It’s July 2025 and the Federal Reserve is facing a moment of crisis. As its Federal Open Market Committee (FOMC) meeting convenes, inflation remains stubborn, the labor numbers show signs of strain and global financial markets are parsing clues for insights on fiscal policy. Looming over the already-charged summit is intense political pressure from President Donald Trump to slash interest rates.
At the center of the meeting is beleaguered Fed Chair Jerome Powell, guiding the agenda amid speculation that his ongoing feud with Trump will lead to his dismissal. Federal Reserve Governor Christopher Waller, a traditional interest rate hawk and a rumored candidate for Powell’s job, seems to have softened his stance on Trump’s demand for cuts. Fellow Board of Governors member Michelle W. Bowman is also sending dove-ish rate signals, a notable shift from her past hold-the-line position.
The stakes are high. The atmosphere is tense.
But the meeting isn’t real.
Welcome to the SIM Fed, an artificial intelligence simulation of a pivotal Federal Reserve meeting. Co-created by George Washington University Professor of Economics and International Affairs Tara M. Sinclair, “FOMC In Silico,” as she dubs it, is a virtual laboratory that gives economists, Fed watchers and market trackers a window into one of the most consequential decision-making processes in the world.
The framework—built as a multi-agent platform powered by an LLM and guided by text prompt commands that Sinclair likens to the “Oregon Trail” computer game—features AI-generated “personas” of actual Fed members. Grounded in real-time data, it models policy deliberations, disagreements and decisions that simulate the drama inside the Fed’s D.C. headquarters—but within a computer database.
“The idea is to use LLMs and publicly available information about these very public figures to create personas of FOMC members,” said Sinclair, who created FOMC In Silico with Stanford Research Scientist Sophia Kazinnik.
Indeed, the SIM Fed members are constructed from an archive of information: speeches, voting records, biographies, policy positions—even their communication styles. Sinclair and Kazinnik then add economic data into the mix—from unemployment rates to GDP growth to inflation figures—and stir the pot by sprinkling in high-stakes scenarios like boiling-point political pressure.
“There’s no way I can get Chair Powell and the rest of the FOMC to come over to GW for a day and let me run them through different scenario exercises,” said Sinclair, who chairs the Economics Department at the Columbian College of Arts and Sciences. “But we can create personas with flexible personalities that give us a close simulation of the real person.”
Real-Life Results
Comparing FOMC In Silico to a flight simulator for monetary policy, Sinclair launched a test run hours before the actual July FOMC meeting. After building the AI persona profiles, she and Kazinnik handed them the same economic data sheets they’d find in their D.C. Eccles Building boardroom. (Days later, they updated the simulation with newly-released labor market figures.) They then introduced the political pressure effect, instructing the personas to reflect the real-world tensions over interest rate cuts.
The AI committee nearly mirrored the real-life policymakers’ decisions. Both the AI and actual FOMC kept interest rates unchanged in a range of 4.25 percent to 4.5 percent. The AI version landed at a 4.42 percent midpoint.
Perhaps more impressively, the AI simulation recorded rare dissent among Fed members. In reality, both Waller and Bowman voted for lower rates—the first dual dissent for rate cuts since 1993. Applying the political influences, Sinclair explained, swayed arguments and fragmented AI personas in the same direction as their real-life counterparts.
“This simulation shows that the Federal Reserve is only partially insulated from politics,” Sinclair and Kazinnik noted in a working paper. “Outside scrutiny can shape internal decision-making, even in an institution guided by formal rules.”
The researchers have continued to run the simulation for each subsequent FOMC meeting—they convene every six to eight weeks—and have also applied the model to past sessions dating back to 2000. Its historical record has been stellar, predicting the policy rate within 25 basis points in 93 percent of past meetings.
In its current format, Sinclair stresses that the model is a simulator, not a forecaster. For now the goal is to grasp how the committee sets policy, not necessarily predict its decisions in advance. “It helps us better understand how all of these mechanisms work, what [the FOMC] is thinking and what kinds of influences are at play,” she said.
As they finalize their research paper, Sinclair and Kazinnik eventually plan to make the tool widely available online. In addition to economists and financial market professionals, she envisions a host of experts adapting the framework to their own fields—like political scientists simulating U.S. Supreme Court deliberations. “We’ve built an entire laboratory and it can be used for many different types of experiments,” Sinclair said.
For her purposes, Sinclair hopes the methodology will move closer to forecasting—perhaps replicating the Fed's quarterly economic projections. Meanwhile, as the traditionally cautious economics field warms to technology innovations, Sinclair sees FOMC In Silico as a bridge between market fervor and AI ascendance. “The tool meets the moment,” she said.
Likewise, her own CCAS department features an AI Economics program within its Center for Economic Research. And in class discussions, Sinclair said the Fed simulator has sparked enthusiasm among students.
“It’s impressive how fast [economists] have adopted these tools and the impact they’ve had on education,” she said. “Now we don’t just teach students how to code—we teach them how to Claude code.”