Robert Litterman on Incentives, Risk, and Why Climate Change Is a Risk-Management Problem

Robert Litterman’s path into economics began far from Wall Street. He entered Stanford in 1969 intending to study physics, but the Vietnam moratorium and campus unrest pushed him to reconsider. Stanford’s newly created human biology program offered a broader lens. “Studying humans as part of nature really is how I thought about it,” he says. One lesson stood out early: incentives. “All animals respond to incentives… humans aren’t that different.” That insight—learned through biology, psychology, and anthropology—would later become the throughline of his work in economics, finance, and climate policy.

After college, Litterman worked as a journalist for the San Diego Union, but a posting in El Centro convinced him he needed to be closer to computers. Graduate school at UC San Diego, then Minnesota, pulled him fully into economics. “I always had this dream of programming computers to do things,” he recalls, and economics became the place where data, math, and computation met real-world problems. Advisers encouraged him to explore tools like neural networks, genetic algorithms, and optimal control theory—techniques that would later define quantitative finance.

His academic career included a brief but formative stop at MIT, where he taught alongside figures like Paul Samuelson, Robert Solow, and Larry Summers. Still, he quickly realized academia wasn’t his long-term home. A move to the Minneapolis Fed followed, and then, unexpectedly, Goldman Sachs called. “They made me an offer I couldn’t refuse,” he says. Though he felt “totally unqualified,” learning to price options and build models proved transformative. Goldman became, in his words, “a kid in a candy store” for someone eager to apply quantitative methods at scale.

At Goldman, Litterman helped pioneer firmwide risk management and eventually co-developed what became the Black-Litterman model with Fischer Black. The problem they were trying to solve was simple but fundamental: portfolio optimizers were wildly unstable because they were hypersensitive to expected return assumptions. The breakthrough came from reframing the problem. “Let’s start with equilibrium,” he explains, and then allow investors to express specific views with varying confidence. That shift—anchoring portfolios in market equilibrium rather than fragile forecasts—turned the model into a lasting tool still used by sophisticated investors worldwide.

Collaboration with intellectual giants shaped his career. Working with Christopher Sims taught him the value of Bayesian thinking. When early forecasts performed poorly, Sims suggested a Bayesian prior. “Have you tried putting a Bayesian prior on?” he asked—a moment that redirected Litterman’s dissertation and approach to data entirely. With Fischer Black, the lesson was similar: recognize when a model behaves badly, then rethink the structure rather than patching it with constraints. “It helps to collaborate with geniuses,” Litterman says plainly.

A near-fatal car accident in 2014 sharpened his understanding of risk. A gasoline tanker lost control in front of him on a rain-soaked freeway, missing him by seconds. “Those seconds could have been your last,” he says. The lesson was stark: “We don’t know how much time we have.” In risk management, time uncertainty changes everything. That experience deepened his urgency around climate change, which he views through the same lens.

For Litterman, climate change is not primarily an environmental issue—it is a catastrophic risk-management failure. “We’re not pricing the risk,” he repeats throughout the interview. Economists, he argues, already know what to do: create incentives that reflect worst-case scenarios. The problem isn’t technical but political. “It’s not an economic problem… it’s a political problem,” because entrenched interests resist change. Delaying action, he warns, transfers risk directly to future generations. “By not pricing carbon today, we are creating additional risk for our grandchildren.”

He rejects the idea that uncertainty justifies inaction. “You’ve got to be prepared for worst-case scenarios,” he says. Hope alone is not a strategy: “Hope is not a good risk-management policy.” The only effective brake, in his view, is carbon pricing—strong, immediate, and credible enough to change expectations. Markets, he notes, are forward-looking, and today’s investment decisions reflect the belief that emissions will not be priced anytime soon. That pessimism, he says, is “the sad thing.”

Looking ahead, Litterman sees promise in clean energy, nuclear power as reliable baseload, and AI’s potential—so long as incentives align. AI’s growing energy demand, he argues, isn’t the real issue. “The issue is not the demand for energy… the issue is pricing emissions.” Without that, economics will always override sustainability.

When asked to define success, Litterman demurs. “Success is overrated,” he says. A better goal might be happiness. Still, success plays a role: “Setting goals and accomplishing those goals—that’s what success is all about.”

The 6Degrees team extends its heartfelt thanks to Robert Litterman for his clarity, candor, and decades of work at the intersection of economics, risk, and public policy. His perspective reminds us that the hardest problems are rarely about missing knowledge—they’re about missing incentives, and the cost of waiting until it’s too late.