识别风险选择的资源理性启发式
Paul M Krueger1, Frederick Callaway2, Sayan Gul3
1Department of Computer Science, Princeton University.
Psychological review
|April 18, 2024
概括
人们经常使用简单的决策策略 (启发式) 由于有限的认知资源. 机器学习可以发现最佳启发式,提高对复杂环境中人类决策的理解.
科学领域:
- 认知科学 认知科学
- 机器学习 机器学习
- 行为经济学是一种行为经济学.
背景情况:
- 人类的决策受限于有限的计算资源.
- 依赖启发式计算是常见的,但发现和预测它们的使用是具有挑战性的.
研究的目的:
- 开发一种机器学习框架,自动推导出最佳决策启发式.
- 研究人们如何在复杂的选择环境中调整他们的策略.
主要方法:
- 利用机器学习来导出考虑认知资源限制的启发式.
- 进行了一项大规模的行为实验,比较发现的启发式与不同环境中的人类选择.
主要成果:
- 该方法重新发现了已知的启发式,并确定了新的启发式.
- 人类的决策策略适应环境结构,但并不总是充分利用它们.
结论:
- 机器学习提供了一种强大的方法来发现有限理性的最佳启发式.
- 人们通常采用有效的,资源意识的策略,尽管可以改进优化.
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