What Are the Actual Benefits of Machine Learning?

Digital Players & Future Markets – Game Theory in Machine Learning & Common Ownership topics | with Martin Schmalz

In this episode, Martin Schmalz explains what machine learning has to do with economics and game theory and its relationship to common ownership. He gives some practical examples for game theoretic situations in which machine learning is already used, and shows where potential benefits and risk for consumers might lie.

In addition, we discuss the topic of common ownership – another one of Martins’ fields of research, i.e. situations in which individuals or groups simultaneously hold shares of competing companies in a market sector. According to Martin, this phenomenon occurs much more often than you might think. He explains its effect on competition and we discuss its potential for collusive market behaviour.

Martin Schmalz is professor of Finance and Economics at the University of Oxford Saïd Business School and co-author of the book 'The Business of Big Data'.

His research interests, among others, are in the areas of financial economics, artificial intelligence and machine learning.