopher Bail of Duke University. The instructional program, which takes place over the summer, involves lectures, group problem sets, and participant-led research projects. The seminar also includes outside speakers who conduct computational social science research in academia, industry, and government. Topics covered include text as data, website scraping, digital field experiments, nonprobability sampling, mass collaboration, and ethics. Interest in the program has been robust, with more than 10 times as many applicants as available slots each year. Sloan funds will allow lectures and course content to be broadcast via interactive video to six new satellite locations, including City University of New York; Northwestern; University of Colorado, Boulder; Seattle; Helsinki; and Cape Town. Additional satellite sites may be added in future years.

Harvard econometrician Jose Zubizarreta is developing new statistical methods for the extraction of causal inferences from large datasets. His methods flexibly adjust for covariates in observational studies while also yielding more stable causal estimates. For part of the research, Zubizarreta will investigate formal and theoretical properties of these methods. His team, however, based as it is at a medical school, will also work on specific applications. These require, for example, developing a new framework for the design and analysis of observational studies with discontinuities, or developing new methods that improve the degree of control (covariate balance) and statistical efficiency of randomized experiments that enhance their generalizability. Zubizarreta plans to produce five peer-reviewed papers on these topics. In addition, all software, code, and examples will be produced in an open source programming language and made freely available, together with documentation and sample data, to the academic community and the public.

This grant funds an initiative by the National Academy of Sciences (NAS) to join with peer institutions from around the world to launch international dialogue about policies governing artificial intelligence (AI) and automation. Partners include the National Academy of Engineering, the Canadian National Research Council, the Royal Society, the Royal Academy of Engineering, the Chinese Academy of Sciences, and the Chinese Academy of Engineering. Participants will include government officials, industry leaders, and academic researchers from many different countries in addition to the United States, U.K., China, and Canada. Topics to be addressed include national security, data use and privacy, and legal and intellectual property conundrums related to AI. Grant funds will partially support a workshop and associated webcast, a subsequent workshop report, and the creation and dissemination of supplementary resources for participants and the public.