Comparing AI and human decision-making mechanisms in daily collaborative experiments
Summary: Artificial intelligence (AI) is trying to catch up with human beings in many aspects. In this track, the potential for replacing human decision-making with AI models, such as large language models (LLMs), has become a topic of considerable debate. To test the performance of AI in daily deci...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225009721 |
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| author | Linghao Wang Zheyuan Jiang Chenke Hu Jun Zhao Zheng Zhu Xiqun Chen Ziyi Wang Tianming Liu Guibing He Yafeng Yin Der-Horng Lee |
| author_facet | Linghao Wang Zheyuan Jiang Chenke Hu Jun Zhao Zheng Zhu Xiqun Chen Ziyi Wang Tianming Liu Guibing He Yafeng Yin Der-Horng Lee |
| author_sort | Linghao Wang |
| collection | DOAJ |
| description | Summary: Artificial intelligence (AI) is trying to catch up with human beings in many aspects. In this track, the potential for replacing human decision-making with AI models, such as large language models (LLMs), has become a topic of considerable debate. To test the performance of AI in daily decision-making, we compared humans, LLMs, and reinforcement learning (RL) in a multi-day commute decision-making game. It denotes a collaborative decision-making process where individual and collective outcomes are interdependent. We examined various performance metrics, including overall system results, system convergence progress, individual decision dynamics, and individual decision mechanisms. We find that LLMs exhibit human-like abilities to learn from historical experience and achieve convergence when making daily commute decisions. However, in the context of multi-person collaboration, LLMs still face challenges, such as weak perception of others’ choices, poor group decision-making mechanisms, and a lack of physical knowledge. |
| format | Article |
| id | doaj-art-b954f23ae6ff4234a6e52f2c39c39cc8 |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-b954f23ae6ff4234a6e52f2c39c39cc82025-08-20T03:24:52ZengElsevieriScience2589-00422025-06-0128611271110.1016/j.isci.2025.112711Comparing AI and human decision-making mechanisms in daily collaborative experimentsLinghao Wang0Zheyuan Jiang1Chenke Hu2Jun Zhao3Zheng Zhu4Xiqun Chen5Ziyi Wang6Tianming Liu7Guibing He8Yafeng Yin9Der-Horng Lee10Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaInstitute of Intelligent Transportation Systems & Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaInstitute of Intelligent Transportation Systems & Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaDepartment of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ, USAInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China; Corresponding authorInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China; Corresponding authorZhejiang Tranalytic Technology Company, Wenzhou, ChinaDepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USADepartment of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, ChinaDepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USAZhejiang University–University of Illinois Urbana Champaign Institute, Zhejiang University, Hangzhou, ChinaSummary: Artificial intelligence (AI) is trying to catch up with human beings in many aspects. In this track, the potential for replacing human decision-making with AI models, such as large language models (LLMs), has become a topic of considerable debate. To test the performance of AI in daily decision-making, we compared humans, LLMs, and reinforcement learning (RL) in a multi-day commute decision-making game. It denotes a collaborative decision-making process where individual and collective outcomes are interdependent. We examined various performance metrics, including overall system results, system convergence progress, individual decision dynamics, and individual decision mechanisms. We find that LLMs exhibit human-like abilities to learn from historical experience and achieve convergence when making daily commute decisions. However, in the context of multi-person collaboration, LLMs still face challenges, such as weak perception of others’ choices, poor group decision-making mechanisms, and a lack of physical knowledge.http://www.sciencedirect.com/science/article/pii/S2589004225009721Artificial intelligence applicationsComputing methodologySocial sciences |
| spellingShingle | Linghao Wang Zheyuan Jiang Chenke Hu Jun Zhao Zheng Zhu Xiqun Chen Ziyi Wang Tianming Liu Guibing He Yafeng Yin Der-Horng Lee Comparing AI and human decision-making mechanisms in daily collaborative experiments iScience Artificial intelligence applications Computing methodology Social sciences |
| title | Comparing AI and human decision-making mechanisms in daily collaborative experiments |
| title_full | Comparing AI and human decision-making mechanisms in daily collaborative experiments |
| title_fullStr | Comparing AI and human decision-making mechanisms in daily collaborative experiments |
| title_full_unstemmed | Comparing AI and human decision-making mechanisms in daily collaborative experiments |
| title_short | Comparing AI and human decision-making mechanisms in daily collaborative experiments |
| title_sort | comparing ai and human decision making mechanisms in daily collaborative experiments |
| topic | Artificial intelligence applications Computing methodology Social sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225009721 |
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