在风险和不确定性下做决定的随机启发式启发式
Leonidas Spiliopoulos1, Ralph Hertwig1
1Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
Frontiers in psychology
|August 21, 2024
概括
确定性启发式模型可以通过添加错误机制来修改以包括随机选择. 这可以实现现实的选择错误,改善决策的认知模型.
科学领域:
- 认知科学 认知科学
- 决策 决策 决策 决策 决策
- 计算机建模 计算建模
背景情况:
- 启发式模型传统上缺乏自由参数,导致决定性结果.
- 这种确定性不考虑观察到的随机选择和决策中的错误.
研究的目的:
- 建议对确定性启发式模型进行修改,以纳入随机性.
- 引入一个允许现实的选择错误的错误机制.
主要方法:
- 讨论错误机制的理论整合到现有的无参数启发式模型中.
- 介绍和比较不同类型的错误机制.
主要成果:
- 确定性启发式可以通过添加一个单一的错误自由参数来转换为随机对应的.
- 提出的错误机制保留了核心的无参数认知过程.
结论:
- 用错误机制修改决定式启发式,提高了它们模拟现实世界决策的能力.
- 这种方法可以更好地比较启发式模型和参数丰富的替代方案.
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