We work on projects with the potential to impact both academic research and policy-making.
We are interested in developing solutions to current and future problems at the intersections of digital marketplaces and platforms, market design and algorithmic decision-making. We reach out to policy-makers and the public to get such solutions implemented.
Designing algorithm checking procedures
In order to address the proliferation of machine learning-algorithms, new regulatory frameworks and policy initiatives increasingly checks of such algorithms. Common checking procedures cannot address all the challenges of algorithm checks outright. For instance, typical one-off product tests cannot address that algorithms often change frequently.
We use the market design toolbox to address such challenges. We view algorithm checks as information production processes, which can be optimized within – potentially conflicting – constraints.
lab in the field-experiment.
Designing markets for data
Numerous policy and industry initiatives try to start marketplaces or even multi-service platforms for data. In theory, data provision has zero marginal costs for sellers. In practice, however, the preparation of existing data and collection of new data is costly and demand for data is difficult to assess. Furthermore, data often only generates utility for buyers in combination with other data and the utility of data can be very heterogeneous among buyers.
We are interested in developing building blocks of markets for data, many of which could also be incorporated in algorithm checking procedures.
Detecting and preventing misbehavior in digital markets
As markets become more and more digitized, both old and new types of misbehavior of market participants arise. Collusion, fraud, discrimination or other forms of misbehavior often take new forms, which are sometimes easier and sometimes more difficult to detect and prevent.
We are interested in uncovering misbehavior in digitized markets and developing changes in the respective's market design to alleviate them.
Enabling digital health innovations
Digitizing health care has great potential. For instance, the digitization of health records could develop into a particular form of digital platform enabling new health treatments such as precision medicine. The SARS-CoV-2 pandemic is likely to serve as a catalyzer in this direction. However, the mixed reception and success of contact tracing apps during the pandemic also provides a cautionary tale. Raising the full potential of such innovations is no easy feat.
We are interested in developing methods and tools to encourage participation in such innovations.
Adressing economic challenges of free digital products and services
Many essential digital products and services are provided to consumers essentially for free. Yet, consumers indirectly pay for such services, for instance, by paying attention to ads or by providing personal data. This causes a number of issues, such as distortions in the measurement of welfare or challenges in the definition of markets relevant to competition policy.
We are interested in the empirical measurement of the consumer surplus of free digital products and services, which can provide valuable information to deal with such issues.
Many of the problems we try to address bring about similar methodological challenges, such as scaling up the elicitation of preferences or spending research budgets as effective as possible. We develop new methods to address these challenges, many of which make use of concepts and techniques from the machine learning literature.