Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation

2025-02-25

AI Summary

This paper addresses the lack of a systematic taxonomy for human-AI interaction in agent-based modeling and simulation (ABMS) amid the rise of large language models (LLMs). We propose the first five-dimensional taxonomy—Why/When/What/Who/How—specifically designed for ABMS contexts. Integrating theories from human-computer interaction, ABMS methodology, and empirical LLM deployment practices, we conduct a structured literature review and cross-dimensional pattern analysis to synthesize existing human-AI interaction practices and identify recurrent interaction archetypes. The taxonomy fills a critical gap in systematic scholarly synthesis, clarifies paradigm boundaries and collaborative pathways for human-in-the-loop integration, and provides actionable design principles for ABMS tool development. Furthermore, it exposes current interaction blind spots—such as underexplored temporal dynamics and asymmetric agency configurations—thereby delineating concrete directions for future research on human-AI co-simulation and socio-technical alignment in ABMS.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Computer Interaction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Recent interest in human-AI interactions in agent-based modeling and simulation (ABMS) has grown rapidly due to the widespread utilization of large language models (LLMs). ABMS is an intelligent approach that simulates autonomous agents' behaviors within a defined environment to research emergent phenomena. Integrating LLMs into ABMS enables natural language interaction between humans and models. Meanwhile, it introduces new challenges that rely on human interaction to address. Human involvement can assist ABMS in adapting to flexible and complex research demands. However, systematic reviews of interactions that examine how humans and AI interact in ABMS are lacking. In this paper, we investigate existing works and propose a novel taxonomy to categorize the interactions derived from them. Specifically, human users refer to researchers who utilize ABMS tools to conduct their studies in our survey. We decompose interactions into five dimensions: the goals that users want to achieve (Why), the phases that users are involved (When), the components of the system (What), the roles of users (Who), and the means of interactions (How). Our analysis summarizes the findings that reveal existing interaction patterns. They provide researchers who develop interactions with comprehensive guidance on how humans and AI interact. We further discuss the unexplored interactions and suggest future research directions.

Problem

• Investigate human-AI interactions in ABMS
• Propose taxonomy for interaction categorization
• Identify unexplored interactions and future directions

Innovation

• Integrates LLMs into ABMS
• Proposes taxonomy for human-AI interactions
• Decomposes interactions into five dimensions