Can LLMs simulate tax evasion emergence?
Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation
January 31, 2025
https://arxiv.org/pdf/2501.18177This paper investigates the emergence of tax evasion (informal economy) using a multi-agent simulation. Agents, representing individuals in an economy, make decisions about paying taxes using a combination of a Large Language Model (LLM) for initial suggestions and Deep Reinforcement Learning (DRL) for final decisions, considering personal traits (encoded as tweets), government policies, and economic incentives.
Key points for LLM-based multi-agent systems:
- LLM-DRL Hybrid Agent: Agents use LLMs to process complex information (personality, history, policies) and provide initial action suggestions (tax payment amount). DRL then refines these suggestions based on rewards and penalties, creating a more nuanced decision-making process.
- Emergent Behavior: The system is designed to allow informal economic activity (tax evasion) to emerge naturally from agent interactions rather than being pre-programmed.
- Personality Modeling: Agent personalities are modeled using real tweets, providing diverse behavioral traits and influencing tax compliance decisions. Experimentation with synthetic "pro-tax evasion" tweets showed a measurable effect on agents' compliance behavior, highlighting the impact of external narratives.
- Policy Exploration: The model simulates the impact of different government policies (tax rates, public goods provision, enforcement) on the emergence and dynamics of tax evasion, offering insights for policy optimization. Stronger enforcement and better public goods provision decrease tax evasion.
- Scalable Simulation: The agent-based simulation can be scaled to model larger populations and longer time horizons, allowing for more realistic and complex economic scenarios.