At the intersection of artificial intelligence and economics stands Benjamin Manning, a PhD candidate at MIT Sloan School of Management whose research explores how AI will transform not just our work, but our decision-making processes. As intelligent systems increasingly act on our behalf, Manning investigates critical questions about preference alignment, AI agency, and the future of market dynamics.
“AI systems will soon handle more of our online activities and decisions,” Manning explains. “This raises fundamental questions about how these systems should understand what we want and what happens when they start making choices for us.”
The Journey to MIT: Following a Path of Intellectual Curiosity
Manning’s academic journey reflects his interdisciplinary interests. With a bachelor’s degree in mathematics from Washington University in St. Louis and a master’s in public policy from Harvard Kennedy School, he developed a foundation that bridges quantitative analysis and social impact before arriving at MIT.
After working as a research assistant and discovering his passion for academic inquiry, Manning chose MIT Sloan for his doctoral studies specifically because of its unparalleled environment for studying the convergence of economics and computer science. “The concentration of Nobel and Turing award winners creates an intellectual environment that’s impossible to replicate elsewhere,” he notes. “The Information Technology group gives me the freedom to explore both disciplines without constraints.”
The impact of MIT’s academic environment has been transformative. “The learning curve during my first year as a PhD candidate exceeded everything I experienced in my entire undergraduate education,” Manning reflects. “While challenging, engaging with such diverse and complex ideas has equipped me with tools to conduct novel research at the intersection of economics and AI.”
Researching AI as Economic Agents
Manning’s research portfolio encompasses two interconnected domains. First, he studies how to design AI systems that can effectively represent human preferences and act as our agents in various contexts. This work examines how these AI representatives might reshape market dynamics and institutional structures when deployed at scale.
“As AI becomes more capable of understanding our preferences and making decisions aligned with our interests, we need to carefully consider how these systems should be designed,” Manning explains. “When millions of AI agents interact in markets, they could fundamentally transform economic behaviors in ways we haven’t fully anticipated.”
This research has practical implications for consumer technology, financial services, and digital marketplaces where AI assistants increasingly mediate transactions. Manning’s work helps establish frameworks for evaluating these systems’ effectiveness and their broader economic impacts.
Accelerating Social Science Through AI Simulation
The second branch of Manning’s research explores how AI can simulate human behavior to accelerate scientific discovery. Traditional social science research often moves slowly due to the challenges of recruiting participants, designing studies, and collecting data. Manning envisions a future where researchers leverage AI to run millions of behavioral simulations rapidly.
“Imagine testing experimental designs in minutes rather than months,” he says. “Researchers could identify promising research directions before investing in expensive human studies. This approach doesn’t replace human insight—it amplifies it by allowing scientists to focus on asking better questions, developing theories, and interpreting results while AI handles computational aspects.”
This methodology could revolutionize fields like behavioral economics, marketing research, and public policy analysis by dramatically shortening the feedback loop between hypothesis formation and testing. As Manning notes, “We may be approaching a world where our understanding of economic and social phenomena can keep pace with the rapid changes occurring in these systems.”
The MIT Advantage: Environment and Mentorship
Manning credits MIT’s collaborative environment for fostering his research progress. “The physical setup of our offices promotes interaction—PhD students share bright spaces with views of Kendall Square, and our suites are integrated with faculty offices, making it easy to discuss ideas with advisors.”
When facing research challenges, Manning has developed a productive ritual: “I leave my phone behind and walk around campus or along the river. That simple act of walking without distractions often leads to solving my hardest problems and generating my best ideas.”
The impact of studying at MIT extends beyond academic preparation. “Even in my fourth year, being an MIT student feels surreal,” Manning admits. “That feeling of privilege and opportunity continues to motivate my work.”
Looking Forward: Academic Aspirations
After completing his PhD, Manning hopes to secure a faculty position at a business school where he can continue his research at the intersection of economics and artificial intelligence. His goal is to follow in the footsteps of his MIT Sloan mentors, contributing to both academic knowledge and practical applications in this rapidly evolving field.
“The future of work isn’t just about how AI will change jobs,” Manning concludes. “It’s about how AI can help us understand human behavior, make better decisions, and accelerate our collective learning. We’re potentially entering an era where our understanding of social and economic systems can evolve nearly as quickly as the systems themselves change.”
This vision represents a profound shift in how we approach complex social problems—using AI not just as a tool that performs tasks, but as a partner in expanding human knowledge and capabilities. Manning’s research at MIT stands at the forefront of this exciting frontier.