It is common for organizations to hire workers based on their knowledge, skills, and abilities. However, despite capable workers being hired, productivity may suffer if employees’ motivational needs are not satisfied. We developed an agent-based model to simulate the completion of tasks by teams of workers in an organization. Each worker is described by an ability value and a 3-parameter motive profile expressing the individual’s needs for affiliation, achievement, and power. During each time step, each worker contributes to an assigned task at a rate determined by the worker’s ability and motive profile, the task’s difficulty and proximity to completion, and the team’s experience. When a task is completed by a team, the workers are re-assigned to a new team and task. At the end of 365 time steps, the model outputs the total number of completed tasks, which is the primary measurement of productivity. Model simulations demonstrate that hiring workers based on their ability and motivational strengths can lead to increased productivity. Additional model simulations illustrate the benefit of identifying failing tasks and re-assigning new teams to these tasks in real-time.

中文翻译：

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使用基于代理的模型评估动机和能力对基于团队的生产力的影响

组织通常根据他们的知识，技能和能力来雇用工人。但是，尽管雇用了有能力的工人，但如果不能满足员工的动机需求，生产率可能会受到影响。我们开发了一个基于代理的模型来模拟组织中的工作人员团队的任务完成情况。每个员工都由能力值和3参数动机描述，这些动机表达个人对结盟，成就和权力的需求。在每个时间步骤中，每个工作人员都按照工作能力，动机，任务的难度和完成程度以及团队的经验来确定所分配的任务。当团队完成任务时，工人将被重新分配到新的团队和任务中。在365个时间步的结尾，该模型输出已完成任务的总数，这是生产率的主要衡量标准。模型仿真表明，根据他们的能力和动机优势雇用工人可以提高生产率。附加的模型仿真说明了识别失败任务并实时将新团队重新分配给这些任务的好处。