Using large-scale experiments and machine learning to discover theories of human decision-making
Using large-scale experiments and machine learning to discover theories of human decision-making
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DOI：
10.1126/science.abe2629
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发表时间：
2021-06-11
期刊：
影响因子：
56.9
通讯作者：
Griffiths, Thomas L.
中科院分区：
文献类型：
--
作者：
Peterson, Joshua C.;Bourgin, David D.;Griffiths, Thomas L.
预测和理解人们如何做出决策一直是许多领域的一个长期目标，人类决策的量化模型为社会科学和工程学的研究提供了信息。我们展示了如何通过使用大数据集来驱动机器学习算法来加速实现这一目标的进展，这些算法被限制在产生可解释的心理学理论上。我们对风险选择进行了迄今为止最大规模的实验，并使用通过人工神经网络实现的基于梯度的可微决策理论优化来分析结果，我们能够总结历史发现，确立现有理论还有改进的空间，并发现一种新的、更准确的人类决策模型，其形式保留了几个世纪以来的研究见解。
Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.