论文标题

善良杀死混乱:令人愉快的性能在不确定性下提高团队绩效

Kill Chaos with Kindness: Agreeableness Improves Team Performance Under Uncertainty

论文作者

Lim, Soo Ling, Bentley, Peter J., Peterson, Randall S., Hu, Xiaoran, McLaren, JoEllyn Prouty

论文摘要

团队是人类成就的核心。在过去的半个世纪中,心理学家已经确定了五个跨文化有效的人格变量:神经质,外向性,开放性,尽职尽责和同意。前四个与团队绩效显示一致的关系。然而,令人愉快的(和谐,无私,谦虚和合作)表现出与团队绩效的不显着且高度可变的关系。我们通过计算建模解决了这种不一致。基于代理的模型(ABM)用于预测人格特质对团队合作的影响,然后使用遗传算法探索ABM的限制,以发现哪些特征与最佳和最差的表现相关,以解决不同级别的不确定性(噪声)的问题。探索所揭示的新依赖性通过分析迄今为止最大的团队绩效数据集的先前未观察到的数据来证实,其中包括593个团队中的3,698个个人,从事有和没有不确定性的5,000多个小组任务,收集了10年的时间。我们的发现是,团队绩效和同意之间的依赖性受到任务不确定性的调节。以这种方式将进化计算与ABM相结合,为团队合作的科学研究,做出新的预测并提高我们对人类行为的理解提供了一种新的方法。我们的结果证实了计算机建模对发展理论的潜在有用性,并阐明了随着工作环境的越来越流畅和不确定的启示。

Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously-unseen data from one the largest datasets on team performance to date comprising 3,698 individuals in 593 teams working on more than 5,000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.

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