In more and more situations, artificially intelligent algorithms have to model humans' (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do...

With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the...

Following Garicano (2000), we consider groups whose members decide what knowledge to acquire and how to use this knowledge in production. If efficient production requires common knowledge, all group members should become workers and acquire common knowledge. But if efficient production requires...

This paper examines the potential reinforcement of motivated beliefs when individuals with identical biases communicate. We propose a controlled online experiment that allows to manipulate belief biases and the communication environment. We find that communication, even among like-minded...