Using large-scale experiments and machine learning to discover theories of human decision-making

doi:10.1126/science.abe2629

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DOI	10.1126/science.abe2629
	Using large-scale experiments and machine learning to discover theories of human decision-making
	Joshua C. Peterson; David D. Bourgin; Mayank Agrawal; Daniel Reichman; Thomas L. Griffiths
	2021-06-11
发表期刊	Science
出版年	2021
英文摘要	Theories of human decision-making have proliferated in recent years. However, these theories are often difficult to distinguish from each other and offer limited improvement in accounting for patterns in decision-making over earlier theories. Peterson et al. leverage machine learning to evaluate classical decision theories, increase their predictive power, and generate new theories of decision-making (see the Perspective by Bhatia and He). This method has implications for theory generation in other domains. Science , abe2629, this issue p. [1209][1]; see also abi7668, p. [1150][2] 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. [1]: /lookup/doi/10.1126/science.abe2629 [2]: /lookup/doi/10.1126/science.abi7668
领域	气候变化 ; 资源环境
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文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/329916
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Joshua C. Peterson,David D. Bourgin,Mayank Agrawal,et al. Using large-scale experiments and machine learning to discover theories of human decision-making[J]. Science,2021.
APA	Joshua C. Peterson,David D. Bourgin,Mayank Agrawal,Daniel Reichman,&Thomas L. Griffiths.(2021).Using large-scale experiments and machine learning to discover theories of human decision-making.Science.
MLA	Joshua C. Peterson,et al."Using large-scale experiments and machine learning to discover theories of human decision-making".Science (2021).