Sendhil Mullainathan: The Sweet Taste of a New Idea in Behavioral Economics and AI
Behavioral economist Sendhil Mullainathan, a professor at MIT, equates the thrill of discovering a new idea to the pleasure of tasting a perfect Levain cookie. This unique perspective drives his innovative approach to economics and artificial intelligence.
“That hedonic pleasure is pretty much the same pleasure I get hearing a new idea, discovering a new way of looking at a situation, or thinking about something, getting stuck and then having a breakthrough. You get this kind of core basic reward,” says Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS).
Mullainathan’s unconventional thinking began early. He recalls struggling with multiple-choice tests, finding multiple valid interpretations for each answer. This “out of phase” mindset, as he describes it, has become a cornerstone of his research process.
His research prowess has earned him accolades such as a MacArthur “Genius Grant,” recognition as a “Young Global Leader” by the World Economic Forum, and a spot on Foreign Policy’s “Top 100 thinkers” list, among others.
Mullainathan’s focus on financial scarcity stems from his childhood experiences. When his father lost his job, the family faced economic uncertainty, shaping Mullainathan’s understanding of economic precarity.
He was drawn to behavioral economics, particularly the work of Richard Thaler, who later won the Nobel Prize. Behavioral economics incorporates psychological aspects into economic decision-making.
“It’s the non-math part of this field that’s fascinating,” says Mullainathan. “What makes it intriguing is that the math in economics isn’t working. The math is elegant, the theorems. But it’s not working because people are weird and complicated and interesting.”
Mullainathan’s research has demonstrated the impact of poverty on cognitive function. His 2013 study in Science, “Poverty Impedes Cognitive Function,” showed that sugarcane farmers performed better on cognitive tests after being paid for their harvest compared to before when they were experiencing financial strain.
Mullainathan emphasizes the importance of these findings for policymakers, noting that understanding the cognitive tax of poverty can lead to more effective program design.
Returning to MIT to focus on AI and machine learning, Mullainathan is particularly interested in how these technologies can augment human capabilities. He envisions AI as a tool to expand human potential, rather than simply automating existing tasks.
“We should be asking, what capacity do you want expanded? How could we build an algorithm to help you expand that capacity? Computer science as a discipline has always been so fantastic at taking hard problems and building solutions,” he says.
He is exploring how AI could provide decision-makers, such as judges or doctors, with insights into their average decision-making patterns, potentially mitigating the impact of daily stresses and biases.
Mullainathan sums the idea up as “average-you is better than you. Imagine an algorithm that made it easy to see what you would normally do. And that’s not what you’re doing in the moment. You may have a good reason to be doing something different, but asking that question is immensely helpful.”
For Sendhil Mullainathan, the pursuit of new ideas remains a rewarding and intellectually stimulating endeavor.
