Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective

Understand

Computational experiments have emerged as a valuable method for studying complex systems, involving the algorithmization of counterfactuals.

• However, accurately representing real social systems in Agent-based Modeling (ABM) is challenging due to the diverse and intricate characteristics of humans, including bounded rationality and heterogeneity.
• To address this limitation, the integration of Large Language Models (LLMs) has been proposed, enabling agents to possess anthropomorphic abilities such as complex reasoning and autonomous learning.
• These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM.