Accurately forecasting central bank policy decisions, particularly those of the Federal Open Market Committee (FOMC) has become increasingly important amid heightened economic uncertainty. While prior studies have used monetary policy texts to predict rate changes, most rely on static classification models that overlook the deliberative nature of policymaking. This study proposes a novel framework that structurally imitates the FOMC's collective decision-making process by modeling multiple large language models (LLMs) as interacting agents. Each agent begins with a distinct initial belief and produces a prediction based on both qualitative policy texts and quantitative macroeconomic indicators. Through iterative rounds, agents revise their predictions by observing the outputs of others, simulating deliberation and consensus formation. To enhance interpretability, we introduce a latent variable representing each agent's underlying belief (e.g., hawkish or dovish), and we theoretically demonstrate how this belief mediates the perception of input information and interaction dynamics. Empirical results show that this debate-based approach significantly outperforms standard LLMs-based baselines in prediction accuracy. Furthermore, the explicit modeling of beliefs provides insights into how individual perspectives and social influence shape collective policy forecasts.

Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members' attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee's "true" attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.

Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain. First-Order Logic theories that admit polynomial-time WFOMC w.r.t domain cardinality are called domain liftable. We introduce the concept of lifted interpretations as a tool for formulating closed-forms for WFOMC. Using lifted interpretations, we reconstruct the closed-form formula for polynomial-time FOMC in the universally quantified fragment of FO2, earlier proposed by Beame et al. We then expand this closed-form to incorporate cardinality constraints, existential quantifiers, and counting quantifiers (a.k.a C2) without losing domain-liftability. Finally, we show that the obtained closed-form motivates a natural definition of a family of weight functions strictly larger than symmetric weight functions.

The giant hedge fund, which manages 35 billion, is as much a technology company as it is a hedge fund. It uses advanced technologies to find investment opportunities, and it just hosted its annual artificial intelligence competition. One of those technological applications involves using natural-language-processing techniques to analyze the Fed minutes, such as those set for release Wednesday afternoon. "Historically, interpretations of those minutes required art, so Fed watchers pontificated and critiqued," the firm said in a note. "Now natural language processing techniques can translate those minutes into relatively objective data."

The giant hedge fund, which manages 35 billion, is as much a technology company as it is a hedge fund. It uses advanced technologies to find investment opportunities, and it just hosted its annual artificial intelligence competition. One of those technological applications involves using natural-language-processing techniques to analyze the Fed minutes, such as those set for release Wednesday afternoon. "Historically, interpretations of those minutes required art, so Fed watchers pontificated and critiqued," the firm said in a note. "Now natural language processing techniques can translate those minutes into relatively objective data."