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• W2955871703 abstract "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." @default.
• W2955871703 created "2019-07-12" @default.
• W2955871703 creator A5035241072 @default.
• W2955871703 creator A5045963738 @default.
• W2955871703 date "2019-07-03" @default.
• W2955871703 modified "2023-09-27" @default.
• W2955871703 title "Machine learning and behavioral economics for personalized choice architecture" @default.
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• W2955871703 hasPublicationYear "2019" @default.
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