Sendhil Mullainathan
E496036
Sendhil Mullainathan is an economist and professor known for his work in behavioral economics, development economics, and the application of data science and machine learning to social and economic problems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sendhil Mullainathan canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T5129070 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sendhil Mullainathan Context triple: [Abhijit Vinayak Banerjee, coAuthor, Sendhil Mullainathan]
-
A.
Matthew Rabin
Matthew Rabin is an influential American economist known for his pioneering work in behavioral economics, particularly on how psychological factors affect decision-making and market outcomes.
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B.
Raj Chetty
Raj Chetty is an influential American economist known for his empirical research on inequality, social mobility, and public policy using large-scale data.
-
C.
Roland Fryer
Roland Fryer is an American economist known for his influential empirical research on racial inequality, education, and discrimination.
-
D.
Steven Levitt
Steven Levitt is an American economist and co-author of "Freakonomics," known for applying economic theory and data analysis to unconventional real-world topics.
-
E.
Susan Athey
Susan Athey is an influential American economist known for her pioneering work in applied microeconomics, market design, and the economics of technology and digital platforms.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sendhil Mullainathan Target entity description: Sendhil Mullainathan is an economist and professor known for his work in behavioral economics, development economics, and the application of data science and machine learning to social and economic problems.
-
A.
Matthew Rabin
Matthew Rabin is an influential American economist known for his pioneering work in behavioral economics, particularly on how psychological factors affect decision-making and market outcomes.
-
B.
Raj Chetty
Raj Chetty is an influential American economist known for his empirical research on inequality, social mobility, and public policy using large-scale data.
-
C.
Roland Fryer
Roland Fryer is an American economist known for his influential empirical research on racial inequality, education, and discrimination.
-
D.
Steven Levitt
Steven Levitt is an American economist and co-author of "Freakonomics," known for applying economic theory and data analysis to unconventional real-world topics.
-
E.
Susan Athey
Susan Athey is an influential American economist known for her pioneering work in applied microeconomics, market design, and the economics of technology and digital platforms.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
economist
ⓘ
person ⓘ university professor ⓘ |
| academicDegree | PhD in economics ⓘ |
| awardReceived |
Guggenheim Fellowship
ⓘ
MacArthur Fellowship NERFINISHED ⓘ |
| boardMemberOf | ideas42 NERFINISHED ⓘ |
| citizenship | United States of America ⓘ |
| coAuthor |
Andrei Shleifer
NERFINISHED
ⓘ
Eldar Shafir NERFINISHED ⓘ Jann Spiess NERFINISHED ⓘ Joshua D. Angrist NERFINISHED ⓘ Marianne Bertrand NERFINISHED ⓘ Markus Mobius NERFINISHED ⓘ Ziad Obermeyer NERFINISHED ⓘ |
| coAuthorOf | Scarcity: Why Having Too Little Means So Much NERFINISHED ⓘ |
| countryOfBirth | India NERFINISHED ⓘ |
| educatedAt | Harvard University ⓘ |
| employer |
Harvard University
ⓘ
Massachusetts Institute of Technology ⓘ Princeton University ⓘ University of Chicago Booth School of Business NERFINISHED ⓘ |
| fieldOfWork |
applied microeconomics
ⓘ
behavioral economics ⓘ data science ⓘ development economics ⓘ machine learning ⓘ public policy ⓘ |
| hasRole |
co-founder of Abdul Latif Jameel Poverty Action Lab (J-PAL)
ⓘ
co-founder of ideas42 ⓘ |
| influencedBy | behavioral economics tradition of Daniel Kahneman and Amos Tversky ⓘ |
| knownFor |
application of machine learning to social problems
ⓘ
research on behavioral biases in economic decision-making ⓘ research on poverty and scarcity ⓘ work on discrimination and algorithmic fairness ⓘ |
| languageSpoken | English ⓘ |
| memberOf | American Academy of Arts and Sciences ⓘ |
| notableWork | Scarcity: Why Having Too Little Means So Much NERFINISHED ⓘ |
| positionHeld |
professor of computation and behavioral science
ⓘ
professor of economics ⓘ |
| researchInterest |
algorithmic bias
ⓘ
consumer behavior ⓘ financial decision-making ⓘ health care ⓘ labor markets ⓘ poverty ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Sendhil Mullainathan Description of subject: Sendhil Mullainathan is an economist and professor known for his work in behavioral economics, development economics, and the application of data science and machine learning to social and economic problems.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.