Machine learning and behavioral economics for personalized choice architecture
This work addresses the challenge of personalizing choice architecture for individuals in policy-making, representing an incremental step by combining existing fields.
The paper tackles the problem of weak generalization in behavioral economics nudges at the individual level by proposing to integrate machine learning and AI to design personalized interventions, aiming to support decision-making and inform policy decisions.
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.