The availability of big data and the adoption of machine learning (ML) tools is profoundly, and increasingly, impacting economic behavior and social interactions. These impacts are the focus of this working package. On the one hand, big data can help improve consumer experiences. Here, we analyze models and methods for prescriptive control of service processes based on real-time data processing using advances in predictive monitoring. Specifically, we study the strategies to identify suitable intervention, compensation, and mitigation once a forecast model signals that a particular consumer is expected to face undesired outcomes. Moreover, we contribute to the development of contextualized ML methodologies to achieve a better understanding of consumer-level implications of ML adoption.

On the other, recent technological advances in the ability to track individual consumers have enabled retailers to collect troves of information on individual consumers and better predict their behavior, which facilitates price discrimination. Thus, we aim to understand from a theoretical and experimental perspective how consumer tracking affects their behavior and market outcomes.

Finally, despite the prominence of data-driven businesses and the surrounding heated policy discussion, little is known about the precise role of data in improving recommendation systems. We contribute to this literature by analyzing the importance of data accumulation for search results in general purpose search engines and for prediction in digital personalized healthcare. These research projects will improve our understanding of how the use of big data and tracking technologies requires new forms of regulation and new consumer policies.