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National Science Foundation

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Doctoral Dissertation Research in Economics: Behavioral Responses to Taxation of Foreign Personal Income

Doctoral Dissertation Research in Economics: Benefit Disclosure in Financial Choices Online and Field Experiments

Innovations in consumer finance offering consumers choices in financial products have not increased consumers' ability to take advantage of these choices. Surveys show low financial literacy rates among US adults, resulting in financial mistakes that hurt consumers' financial well-being. Financial education to improve financial literacy have failed because of the false assumption that consumers are aware of the benefits of learning to make financial choices.

Secure and Trustworthy Cyberspace Large Projects

The SaTC program welcomes proposals that address cybersecurity and privacy, and draw on expertise in one or more of these areas: computing, communication and information sciences; engineering; education; mathematics; statistics; and social, behavioral, and economic sciences. Proposals that advance the field of cybersecurity and privacy within a single discipline or interdisciplinary efforts that span multiple disciplines are both welcome.

Interests: 

Studies on the Data-Driven Economy and School Choice

Doctoral Dissertation Research: An Historical Study of Medical, Scientific, and Cultural Perspectives on Vision

This doctoral dissertation research project is a study of vision that traces the development of ophthalmology in early modern Europe. The research will use archival sources and historical analysis to investigate the ways in which the eye was studied, eye diseases were treated, and the knowledge of the eye was transmitted during the sixteenth and seventeenth centuries in Europe. Knowledge of the eye not only formed a critical branch of medical and technological investigation, it was also of cultural and scientific significance.

A Science of Science Policy Approach to Analyzing and Innovating the Biomedical Research Enterprise (SCISIPBIO)

The National Science Foundation (NSF) and the National Institutes of Health (NIH) are interested in proposals that will propel our understanding of the biomedical research enterprise by drawing from the scientific expertise of the science of science policy research community.

Deadline: 

Friday, September 9, 2022
Thursday, February 9, 2023
Monday, September 11, 2023

Interests: 

Future of Work at the Human-Technology Frontier: Core Research

The Future of Work at the Human-Technology Frontier (FW-HTF), one of the Big Ideas, is one mechanism by which NSF is responding to the challenges and opportunities for the future of jobs and work.

Deadline: 

Wednesday, March 2, 2022

Interests: 

Dynamic Pricing and Matching with Asymmetric Information

This award funds research on two projects in economic theory. The first examines how a monopolist will choose prices over time, with a focus on how monopoly power in the product market affects how the firm makes decisions over time about production technologies. The second project is in the general area of mechanism design, the design of methods to allocate recourses. The specific application is in matching markets under incomplete information, and the work could give us new ideas about how to design allocation methods that will lead to stable outcomes.

Doctoral Dissertation Research: The direct and indirect effect of innovation subsidies

Government subsidies to support innovation in firms are a widespread policy. However, little is known about their effectiveness to promote technological upgrading and boost firm performance in developing countries. The existing rigorous studies about this type of intervention are focused on developed countries and high-tech industries, and infer technological improvement from patents and R&D expenditure.

Doctoral Dissertation Research: Recovering the Polyvalent Genealogies of Machine Learning, 1948 - 2017

Machine learning techniques currently make "high-stakes" judgments in areas as diverse as criminal justice, credit risk, social welfare, hiring, and congressional redistricting. Such techniques make these decisions using patterns learned from historical social data. Emphasis on prediction rather than the circumstances of dataset creation have led to machine learning systems that preferentially target vulnerable populations for disparately adverse social judgments while making it more difficult for those subject to these decisions to protest unfair treatment.

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