Crowdsourcing is increasingly employed by enterprises outsourcing certain internal problems to external boundedly rational problem solvers who may be more efficient. However, despite the relative abundance of crowdsourcing research, how the matching relationship between task types and solver types works is far from clear. This study intends to clarify this issue by investigating the interplay between task structure and individual bounded rationality on crowdsourcing performance. For this purpose, we have introduced interaction relationships of task decisions to define three differently structured tasks, i.e., local tasks, small-world tasks and random tasks. We also consider bounded rationality, considering two dimensions i.e., bounded rationality level used to distinguish industry types, and bounded rationality bias used to differentiate professional users from ordinary users. This agent-based model (ABM) is constructed by combining NK fitness landscape with the TCPE (Task-Crowd-Process-Evaluation), a framework depicting crowdsourcing processes, to simulate the problem-solving process of tournament-based crowdsourcing. Results would suggest that under the same task complexity, random tasks are more difficult to complete than local tasks. This is evident in emerging industries, where the bounded rationality level of solvers is generally low, regardless of the type of solvers, local tasks always perform best and random tasks worst. However, in traditional industries, where the bounded rationality level of solvers is generally higher, when solvers are ordinary users, local tasks perform best, followed by small-world and then random tasks. When solvers are more expert, random tasks perform best, followed by small-world and then local tasks, but the gap between these three tasks in crowdsourcing performance is not immediately obvious. When solvers are professional, random tasks perform best, followed by small-world and then local tasks, and the gap between these three tasks in crowdsourcing performance is obvious.

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任务结构，个体有限理性和众包绩效：基于代理的仿真方法

企业越来越多地使用众包，将某些内部问题外包给可能效率更高的外部有限理性的问题解决者。然而，尽管众包研究相对丰富，但任务类型和求解器类型之间的匹配关系如何工作尚不清楚。本研究旨在通过调查任务结构和众包绩效的个体有限理性之间的相互作用来阐明这一问题。为此，我们引入了任务决策的交互关系来定义三个结构不同的任务，即本地任务，小世界任务和随机任务。我们还考虑了有限理性，考虑了两个维度，即用于区分行业类型的有限理性水平，以及用于区分专业用户和普通用户的有限理性偏见。该基于代理的模型（ABM）是通过将NK适应度景观与描绘众包过程的框架TCPE（任务-人群-过程-评估）相结合而构建的，以模拟基于锦标赛的众包的解决问题的过程。结果表明，在相同的任务复杂度下，随机任务比本地任务更难完成。这在新兴行业中很明显，在新兴行业中，求解器的有限理性水平通常较低，无论求解器的类型如何，本地任务始终执行最佳，而随机任务则执行最差。但是，在传统的行业中，求解器的有限理性水平通常较高，当求解器是普通用户时，本地任务执行得最好，其次是小世界，然后是随机任务。当求解器更熟练时，随机任务的执行效果最佳，其次是小任务，然后是局部任务，但是这三项任务在众包绩效中的差距并不是立即显而易见的。当求解器专业时，随机任务的执行效果最佳，其次是小任务，然后是本地任务，这三项任务之间在众包性能方面的差距显而易见。