手段选择问题的计算挑战:目标系统的网络结构预测了人类的表现
Daniel Reichman1, Falk Lieder2, David D Bourgin3
1Department of Computer Science, Worcester Polytechnic Institute.
Cognitive science
|August 29, 2023
概括
人们在复杂的目标选择任务中表现更好,当底层结构更类似于树时. 这项研究探讨了优化中的人类问题解决,将其与网络结构联系起来以更好地理解.
科学领域:
- 认知科学 认知科学
- 计算复杂性 计算复杂性
- 人与计算机的交互
背景情况:
- 人类解决问题往往涉及复杂的决策,特别是在选择实现多个目标的手段时.
- 经典的优化问题,如设置覆盖面和最大覆盖面,在它们的一般形式上是计算难以解决的.
研究的目的:
- 在设置覆盖和最大覆盖问题上研究人类的表现.
- 探索目标-手段网络结构与人类问题解决效率之间的关系.
- 测试树状网络结构在这些优化任务中提高人类性能的预测.
主要方法:
- 进行了三个行为实验来评估人类的表现.
- 模拟目标实现作为一个双边图,其中意味着连接到目标.
- 分析了网络结构,特别是树状结构对任务性能的影响.
主要成果:
- 证实了人们在目标系统显示更多树状结构时表现更好的预测.
- 证明了组合网络参数与人类性能之间的相关性.
- 确定了个体在多个目标中与手段选择作斗争的特定条件.
结论:
- 人类在复杂的优化任务中的表现受到底层网络结构的影响.
- 目标-手段关系中的树状结构有助于人类更有效地解决问题.
- 组合参数对于理解人类在多目标决策中的局限性是有价值的.
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