To improve problem‐solving performance, individuals can rely on social learning. This approach is constrained by an individual's social network, which influences the efficiency of the problem‐solving process. To date, research disagrees on what kind of network structure is preferable, providing support for efficient network structures, as well as for inefficient networks. However, studies implicitly assume that solvers always imitate superior solutions, an assumption that lacks empirical grounding. We propose a simple derivation of an existing simulation framework by incorporating a known cognitive bias (‘IKEA effect’), whereby individuals are assumed to prioritize individual information. This effect allows inefficiencies to be embodied at the individual microlevel, reducing the need for inefficiencies at the structural macrolevel. Simulation results explain discrepancies in previous results, illustrating how more realistic microlevel assumptions substantially impact macrolevel outcomes.

中文翻译：

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宜家在集体解决问题中的作用：当个人优先考虑自己的解决方案时

为了提高解决问题的能力，个人可以依靠社交学习。这种方法受到个人社交网络的约束，这会影响问题解决过程的效率。迄今为止，研究在哪种网络结构更合适上存在分歧，这为有效的网络结构以及效率低下的网络提供了支持。但是，研究隐含地认为求解器总是模仿上乘的解决方案，这一假设缺乏经验基础。我们通过合并已知的认知偏差（“宜家效应”）提出了对现有模拟框架的简单推导，其中假定个人优先考虑个人信息。这种效果允许在单个微观级别体现低效率，从而减少了在结构宏级别对低效率的需求。