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Behavioral economist Sendhil Mullainathan has never forgotten the joy he experienced the first time he savored a delectable crisp yet gooey Levain cookie. He likens that experience to his encounters with fresh ideas.
“That hedonic gratification is very much akin to the pleasure I derive from hearing a new concept, discovering an innovative perspective on a situation, or contemplating something, getting stuck, and then achieving a breakthrough. You receive this kind of fundamental reward,” states Mullainathan, the Peter de Florez Professor with dual roles in the MIT departments of Economics and Electrical Engineering and Computer Science, as well as a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS).
Mullainathan’s passion for fresh concepts, and by extension, his inclination to transcend conventional interpretations of a situation or challenge by examining it from various viewpoints, appears to have begun quite early. As a student, he recalls how the multiple-choice options on exams seemed to present potential correctness.
“They would pose, ‘Here are three options. Which of these choices is the fourth?’ Well, I thought, ‘I don’t know.’ Each of them had justifiable explanations,” Mullainathan remarks. “While there’s a straightforward answer that most would instinctively choose, I simply perceived things quite differently.”
Mullainathan asserts that his cognitive process, and how it has always functioned, is “out of sync” — that is, not aligned with how the majority would typically select the single correct answer on a test. He compares his thought process to “one of those clips where an army is marching and one individual is out of step, prompting everyone to wonder, what’s wrong with this person?”
Fortunately, Mullainathan adds, “being out of sync is somewhat advantageous in research.”
And it appears to be so. Mullainathan has been awarded a MacArthur “Genius Grant,” recognized as a “Young Global Leader” by the World Economic Forum, listed as a “Top 100 thinker” by Foreign Policy magazine, featured in the “Smart List: 50 people who will change the world” by Wired magazine, and received the Infosys Prize, the largest monetary honor in India acknowledging excellence in science and research.
Another significant facet of Mullainathan’s identity as a researcher — his emphasis on financial scarcity — also traces back to his youth. When he was around 10, shortly after his family relocated to the Los Angeles area from India, his father lost his job as an aerospace engineer due to a shift in security clearance regulations affecting immigrants. When his mother informed him that, without employment, the family would have no funds, he was incredulous.
“Initially, I thought, that cannot be right. It didn’t quite register,” he explains. “So that was the first moment I realized there’s no safety net. Anything can happen. It was the first time I truly recognized economic instability.”
The family managed by running a video store and subsequently other small enterprises, leading Mullainathan to Cornell University, where he majored in computer science, economics, and mathematics. Despite his heavy math workload, he found himself drawn not to traditional economics, but to the behavioral economics of an early trailblazer in the field, Richard Thaler, who later received the Nobel Memorial Prize in Economic Sciences for his contributions. Behavioral economics integrates the psychological, and often irrational, elements of human behavior into the study of economic decision-making.
“The non-math aspect of this discipline is captivating,” Mullainathan observes. “What makes it compelling is that the mathematical models in economics aren’t functioning effectively. The math is elegant, the theorems sound. But it doesn’t work because humans are strange, complex, and fascinating.”
Given how nascent behavioral economics was at the time of his graduation, Mullainathan recounts Thaler advising him to delve into standard economics in graduate school and establish a reputation before pivoting to behavioral economics, “because it was so overlooked. It was deemed extremely risky since it didn’t even conform to a traditional field,” Mullainathan states.
Unable to resist examining human quirks and complexities, however, Mullainathan concentrated on behavioral economics, secured his PhD at Harvard University, and claims he then spent roughly a decade studying individuals.
“I aimed to acquire the intuition that a good academic psychologist possesses about people. I was dedicated to comprehending individuals,” he expresses.
As Mullainathan developed theories regarding why people make specific economic choices, he was eager to empirically test these theories.
In 2013, he released a paper in Science titled “Poverty Impedes Cognitive Function.” The study assessed sugarcane farmers’ performance on cognitive tests in the days leading up to their annual harvest, when they were financially depleted, sometimes nearly to the brink of starvation. In the controlled experiment, the same farmers took tests after their harvest was completed and they had received payment for a flourishing crop — and they scored markedly higher.
Mullainathan expresses satisfaction that the research had a widespread influence, and that policymakers often consider its underlying principles.
“Policies as a whole tend to be quite challenging to alter,” he notes, “but I believe it has fostered awareness at every stage of the design process, leading people to acknowledge that, for instance, if I create a program for those in economic vulnerability difficult to enroll in, that’s essentially a significant handicap.”
For Mullainathan, the most crucial impact of the research was on individuals, an effect he observed in reader comments following the research’s coverage in The Guardian.
“Ninety percent of the individuals who posted those comments shared sentiments like, ‘I experienced economic insecurity at one time. This perfectly embodies what it felt like to be impoverished.’”
Such revelations regarding how external influences shape personal circumstances could be among vital advancements made possible by algorithms, Mullainathan argues.
“I believe that in the previous era of science, research was conducted in expansive laboratories and translated into significant applications. I think the upcoming era of science will equally focus on enabling individuals to reconceptualize their identities and their life experiences.”
Last year, Mullainathan returned to MIT (having previously taught there from 1998 to 2004) to concentrate on artificial intelligence and machine learning.
“I desired to be in an environment where I could engage in both computer science and a premier behavioral economics department,” he states. “And truly, if you objectively evaluated ‘which institutions excel in both,’ MIT stands at the pinnacle of that list.”
While AI can streamline tasks and systems, the automation of abilities humans already possess is “difficult to generate excitement about,” he states. Computer science can be employed to enhance human abilities, a concept only constrained by our creativity in formulating inquiries.
“We should be contemplating, what capacity do you wish to expand? How could we construct an algorithm to assist you in enhancing that capacity? Computer science as a field has consistently excelled in addressing intricate problems and devising solutions,” he remarks. “If you have a capacity that you’d like to grow, that seems like a very challenging computing puzzle. Let’s explore how to tackle that.”
The fields that “are far from having reached the frontiers achieved by physics,” such as psychology and economics, could be on the brink of tremendous advancements, Mullainathan asserts. “I firmly believe that the next wave of breakthroughs will originate from the intersection of understanding people and the comprehension of algorithms.”
He illustrates a potential application of AI where a decision-maker, such as a judge or doctor, could access what their typical choices might be concerning a specific set of circumstances. Such an average would likely be less influenced by daily variables — such as a bad mood, digestive issues, slow traffic on the commute, or a disagreement with a partner.
Mullainathan encapsulates the concept as “average-you is preferable to you. Envision an algorithm that simplifies the process of understanding what you would typically do. And that doesn’t align with your current actions. You might have valid reasons for acting differently, but posing that question proves immensely beneficial.”
Looking ahead, Mullainathan will undoubtedly continue pursuing such innovative ideas — for to him, they present a truly delightful reward.
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