EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments

TL;DR

EconAI introduces a novel LLM-powered simulation framework that models adaptive economic agents with dynamic decision-making, incorporating sentiment, memory, and long-term goals for more realistic economic environment simulations.

Contribution

It is the first system to unify macro and microeconomic agent interactions using LLMs with adaptive behaviors and memory mechanisms.

Findings

Improves stability in economic responses.

Replicates real-world employment-consumption cycles.

Enhances decision robustness.

Abstract

The integration of large language models (LLMs) in economic simulations has significantly enhanced agent-based modeling, yet existing frameworks struggle to capture the interplay between short-term optimization and long-term strategic planning. Conventional approaches rely on static data-driven predictions, failing to incorporate adaptive behaviors influenced by economic sentiment, market volatility, and individual goals. To address these limitations, we introduce a novel EconAI framework, incorporating economic sentiment indexing (ESI), memory weighting, and dynamic decision-making mechanisms. By quantifying economic belief, adjusting historical data influence, and linking work-consumption behaviors, EconAI achieves a more human-like decision process, where agents adapt their actions based on both market signals and long-term objectives. It is the first LLM-powered simulation system that can simulate the macro/microeconomic environment and interactions in a unified framework. Empirical evaluations show that EconAI improves stability in economic responses, better replicates real-world employment-consumption cycles, and enhances overall decision robustness. This advancement marks a crucial step towards more realistic, adaptive economic agent simulations.