The study evaluates mentalization—the capacity to infer others’ beliefs and intentions—in large language models (LLMs) using two economic games and cognitive computational modeling. Researchers tested 2,099 LLM agents from four model families (DeepSeek, GPT‑4.1, GPT‑5, Gemini 2.0 Flash) against opponents of varying sophistication, comparing their performance to 251 human participants. Results show that LLMs exhibit distinct mentalizing behaviors that vary by model provider and size, with strategic prompting generally enhancing performance; notably, GPT‑5 agents adapt their recursive reasoning depth to match opponent sophistication, outperforming humans in one task.
By Aamir Sohail, Xintong Zhong, Arkady Konovalov, Patricia L. Lockwood, Lei Zhang
arXiv:2608.18265v2 Announce Type: replace-cross
Abstract: We introduce a general, easy-to-implement AI-based method for modeling and analyzing the structure and complexity of human behavior. We assig...
By Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei
The paper presents an AI-based method that uses a large language model to emulate human decision-making by assigning it a "type vector" describing traits such as Altruism and Risk Aversion. By varying these dimensions and values, the authors fit the model to over 119,000 decisions from 78,657 participants in 10 classic economic games, finding that three dimensions—Risk Aversion, Strategic Sophistication, and Trust—sufficiently capture human behavior. The resulting type clusters, fewer than a dozen, predict behavior in new games, suggesting a low-dimensional, portable representation of human behavior across diverse settings.
By Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei
arXiv:2608. 01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma.
By Phu Hoa Pham, Duy Minh Dao Sy, Trung Kiet Huynh, Phu Quy Nguyen Lam, Chi Nguyen Tran, Minh Trung Le, Phong Hao Le, Dinh Nam Nguyen, Thien Ky Nguyen Dong, Elias Fernandez Domingos, Le Hong Trang, The Anh Han
arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.
By Henrique Ferraz de Arruda, Carlos Gracia L\'azaro, Alberto Aleta, Yamir Moreno
arXiv:2609.16436v1 Announce Type: cross
Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the...
By Jiayue Gaveal Fan, Arul Murugan, Shreyas Krishnan, Abhishek Nagaraj