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arXiv · 2605.13762

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

Annie Liu·Zane Cao·Lang Chen·Zongxin Xu·Zigan Wang
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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.

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Annie Liu, Zane Cao, Lang Chen, Zongxin Xu, Zigan Wang. 2026-05-13. EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments. https://arxiv.org/abs/2605.13762

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