Humans act via a nuanced process that depends both on rational deliberation and also on identity and contextual factors. In this work, we study how large language models (LLMs) can simulate human action in the context of social dilemma games. While prior work has focused on "steering" (weak binding) of chat models to simulate personas, we analyze here how deep binding of base models with extended backstories leads to more faithful replication of identity-based behaviors. Our study has these findings: simulation fidelity vs human studies is improved by conditioning base LMs with rich context of narrative identities and checking consistency using instruction-tuned models. We show that LLMs can also model contextual factors such as time (year that a study was performed), question framing, and participant pool effects. LLMs, therefore, allow us to explore the details that affect human studies but which are often omitted from experiment descriptions, and which hamper accurate replication.
Can Conversational AI loosen Us-Versus-Them Boundaries? The Effects of Common, Dual, and Separate Identity Framings on Pro-Immigrant Intergroup Helping
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；University of Science and Technology of China（中国科学技术大学）
Data Science Section, IT University of Copenhagen（丹麦哥本哈根IT大学数据科学系）；Computer Science Department, University of Milan（意大利米兰大学计算机科学系）；Copenhagen Center for Social Data Science, University of Copenhagen（丹麦哥本哈根大学哥本哈根社会科学数据科学中心）
When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas
ETH Zürich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；Max Planck Institute for Intelligent Systems, Tübingen（图宾根人工智能研究所）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
Payoff scaling shapes cooperation in LLM agents across languages
Faculty of Information Technology, University of Science (HCMUS), Ho Chi Minh City, Vietnam（信息技术学院，科学大学（HCMUS），胡志明市，越南）；Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam（计算机科学与工程学院，胡志明市技术大学（HCMUT），胡志明市，越南）；Vietnam National University – Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam（越南国家大学——胡志明市（VNU-HCM），胡志明市，越南）；Luxembourg Institute of Science and Technology (LIST), Luxembourg（卢森堡科学与技术研究所（LIST），卢森堡）；School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom（计算、工程与数字技术学院，泰赛德大学，米德尔斯布罗，英国）
Equal Access, Unequal Interaction: A Counterfactual Audit of LLM Fairness
Computational Modeling and Simulation University of Pittsburgh（计算建模与仿真大学匹兹堡大学）；Mathematics & Statistics Department University of Minnesota Duluth（数学与统计学系明尼苏达大学 Duluth分校）；Independent Researcher in AI and Statistics（人工智能与统计学独立研究者）；Hardware Technology Organization（硬件技术组织）
AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
Center for Applied Communication Research (CICA)（应用沟通研究中心）；Human & NonHuman Communication Laboratory（人类与非人类沟通实验室）；Faculty of Communication（传播学院）；Universidad Anáhuac México（墨西哥安纳胡阿克大学）
