Project Grant 2315663
- This National Science Foundation (NSF) Division of Social and Economic Science Project Grant award of $234,489 supports collaborative research at New York University (NYU) from September 1, 2023 to August 31, 2026. The primary objectives are to: Conduct a series of experiments to understand how people form subjective causal models based on observed data patterns, and how these models may be influenced by preconceptions triggered by natural contexts. This will provide insights into how economic...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $125,000 to support collaborative research on advancing the theoretical foundations of causal learning using modern machine learning tools. The key objectives are to: 1) develop methods for imputing unobserved counterfactual outcomes by integrating flexible machine learning models with statistical principles, 2) promote design-based approaches for quantifying...
- This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
- This $364,766 Project Grant awarded by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports fundamental research on the psychology of mass behavior and how large groups of people reason, form beliefs, and interact. The research project, conducted by the University of California, Santa Barbara, aims to extend behavioral economics methods to understand how individuals respond to and reason about the behavior of large groups. Key areas...
- This $616,000 Project Grant from the National Science Foundation (NSF) Division of Computing and Communication Foundations, under the CFDA program "Computer and Information Science and Engineering", supports research at the California Institute of Technology (Caltech) to address algorithmic and information-theoretic challenges in causal inference. The key objectives are to: Increase the range of applicability of causal inference methods by developing new algorithms and sample...
- This $272,750 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports fundamental research at the Massachusetts Institute of Technology (MIT) on how people make decisions and interact based on information. The key research focuses include: 1) understanding the long-term implications of people's limited or imperfect memory, which can lead to overconfidence and neglect of rare events; 2) examining how people decide...
- This $379,937 Project Grant from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports a computational research project at Yale University investigating how humans infer social relationships and group dynamics. The project aims to develop a theory and computational model of "structure learning" - the psychological processes that allow people to learn about social relationships by observing patterns of interaction between individuals....
- The University of California, San Diego will use a $404,640 Project Grant from the National Science Foundation to research how poverty falls under the agency's Social, Behavioral, and Economic Sciences program (CFDA 47.075). The two-year grant running from July 1, 2021 to June 30, 2023 will support the university's research into contributing to scientific strength and welfare through promoting basic research and education in social, behavioral and economic sciences, as well as monitoring and...
- The National Science Foundation (NSF) awarded a $1,182,881 Project Grant to the University of California, San Diego (UCSD) under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of new methods to ensure that machine learning models assigned to decisions such as lending and hiring can be changed through individual actions, protecting the right to access these services. The project will create techniques for (1) detecting...
- The University of California, San Diego will receive $400,000 from the National Science Foundation Directorate for Mathematical and Physical Sciences under the agency's Social, Behavioral, and Economic Sciences program (CFDA 47.075) from September 1, 2021 through August 31, 2024. The funding will support the project "Ethical and Responsible Research in the Design of Sociotechnical Systems," which aims to contribute to basic research and education in the social, behavioral and...
The National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program has awarded the University of California, San Diego (UCSD) a $319,541 project grant for "Collaborative Research: Causal Structures: Experiments and Machine Learning." This 3-year research project aims to understand how economic agents develop subjective causal models and narratives to interpret information and make decisions. The researchers will conduct a series of experiments to examine how people extract patterns from data and develop predictive models, both in abstract settings and with natural context. Additionally, the project will use machine learning to analyze news media and identify heterogeneous causal models propagated by different outlets. This research seeks to provide insights into how people form and use causal models, which is important for understanding susceptibility to manipulation and designing effective policies to correct beliefs and promote optimal behavior.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $319.5k | 8/17/23 |