使用大规模实验和机器学习来发现人类决策理论
Joshua C Peterson1, David D Bourgin2, Mayank Agrawal3,4
1Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. joshuacp@princeton.edu.
概括
研究人员开发了一种新的机器学习方法来模拟人类的决策. 这种方法使用大数据集来创建可解释的心理理论,从而产生更准确的风险选择行为模型.
科学领域:
- 认知心理学
- 计算神经科学
- 行为经济学
背景情况:
- 了解人类决策在社会科学和工程学中至关重要.
- 量化模型是预测选择的关键, 但现有的理论有局限性.
研究的目的:
- 为了加速人类决策的进步.
- 使用机器学习开发可解释的心理理论.
- 发现一个更准确的风险选择行为模型.
主要方法:
- 进行了迄今为止最大的风险选择实验.
- 使用大型数据集来驱动机器学习算法.
- 通过人工神经网络对可差分决策理论进行基于梯度的优化.
主要成果:
- 在决策研究中成功回顾历史发现.
- 证明现有理论可以得到改进.
- 发现了一种新的,更准确的人类决策模式.
结论:
- 机器学习被解释性所限制,可以推进心理学理论.
- 新模型提供了更高的准确性,
- 这种方法有助于更深入地了解风险选择和人类行为.
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