Behavioral economist Sendhil Mullainathan never forgot about the pleasure when he first tried a delicious, crunchy, but sticky Levaina cookie. He compares experience with new ideas.
“This hedonic pleasure is almost the same pleasure I hear a new idea, discovering a new way of looking at the situation or thinking about something, getting stuck, and then a breakthrough. You get a basic reward,” says Mullainathan, Peter de Florez professor with double nominations in the myth department of economics and electrical cut (lid).
Mullainathan's love for new ideas, and thus offenses beyond the usual interpretation of the situation or problem by looking at her from many different points, it seems that it began very early. He says that as a child at school, multiple -choice answers on tests seemed to offer the possibilities of correctness.
“They would say:” Here are three things. Which of these choices is the fourth? “Well, I thought,” I don't know. ” There are good explanations for everyone – says Mullainathan. “Although there is a simple explanation that most people would choose, natively, I just saw things completely different.”
Mullainathan says that his mind works and always acted, is “outside the phase” – that is, not synchronized with how most people would easily choose one correct answer on the test. He compares the way he thinks to “one of those films in which the army marching and one guy is not in a crotch, and everyone thinks: what is wrong with this guy?”
Fortunately, Mullainathan says: “Untak is helpful in the study.”
And apparently yes. Mullainathan received MacArthur “Genius Grant”, he was appointed by the World Economic Forum of the “Young World Leader”, he was named by the “best 100 thinker” by Foreign policy The magazine was included in the “Smart List: 50 people who change the world” Wired Warehouse I won the Infosys Award, the largest cash prize in India, recognizing perfection in science and research.
Another key aspect of who Mullainathan is a researcher – his focus on financial shortage – also dates back to childhood. When he was about 10 years old, just a few years after his family moved to the Los Angeles region from India, his father lost his job as an aviation engineer due to a change in provisions regarding immigrants. When his mother told him that his family would not have money without work, he was saying that he was incredulous.
“At the beginning I thought it couldn't be appropriate. It would not fully process,” he says. “It was the first time I thought there is no floor. Something could happen. It was the first time I really appreciated the economic uncertainty.”
His family got a video store, and then other small companies, and Mullainathan reached Cornell University, where he studied computer science, economics and mathematics. Although he did a lot of mathematics, he did not find himself with standard economics, but the early pioneer's behavioral economy in this field, Richard Thaler, who later won the Nobel Prize in the field of economic sciences for his work. Behavioral economy introduces psychological and often irrational aspects of human behavior in the study of making economic decisions.
“It's a fascinating part of this field,” says Mullainathan. “It is intriguing that mathematics in economics does not work. Mathematics is elegant, claims. But this does not work because people are strange, complicated and interesting.”
Behavioral economy was as new as Mullainathan graduated from his studies that Thaler advised him to study standard economy at the graduate school and made a brand before he focused on behavioral economy, “because it was so marginalized. It was considered to be super risky, because it didn't even fit into the field,” says Mullainathan.
However, unable to resist thinking about the weirdness and complications of humanity, Mullainathan focused on behavioral economy, obtained a doctorate at Harvard University and says that he spent about 10 years studying people.
“I wanted to get the intuition that a good academic psychologist about people has. I was involved in understanding people,” he says.
Because Mullainathan formulated theories about why people make certain economic elections, he wanted to empirically test these theories.
In 2013, he published an article in Science The entitled “Poverty hinders the cognitive function”. The study measured the results of farmers from sugar cane in intelligence tests on days before their annual collections, when there was no money, sometimes almost until hunger. In the controlled study, the same farmers conducted tests after their collection and were paid for successful cultivation – and achieved much higher results.
Mullainathan says he is happy that the research had a long -term influence and that those who often include their premise.
“Politics as a whole are difficult to change,” he says, “but I think it has created sensitivity at every level of the design process, that people are aware that, for example, if I prepare a program for people living in economic uncertainty, it is difficult to register, it will really be a huge tax.”
Mullainathan was the most important impact of research on the units, the influence he saw in the comments of readers that appeared after discussing the research The Guardian.
“Ninety percent of people who wrote these comments said:” At some point I was economically uncertain. It perfectly reflects how it is to be poor. “
Mullainathan says that such information on external influences affecting personal life may belong to personal life, possible thanks to algorithms.
“I think that in the past era of science was conducted in Big Labs and was included in great things. I think that in the next era of science will be the same on allowing units to think about who they are and what their lives are.”
Last year, Mullainathan returned to the myth (after prior teaching in MIT in 1998–2004) to focus on artificial intelligence and machine learning.
“I wanted to be in a place where I could have one foot in computer science and one foot in the highest level of behavioral economic department,” he says. “And really, if you just objectively said:” What are the places that are in both, both “, the myth is at the top of this list.”
While artificial intelligence can automate tasks and systems, such automation of abilities that people already have, is “difficult to excite,” he says. Computer science can be used to expand human skills, which is limited only to our creativity in asking questions.
“We should ask what capacity you want to develop? How could we build an algorithm to help you expand this ability? Information technology, because discipline has always been so fantastic in taking difficult problems and building solutions,” he says. “If you have the capacity you want to expand, it seems a very difficult calculation challenge. Let's think about how to take it.”
Mullainathan says that “very far from hitting the border”, like psychology and economics, can be on the edge of a lot of achievement, says Mullainathan. “Basically, I think that the next generation of breakthroughs will result from the intersection of understanding people and understanding of algorithms.”
It explains the possible application of artificial intelligence, in which a decision -maker, for example a judge or doctor, may have access to what their average decision would be associated with a specific set of circumstances. Such an average would be potentially free than everyday influences-as much as a bad mood, indigestion, slow movement on the way to work or fighting the spouse.
Mullainathan summarizes this idea as “average-you are better than you. Imagine an algorithm that made it easier to see what you would normally do. And this is not what you are doing at the moment. You can have a good reason to do something else, but asking this question is extremely helpful.”
Moving forward, Mullainathan will absolutely try to work on such new ideas – because they offer such a delicious reward for him.
