Evidence map›Paper›PMID 40520093›Full record

ArticleiScience2025

Comparing AI and human decision-making mechanisms in daily collaborative experiments.

Linghao Wang, Zheyuan Jiang, Chenke Hu, Jun Zhao, Zheng Zhu, Xiqun Chen, Ziyi Wang, Tianming Liu, Guibing He, Yafeng Yin and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Linghao WangInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Zheyuan JiangInstitute of Intelligent Transportation Systems & Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Chenke HuInstitute of Intelligent Transportation Systems & Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Jun ZhaoDepartment of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ, USA.
Zheng ZhuInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Xiqun ChenInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
Ziyi WangZhejiang Tranalytic Technology Company, Wenzhou, China.
Tianming LiuDepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA.
Guibing HeDepartment of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
Yafeng YinDepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA.
Der-Horng LeeZhejiang University-University of Illinois Urbana Champaign Institute, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial intelligence applicationsComputing methodologySocial sciences

Identifiers

PMID40520093
PMCPMC12167486

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.