A dataset of human decision-making in teamwork management

Han Yu, Zhiqi Shen, Chunyan Miao, Cyril Leung, Yiqiang Chen, Simon Fauvel, Jun Lin, Lizhen Cui, Zhengxiang Pan & Qiang Yang

Scientific Data 4, Article number: 160127 (2017)
Keywords: Computational science, Decision making

2017年1月17日,国际学术权威刊物自然出版集团旗下期刊《Scientific Data》杂志在线发表了南洋理工大学的LILY中心众智科学(Crowd Science)研究团队,包括于涵研究员(Lee Kuan Yew Post-Doctoral Fellow)、申志奇高级研究员、苗春燕教授、Simon Fauvel研究员、林军研究员等,与加拿大英属哥伦比亚大学梁锡高教授、中科院计算所陈益强研究员、山东大学崔立真教授以及香港科技大学杨强教授等共同撰写的论文《A dataset of human decision-making in teamwork management》(团队管理中的人类决策行为数据),发布并向学术界共享了一个综合多因子的团队管理中人类决策行为追踪与分析的大规模数据集。该数据集通过一个模拟复杂项目管理过程的游戏,给参与者提供了不同条件的团队成员的能力和任务特性,以展示他们的决策战略。数据集包含来自不同国家背景的1144名参与者在团队管理过程中决策情况、决策策略、决策结果以及情绪反应的详细数据。该数据集是目前国际公开发布的第一个同时涵盖决策这四个方面的数据集。通过重复测量,数据集可以帮助建立团队管理中决策的基线变异性,导致更现实的决策理论模型和更有效的决策支持方法,为通过大数据分析来研究人类团队合作中的决策要素提供了关键的数据支持。

论文链接: http://www.nature.com/articles/sdata2016127


Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members’ capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.