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AI for social science and social science of AI: A Survey

Ruoxi Xu, Yingfei Sun, Mengjie Ren, Shiguang Guo, Ruotong Pan, Hongyu Lin, Le Sun, Xianpei Han

TL;DR

Xu et al. present a two-pronged view on AI and social science: AI for social science (AI as a methodological aid) and the social science of AI (AI agents as social entities). The paper foregrounds large language models as a enabling technology, detailing their roles in hypothesis generation and verification, and surveys cross-disciplinary applications and public simulation tools. It provides a cohesive framework to distinguish the two directions, catalogues state-of-the-art platforms like GenerativeAgents, SkyAGI, AgentVerse, and LangChain, and discusses limitations, ethics, and future research directions. Overall, the work highlights the potential and challenges of integrating AI with social science, arguing for standardized evaluation, domain knowledge integration, and multimodal approaches as AI becomes pervasive in society.

Abstract

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.

AI for social science and social science of AI: A Survey

TL;DR

Xu et al. present a two-pronged view on AI and social science: AI for social science (AI as a methodological aid) and the social science of AI (AI agents as social entities). The paper foregrounds large language models as a enabling technology, detailing their roles in hypothesis generation and verification, and surveys cross-disciplinary applications and public simulation tools. It provides a cohesive framework to distinguish the two directions, catalogues state-of-the-art platforms like GenerativeAgents, SkyAGI, AgentVerse, and LangChain, and discusses limitations, ethics, and future research directions. Overall, the work highlights the potential and challenges of integrating AI with social science, arguing for standardized evaluation, domain knowledge integration, and multimodal approaches as AI becomes pervasive in society.

Abstract

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.
Paper Structure (55 sections, 3 figures, 4 tables)

This paper contains 55 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. AI for social science
  4. Hypothesis Generation
  5. Literature Review
  6. Hypothesis Propose
  7. Conclusion
  8. Hypothesis Verification
  9. Experiment Research
  10. Experiment Assistant
  11. Experiment Simulation
  12. Conclusion
  13. Survey Research
  14. Sampling
  15. Measurement
  16. ...and 40 more sections

Figures (3)

  • Figure 1: Overview of the intersection of AI and social science. We have separately discussed "AI for social science" which summarizes the application of AI at every stage of social science research to provide guidance on tool selection for researchers, "social science of AI" which systematically describes the intelligence level and characteristics of AI agents from a social science perspective on different sub-disciplines, and "public tools and resources" which focus on simulation tools. These fields share technical methodologies to some extent, yet they possess distinct research subjects and objectives.
  • Figure 2: Computer simulation respectively in the context of "AI for social science" and "social science of AI". For “AI for social science”, AI agents are deployed to mimic human behaviors to enhance the understanding of human society. Conversely, "social science of AI" delves into AI agents' own social questions.
  • Figure 3: The application of large language models at every stage of social science research. Large language models offer new possibilities for improving existing social science research processes and automated science, but also bring new potential risks and ethical issues. Social science researchers should carefully consider whether and how to apply large language models in their research.