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Missing the Wood for the Wrong Trees: On the Difficulty of Defining the Complexity of Complex Problem Solving Scenarios

机译:为错树错过树林:关于定义复杂问题解决方案的复杂性的困难

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摘要

In this paper we discuss how the lack of a common framework in Complex Problem Solving (CPS) creates a major hindrance to a productive integration of findings and insights gained in its 40+-year history of research. We propose a framework that anchors complexity within the tri-dimensional variable space of Person, Task and Situation. Complexity is determined by the number of information cues that need to be processed in parallel. What constitutes an information cue is dependent on the kind of task, the system or CPS scenario used and the task environment (i.e., situation) in which the task is performed. Difficulty is conceptualised as a person’s subjective reflection of complexity. Using an existing data set of N = 294 university students’ problem solving performances, we test the assumption derived from this framework that particular system features such as numbers of variables (NoV) or numbers of relationships (NoR) are inappropriate indicators of complexity. We do so by contrasting control performance across four systems that differ in these attributes. Results suggest that for controlling systems (task) with semantically neutral embedment (situation), the maximum number of dependencies any of the output variables has is a promising indicator of this task’s complexity.
机译:在本文中,我们讨论了复杂问题解决(CPS)中缺乏通用框架的情况如何严重阻碍了其40多年研究历史中获得的发现和见解的有效整合。我们提出了一个框架,将人员,任务和情况的三维可变空间内的复杂性锚定。复杂度取决于需要并行处理的信息提示的数量。构成信息提示的内容取决于任务的种类,使用的系统或CPS方案以及执行任务的任务环境(即情况)。困难被概念化为一个人对复杂性的主观反映。使用现有的N = 294位大学生解决问题的表现的数据集,我们测试了从该框架得出的假设,即特定的系统功能(例如变量数量(NoV)或关系数量(NoR))是不恰当的复杂性指标。为此,我们通过对比四个属性不同的系统的控制性能来做到这一点。结果表明,对于控制具有语义中性嵌入(状况)的系统(任务),任何输出变量具有的最大依赖项数量都可以表明该任务的复杂性。

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