## The AI Economist

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### Profile overview
- Sendhil Mullainathan: 52-year-old behavioral economist bridging math, computer science, economics, and human behavior.
- Career highlights:
  - Won a $500,000 grant from the MacArthur Foundation in 2002 at the age of 29; served on the MacArthur board for 12 years until last June.
  - Google Scholar profile lists more than a dozen works with several times 1,000 citations; his body of work has been cited almost 100,000 times.
  - Academic appointments at Harvard, the University of Chicago, and MIT.

### Early life and education
- Born in a village in the Indian state of Tamil Nadu, south of Chennai; family owned land.
- Father, Mark, entered a doctoral program in aeronautical engineering at Caltech, left when Sendhil was 3.
- Family moved to Los Angeles when Sendhil was 7 after Mark obtained visas.
- At age 10, family experienced job loss due to a Presidential order requiring security clearance for aerospace work under President Ronald Reagan; this shaped Mullainathan’s early interest in unemployment and job security.
- Attended the Clarkson School in Potsdam, New York, as a 16-year-old; later studied at Cornell, majoring in mathematics with additional majors in computer science and economics.
- Completed a doctorate in economics at Harvard in 1998 after initially entering the computer science doctoral program at MIT and deferring for a year.

### Research contributions in behavioral economics
- Research spans psychology of memory, decision-making by judges, and the economics of scarcity.
- Key findings and works:
  - 2001 research (with Marianne Bertrand) showed that “CEO pay responds significantly to luck,” such as moves in oil prices.
  - 2004 paper (Bertrand and Mullainathan), “Are Emily and Greg More Employable than Lakisha and Jamal?” found that fictitious resumes with White-sounding names got 50 percent more callbacks.
  - Collaborated with Eldar Shafir on the psychology and economics of scarcity, culminating in the 2013 book Scarcity: The New Science of Having Less and How It Defines Our Lives; findings: scarcity dramatically affects brain function, soaking up cognitive bandwidth and trapping people in a scarcity cycle.

### Focus on AI and machine learning
- Began applying machine learning to study human decision-making in and after 2012, seeking research “well off the beaten path.”
- 2017 paper (with four colleagues) applied an algorithm to a database of more than 700,000 people arrested between 2008 and 2013 in New York City to analyze risk of flight or reoffending.
  - Findings:
    - Judges routinely made wrong calls, often releasing defendants on bail that the algorithm flagged as high-risk.
    - Researchers attributed part of this to judges being “subject to the gambler’s fallacy.”
    - Estimated impacts of using a risk-assessing algorithm:
      - Could help reduce crime by 25 percent with no change in the number of people held in jail.
      - Or reduce jail populations by 42 percent with no increase in crime.
  - Result: Researchers built an AI tool that New York City judges use today to aid decision-making.
- 2024 paper (with Jens Ludwig) used AI to show that defendant mugshots can reliably predict judges’ jail-or-bail rulings using North Carolina data; found that people who appear well groomed or who have wider or rounder faces are more likely to be released on bail.
- Other AI applications cited:
  - An experiment where an algorithm detected minuscule differences in electrocardiograms that doctors missed, identifying candidates at higher risk of sudden cardiac arrest.

### Current initiatives and vision
- Returned to MIT in 2024 as professor in economics and electrical engineering and computer science; leading “The Bike Shop @ MIT.”
  - Concept: build “bicycles for the mind,” using algorithms as tools to augment human decision-making and scientific discovery.
  - Origin of metaphor: a March 1973 Scientific American graphic showing “Man on a bicycle” as most efficient mover; Mullainathan writes computers should be “bicycles for the mind.”
- Ongoing project with collaborators in India to apply AI to mathematics education:
  - Compiling thousands of examples of students’ work on mathematics homework.
  - Plan: use AI to identify where students go wrong and create an algorithm mapping the “cartography of confusion” to help teachers target instruction.
- Stated perspective:
  - “Economics is uniquely suited to this moment.”
  - “Algorithms are the new factory floor of science. They have the capacity to help us stitch the models together.”
  - Predicts algorithms “will help us transform philosophical questions into definitive science in the next 20 years.”

### Notable personal and professional traits
- Known for deep dives into varied topics (ice cream, espresso, nutrition, exercise).
- Described by colleagues as innovative, intellectually stimulating, and someone who repeatedly reevaluates his research direction.
- Emphasis on integrating the “mushy, qualitative weirdness of life” with formal algorithmic models to improve human decision-making on consequential matters.

*Source: People in Economics — The AI Economist (F&D, December 2025).*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2025/12/pie.pdf_