• Corpus ID: 195798925

Machine learning and behavioral economics for personalized choice architecture

  title={Machine learning and behavioral economics for personalized choice architecture},
  author={Emir Hrnjic and Nikodem Tomczak},
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… 

A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities

The platform architecture is described, focusing on the details that help to maximize the quality and organization of the data throughout the whole process, from the data ingestion with a data-science purposed software development kit to the data pipelines, feature engineering and model management.

Consumer behavior clustering of food retail chains by machine learning algorithms

Analysis of the behavior of an economic agent is one of the central themes of microeconomics. Right now, with the comprehensive increase in the amount of data and the expansion of the computing

Paternalism in the governance of artificial intelligence and automated decision-making in the United Kingdom

This paper raises the question of whether recent dynamics in the governance of artificial intelligence (AI) and automated decision making (ADM) in the United Kingdom (UK) are paternalistic in nature.



Predicting human decisions with behavioral theories and machine learning

An open tournament for prediction of human choices in fundamental economic decision tasks is presented and it is suggested that integration of certain behavioral theories as features in machine learning systems provides the best predictions.

Applying Insights from Behavioral Economics to Policy Design

An understanding of psychology and other social science disciplines can inform the effectiveness of the economic tools traditionally deployed in carrying out the functions of government, which include remedying market failures, redistributing income, and collecting tax revenue.

Behavioral Economics and Public Policy: A Pragmatic Perspective

The debate about behavioral economics – the incorporation of insights from psychology into economics – is often framed as a question about the foundational assumptions of economic models. This paper

Big Data: New Tricks for Econometrics

A few tools for manipulating and analyzing big data such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships.

Human Decisions and Machine Predictions

While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Machine learning: Trends, perspectives, and prospects

The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

Counterfactual Fairness

This paper develops a framework for modeling fairness using tools from causal inference and demonstrates the framework on a real-world problem of fair prediction of success in law school.

Targeting with machine learning: An application to a tax rebate program in Italy

Economic reasoning and artificial intelligence

This work asks how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.

Machine Learning Methods for Demand Estimation

This work proposes a method of combining the underlying models via linear regression that is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions.