AI Simulation Reveals Political Pressure's Impact on Federal Reserve Decision-Making

In AI-simulated Fed meeting, political pressure polarises board

TOKYO, Sept 11 (Reuters) - A simulated Federal Reserve meeting that used artificial intelligence agents modeled on real-life policymakers showed political pressure polarised members of the board in their rate-setting deliberations. In the study released on August 31, academics at George Washington University simulated a Federal Open Market Committee meeting using AI agents modeled by each member based on their historical policy stances, biographies and speeches. The AI FOMC was then made to process real-time economic data and financial news to reach a decision. The findings showed when political pressure is applied, the AI agents became fragmented and dissent more common, according to the paper by Sophia Kazinnik and Tara Sinclair. "This simulation shows that the Federal Reserve is only partially insulated from politics," and that "outside scrutiny can shape internal decision-making, even in an institution guided by formal rules," they said in the paper. The simulation replicates the July 2025 FOMC meeting. While few central banks may be ready to have AI agents set monetary policy, a growing number of them are using the technology to streamline operations. The Fed conducted research using generative AI models to analyse minutes of the FOMC meetings. The European Central Bank uses machine learning models to forecast euro-area inflation. The Bank of Japan uses AI to gather information and deepen economic analyses. Research it published in December used large language models (LLM) to show how key factors driving up prices may be shifting to labour costs from raw material prices. Australia's central bank is testing a new AI tool that gives summaries on policy-related analytical questions, its governor Michele Bullock said on September 3. "To be clear, we are not using AI to formulate or set monetary policy or any other policy," she said. "Instead, we are looking to leverage it to improve efficiency and amplify the impact of staff efforts in areas such as research and analysis." Central banks see strategic importance in AI and are actively experimenting on data retrieval and analyses, the Bank for International Settlements said in a report in April. "Yet, below the surface, many central banks are still in the initial adoption phase," given the need to ensure the technology is used with adequate governance and high-quality data, it said. Reporting by Leika Kihara; Editing by Sam Holmes Our Standards: The Thomson Reuters Trust Principles., opens new tab

In AI-Simulated Fed Meeting, Political Pressure Polarises Board

TOKYO (Reuters) -A simulated Federal Reserve meeting that used artificial intelligence agents modeled on real-life policymakers showed political pressure polarised members of the board in their rate-setting deliberations. In the study released on August 31, academics at George Washington University simulated a Federal Open Market Committee meeting using AI agents modeled by each member based on their historical policy stances, biographies and speeches. The AI FOMC was then made to process real-time economic data and financial news to reach a decision. The findings showed when political pressure is applied, the AI agents became fragmented and dissent more common, according to the paper by Sophia Kazinnik and Tara Sinclair. "This simulation shows that the Federal Reserve is only partially insulated from politics," and that "outside scrutiny can shape internal decision-making, even in an institution guided by formal rules," they said in the paper. The simulation replicates the July 2025 FOMC meeting. While few central banks may be ready to have AI agents set monetary policy, a growing number of them are using the technology to streamline operations. The Fed conducted research using generative AI models to analyse minutes of the FOMC meetings. The European Central Bank uses machine learning models to forecast euro-area inflation. The Bank of Japan uses AI to gather information and deepen economic analyses. Research it published in December used large language models (LLM) to show how key factors driving up prices may be shifting to labour costs from raw material prices. Australia's central bank is testing a new AI tool that gives summaries on policy-related analytical questions, its governor Michele Bullock said on September 3. "To be clear, we are not using AI to formulate or set monetary policy or any other policy," she said. "Instead, we are looking to leverage it to improve efficiency and amplify the impact of staff efforts in areas such as research and analysis." Central banks see strategic importance in AI and are actively experimenting on data retrieval and analyses, the Bank for International Settlements said in a report in April. "Yet, below the surface, many central banks are still in the initial adoption phase," given the need to ensure the technology is used with adequate governance and high-quality data, it said. (Reporting by Leika Kihara; Editing by Sam Holmes)

In AI-simulated Fed meeting, political pressure polarises board

TOKYO (Reuters) -A simulated Federal Reserve meeting that used artificial intelligence agents modeled on real-life policymakers showed political pressure polarised members of the board in their rate-setting deliberations. In the study released on August 31, academics at George Washington University simulated a Federal Open Market Committee meeting using AI agents modeled by each member based on their historical policy stances, biographies and speeches. The AI FOMC was then made to process real-time economic data and financial news to reach a decision. The findings showed when political pressure is applied, the AI agents became fragmented and dissent more common, according to the paper by Sophia Kazinnik and Tara Sinclair. "This simulation shows that the Federal Reserve is only partially insulated from politics," and that "outside scrutiny can shape internal decision-making, even in an institution guided by formal rules," they said in the paper. The simulation replicates the July 2025 FOMC meeting. While few central banks may be ready to have AI agents set monetary policy, a growing number of them are using the technology to streamline operations. The Fed conducted research using generative AI models to analyse minutes of the FOMC meetings. The European Central Bank uses machine learning models to forecast euro-area inflation. The Bank of Japan uses AI to gather information and deepen economic analyses. Research it published in December used large language models (LLM) to show how key factors driving up prices may be shifting to labour costs from raw material prices. Australia's central bank is testing a new AI tool that gives summaries on policy-related analytical questions, its governor Michele Bullock said on September 3. "To be clear, we are not using AI to formulate or set monetary policy or any other policy," she said. "Instead, we are looking to leverage it to improve efficiency and amplify the impact of staff efforts in areas such as research and analysis." Central banks see strategic importance in AI and are actively experimenting on data retrieval and analyses, the Bank for International Settlements said in a report in April. "Yet, below the surface, many central banks are still in the initial adoption phase," given the need to ensure the technology is used with adequate governance and high-quality data, it said.