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Application of Artificial Intelligence for Monetary Policy-Making

National Bank of Georgia, Working Papers  Authors: Mariam Dundua and Otar Gorgodze

The National Bank of Georgia’s (NBG) Working Papers are published to elicit comments and encourage debate on ongoing research. Working Paper Series aim to present original research contributions relevant to central banks. The views expressed here are those of the author(s) and do not necessarily represent the views of the NBG. No responsibility for them should be attributed to the NBG. The working papers have not been peer-reviewed.

The recent advances in Artificial Intelligence (AI), in particular, the development of reinforcement learning (RL) methods, are specifically suited for application to complex economic problems. We formulate a new approach looking for optimal monetary policy rules using RL. Analysis of AI generated monetary policy rules indicates that optimal policy rules exhibit significant nonlinearities. This could explain why simple monetary rules based on traditional linear modeling toolkits lack the robustness needed for practical application. The generated transition equations analysis allows us to estimate the neutral policy rate, which came out to be 6.5 percent. We discuss the potential combination of the method with state-of-the-art FinTech developments in digital finance like DeFi and CBDC and the feasibility of MonetaryTech approach to monetary policy.