“MIT hasn’t just prepared me for the longer term of labor — it’s pushed me to review it. As AI systems develop into more capable, more of our online activity might be carried out by artificial agents. That raises big questions: How should we design these systems to grasp our preferences? What happens when AI begins making a lot of our decisions?”
These are a number of the questions MIT Sloan School of Management PhD candidate Benjamin Manning is researching. A part of his work investigates tips on how to design and evaluate artificial intelligence agents that act on behalf of individuals, and the way their behavior shapes markets and institutions.
Previously, he received a master’s degree in public policy from the Harvard Kennedy School and a bachelor’s in mathematics from Washington University in St. Louis. After working as a research assistant, Manning knew he desired to pursue an instructional profession.
“There’s no higher place on the earth to review economics and computer science than MIT,” he says. “Nobel and Turing award winners are in every single place, and the IT group lets me explore each fields freely. It was my top alternative — once I was accepted, the choice was clear.”
After receiving his PhD, Manning hopes to secure a college position at a business school and do the identical sort of work that MIT Sloan professors — his mentors — do on daily basis.
“Even in my fourth yr, it still feels surreal to be an MIT student. I don’t think that feeling will ever fade. My mom definitely won’t ever recover from telling people about it.”
Of his MIT Sloan experience, Manning says he didn’t realize it was possible to learn a lot so quickly. “It’s no exaggeration to say I learned more in my first yr as a PhD candidate than in all 4 years of undergrad. While the pace might be intense, wrestling with so many recent ideas has been incredibly rewarding. It’s given me the tools to do novel research in economics and AI — something I never imagined I’d be able to.”
As an economist studying AI simulations of humans, for Manning, the longer term of labor not only means understanding how AI acts on our behalf, but in addition radically improving and accelerating social scientific discovery.
“One other a part of my research agenda explores how well AI systems can simulate human responses. I envision a future where researchers test tens of millions of behavioral simulations in minutes, rapidly prototyping experimental designs, and identifying promising research directions before investing in costly human studies. This isn’t about replacing human insight, but amplifying it: Scientists can concentrate on asking higher questions, developing theory, and interpreting results while AI handles the computational heavy lifting.”
He’s excited by the prospect: “We’re possibly moving toward a world where the pace of understanding may get much closer to the speed of economic change.”
