Author: Michaela Jarvis | MIT Laboratory for Information and Decision Systems<\/p>\n
Behavioral economist Sendhil Mullainathan has never forgotten the pleasure he felt the first time he tasted a delicious crisp, yet gooey Levain cookie. He compares the experience to when he encounters new ideas.<\/p>\n
\u201cThat 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,\u201d 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).<\/p>\n
Mullainathan\u2019s love of new ideas, and by extension of going beyond the usual interpretation of a situation or problem by looking at it from many different angles, seems to have started very early. As a child in school, he says, the multiple-choice answers on tests all seemed to offer possibilities for being correct.<\/p>\n
\u201cThey would say, \u2018Here are three things. Which of these choices is the fourth?\u2019 Well, I was like, \u2018I don\u2019t know.\u2019 There are good explanations for all of them,\u201d Mullainathan says. \u201cWhile there\u2019s a simple explanation that most people would pick, natively, I just saw things quite differently.\u201d<\/p>\n
Mullainathan says the way his mind works, and has always worked, is \u201cout of phase\u201d \u2014 that is, not in sync with how most people would readily pick the one correct answer on a test. He compares the way he thinks to \u201cone of those videos where an army\u2019s marching and one guy\u2019s not in step, and everyone is thinking, what\u2019s wrong with this guy?\u201d<\/p>\n
Luckily, Mullainathan says, \u201cbeing out of phase is kind of helpful in research.\u201d<\/p>\n
And apparently so. Mullainathan has received a MacArthur \u201cGenius Grant,\u201d has been designated a \u201cYoung Global Leader\u201d by the World Economic Forum, was named a \u201cTop 100 thinker\u201d by Foreign Policy<\/em> magazine, was included in the \u201cSmart List: 50 people who will change the world\u201d by Wired<\/em> magazine, and won the Infosys Prize, the largest monetary award in India recognizing excellence in science and research.<\/p>\n Another key aspect of who Mullainathan is as a researcher \u2014 his focus on financial scarcity \u2014 also dates back to his childhood. When he was about 10, just a few years after his family moved to the Los Angeles area from India, his father lost his job as an aerospace engineer because of a change in security clearance laws regarding immigrants. When his mother told him that without work, the family would have no money, he says he was incredulous.<\/p>\n \u201cAt first I thought, that can\u2019t be right. It didn\u2019t quite process,\u201d he says. \u201cSo that was the first time I thought, there\u2019s no floor. Anything can happen. It was the first time I really appreciated economic precarity.\u201d<\/p>\n His family got by running a video store and then other small businesses, and Mullainathan made it to Cornell University, where he studied computer science, economics, and mathematics. Although he was doing a lot of math, he found himself drawn not to standard economics, but to the behavioral economics of an early pioneer in the field, Richard Thaler, who later won the Nobel Memorial Prize in Economic Sciences for his work. Behavioral economics brings the psychological, and often irrational, aspects of human behavior into the study of economic decision-making.<\/p>\n \u201cIt\u2019s the non-math part of this field that\u2019s fascinating,\u201d says Mullainathan. \u201cWhat makes it intriguing is that the math in economics isn\u2019t working. The math is elegant, the theorems. But it\u2019s not working because people are weird and complicated and interesting.\u201d<\/p>\n Behavioral economics was so new as Mullainathan was graduating that he says Thaler advised him to study standard economics in graduate school and make a name for himself before concentrating on behavioral economics, \u201cbecause it was so marginalized. It was considered super risky because it didn\u2019t even fit a field,\u201d Mullainathan says.<\/p>\n Unable to resist thinking about humanity\u2019s quirks and complications, however, Mullainathan focused on behavioral economics, got his PhD at Harvard University, and says he then spent about 10 years studying people.<\/p>\n \u201cI wanted to get the intuition that a good academic psychologist has about people. I was committed to understanding people,\u201d he says.<\/p>\n As Mullainathan was formulating theories about why people make certain economic choices, he wanted to test these theories empirically.<\/p>\n In 2013, he published a paper in Science<\/em> titled \u201cPoverty Impedes Cognitive Function.\u201d The research measured sugarcane farmers\u2019 performance on intelligence tests in the days before their yearly harvest, when they were out of money, sometimes nearly to the point of starvation. In the controlled study, the same farmers took tests after their harvest was in and they had been paid for a successful crop \u2014 and they scored significantly higher.<\/p>\n Mullainathan says he is gratified that the research had far-reaching impact, and that those who make policy often take its premise into account.<\/p>\n \u201cPolicies as a whole are kind of hard to change,\u201d he says, \u201cbut I do think it has created sensitivity at every level of the design process, that people realize that, for example, if I make a program for people living in economic precarity hard to sign up for, that\u2019s really going to be a massive tax.\u201d<\/p>\n To Mullainathan, the most important effect of the research was on individuals, an impact he saw in reader comments that appeared after the research was covered in The Guardian.<\/em><\/p>\n \u201cNinety percent of the people who wrote those comments said things like, \u2018I was economically insecure at one point. This perfectly reflects what it felt like to be poor.\u2019\u201d<\/p>\n Such insights into the way outside influences affect personal lives could be among important advances made possible by algorithms, Mullainathan says.<\/p>\n \u201cI think in the past era of science, science was done in big labs, and it was actioned into big things. I think the next age of science will be just as much about allowing individuals to rethink who they are and what their lives are like.\u201d<\/p>\n Last year, Mullainathan came back to MIT (after having previously taught at MIT from 1998 to 2004) to focus on artificial intelligence and machine learning.<\/p>\n \u201cI wanted to be in a place where I could have one foot in computer science and one foot in a top-notch behavioral economic department,\u201d he says. \u201cAnd really, if you just objectively said \u2018what are the places that are A-plus in both,\u2019 MIT is at the top of that list.\u201d<\/p>\n While AI can automate tasks and systems, such automation of abilities humans already possess is \u201chard to get excited about,\u201d he says. Computer science can be used to expand human abilities, a notion only limited by our creativity in asking questions.<\/p>\n \u201cWe 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,\u201d he says. \u201cIf you have a capacity that you\u2019d like to expand, that seems like a very hard computing challenge. Let\u2019s figure out how to take that on.\u201d<\/p>\n The sciences that \u201care very far from having hit the frontier that physics has hit,\u201d like psychology and economics, could be on the verge of huge developments, Mullainathan says. \u201cI fundamentally believe that the next generation of breakthroughs is going to come from the intersection of understanding of people and understanding of algorithms.\u201d<\/p>\n He explains a possible use of AI in which a decision-maker, for example a judge or doctor, could have access to what their average decision would be related to a particular set of circumstances. Such an average would be potentially freer of day-to-day influences \u2014 such as a bad mood, indigestion, slow traffic on the way to work, or a fight with a spouse.<\/p>\n Mullainathan sums the idea up as \u201caverage-you is better than you. Imagine an algorithm that made it easy to see what you would normally do. And that\u2019s not what you\u2019re doing in the moment. You may have a good reason to be doing something different, but asking that question is immensely helpful.\u201d<\/p>\n Going forward, Mullainathan will absolutely be trying to work toward such new ideas \u2014 because to him, they offer such a delicious reward.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"