# Machine learning and behavioral economics for personalized choice architecture

arXiv: https://arxiv.org/abs/1907.02100 · HTML: https://mlsanity.com/paper/1907.02100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities](https://mlsanity.com/paper/2111.11203/) (2021)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)
- [Artificial intelligence moral agent as Adam Smith's impartial spectator](https://mlsanity.com/paper/2305.11519/) (2023)

## Abstract (arXiv)

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.