Human Behavior Simulation: Objectives, Methodologies, and Open Problems

TL;DR

The paper surveys human behavior simulation across cognitive, physiological, social, and economic domains, framing objectives around scientific discovery and decision-making environments. It systematically analyzes three methodological families—knowledge-driven, data-driven, and their knowledge-data co-driven hybrids—with an emphasis on how large language models and DL enable enhanced realism and scale. Key contributions include a taxonomy of behaviors, a synthesis of current methods, and a discussion of open challenges such as multi-behavior integration, multi-scale modeling, and LLM-based agent realism. The work holds practical significance for building decision-support systems, urban planning tools, and policy simulations by guiding researchers toward robust, interpretable, and generalizable simulation frameworks.

Abstract

In recent years, human behavior simulation has drawn increasing attention from both academia and industry. The reasons fall into two aspects. First, simulation serves as a critical tool for understanding human behaviors, which has become one of the most important research topics in the history. Second, researchers have gradually reached a consensus that simulation, especially human behavior simulation, is critical for real-world decision-making systems. As a result, lots of human behavior simulation research and applications have sprung up across numerous disciplines in the past few years. In addition to the traditional methods, such as building mathematical and physical models, leveraging the recent advances of deep learning techniques -- especially the nascent Large Language Model technology -- for accurate human behavior simulation has also been one of the hottest research topics. In this study, we provide a comprehensive review of the latest research advancements in human behavior simulation. We summarize the objectives, problem formulations, and commonly used methods and discuss the consistency in the development of related research in different disciplines, which reveals the gaps and opportunities for high-impact research in this promising direction.

Figures (6)

• Figure 1: The relationship between the four types of behavior.
• Figure 2: The illustration of cognitive behavior simulation.
• Figure 3: The illustration of Physiological behavior simulation.
• Figure 4: The illustration of social behavior simulation.
• Figure 5: The illustration of economic behavior simulation.