# Empowering Economic Simulation Through Situation-Aware Llm-Driven Generative System

arXiv: https://arxiv.org/abs/2606.20720 · HTML: https://mlsanity.com/paper/2606.20720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [Scaling Multi-Agent Systems with Prospect-State Propagation](https://mlsanity.com/paper/2609.08033/) (2026)

## Abstract (arXiv)

Traditional economic modeling typically follows a TOP-DOWN paradigm, neglecting individual diversity and the complexity of social interactions. To better capture the complexity of societal structure, Agent-Based Modeling (ABM) employs a BOTTOM-UP solution by incorporating micro-level dynamics to generate macroeconomic phenomena. Reinforcement Learning further improves its decision-making ability through tailored reward signals. However, existing ABM systems struggle to generalize beyond predefined scenarios. Recognizing the potential of LLM-driven role-playing in perception and human-like decision-making, we propose SAMAS, which models individual agents with rich macroeconomic understanding embedded in LLMs and economic trajectories experienced in the passing simulation steps. By jointly modeling both macro-level structural patterns and micro-level dynamic behaviors, SAMAS achieves superior performance in volatility realism and turning point prediction.