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摘要翻译：
行为经济学通过引入选择架构的概念，改变了我们对市场参与者的思考方式，并使政策制定发生了革命性的变化。然而，尽管行为经济学的干预措施在人群层面上是有效的，但在个人层面上的努力往往表现为泛化能力弱。数据科学、人工智能(AI)和机器学习(ML)的最新发展已经显示出通过提供工具和方法来产生具有更强预测能力的模型来缓解一些弱泛化问题的能力。本文旨在描述ML和人工智能如何与行为经济学合作，通过设计个性化干预来支持和增强决策，并在假设可以抽样足够多的个性化特征和心理变量的情况下为政策决策提供信息。
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英文标题：
《Machine learning and behavioral economics for personalized choice
architecture》
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作者：
Emir Hrnjic and Nikodem Tomczak
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.
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PDF链接：
https://arxiv.org/pdf/1907.02100
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