AI 中文总结
该研究基于NK模型构建含任务依赖的劳动力模型，探究生产力景观崎岖度对组织优化劳动分工的影响，通过群体选择算法揭示非线性自适应动态，为企业适配市场、促进创新提供洞见。
AI 中文摘要
专业化与任务分配可提升从生物有机体到社会经济机构等各类系统的效率与创新能力。任务分配的演化及其对组织生产力的影响，涵盖了任务依赖关系与适应性策略之间的动态关联。受进化生物学中广泛应用的崎岖景观NK模型启发，我们探究组织的劳动分工，该模型融入了组织内技术专家属性间的相互依赖关系。我们的模型将员工分为两类，按任务分配策略划分：专家（specialists）被永久分配至单一任务，通才（generalists）则在每个时间步随机选择一项任务。我们研究由任务依赖关系塑造的生产力景观崎岖度如何影响组织优化劳动分工、满足市场需求的能力。通过群体选择算法，我们揭示了非线性适应性动态的涌现，为企业如何调整策略以满足市场需求、促进创新提供了洞见。
英文摘要
Specialization and task allocation enhance efficiency and innovation across diverse systems, from biological organisms to socioeconomic institutions. The evolution of task distribution and its influence on organizational productivity encapsulate the dynamics between task dependencies and adaptive strategies. We explore the organizational division of labor, inspired by the NK model of rugged landscapes, which is widely applied in evolutionary biology, and incorporate interdependencies among the attributes of technical experts within an organization. Our model considers two types of employees characterized by their task allocation strategies: specialists, who are permanently assigned to a single task, and generalists, who stochastically select a task at each time step. We investigate how the ruggedness of the productivity landscape, shaped by task interdependency, affects the organization's capacity to optimize labor division and meet market demands. Using group selection algorithms, we reveal the emergence of nonlinear adaptive dynamics, providing insights into how companies can adapt their strategies to meet market demands and foster innovation.
Comments17 pages, 9 figures, 1 table
