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.

翻译：团队是人类成就的核心。过去半个世纪，心理学家确定了跨文化有效的五大人格变量：神经质、外向性、开放性、尽责性和宜人性。前四种人格特质与团队绩效的关系已得到一致验证。然而，宜人性特质（即和谐、利他、谦逊与合作）与团队绩效的关系却不显著且波动较大。我们通过计算建模解决了这一矛盾。我们采用基于主体的模型（ABM）预测人格特质对团队协作的影响，随后利用遗传算法探索ABM的边界，以发现对于不同不确定性（噪声）程度的问题，哪些特质与最佳及最差绩效团队相关。通过分析一项迄今最大规模的团队绩效数据集（涵盖10年间收集的593个团队中3698名个体在超过5000项有/无不确定性的群体任务中的数据），我们验证了探索所揭示的新依赖关系。研究发现，团队绩效与宜人性之间的依赖关系受任务不确定性的调节。将进化计算与ABM相结合，为科学化研究团队协作提供了新方法，可提出新预测并增进对人类行为的理解。我们的结果证实了计算建模在理论发展中的潜在价值，同时为工作环境日益流动且不确定的未来团队模式提供了启示。