We are interested in the intersections of digital platforms, market design and algorithmic decision-making.
Growing omnipresence of digital platforms
Search engines, social networks, sharing platforms, app stores, price comparison sites and other digital platforms increasingly influence our lives, both online and offline. The market capitalizations of the firms running digital platforms belong to the highest market capitalizations in the world.
Growing omnipresence of algorithmic decision-making
At the same time, we are becoming more and more aware of the omnipresence of algorithmic decision-making, particularly machine learning, in the products we buy and the services we use. To uninformed observers, applications of such algorithms might sometimes seem almost magical.
Growing importance of the market design paradigm
Probably less prominent, but nonetheless impactful, is the shift of the economics discipline towards actively shaping markets. Instead of just analyzing their rules and institutions, economists increasingly develop concrete solutions for problems in a market setting, applying the economics tool set with an engineering mindset. Economic policy-making as well as business practice are getting increasingly influenced by this paradigm.
These three developments interact and sometimes even reinforce each other. For instance, digital platforms change the design of markets and sometimes create entirely new ones. A common characteristic of digital platforms is their use of algorithmic decision-making, which in turn becomes increasingly important in the design of markets. The research of the Junior Research Group “Digital Market Design” focusses on such intersections.
Four examples of how digital platforms change the rules and institutions of markets:
Changing the design of markets is at the core of the business strategy of most of these digital platforms. The firms behind these platforms increasingly employ economists to optimize these market design interventions, sometimes with unfavorable side-effects.
Algorithmic decision-making is also increasingly used to automate market design components on digital platforms. Four examples:
The usage of algorithmic components can be both beneficial and detrimental to reach the particular aims of a market design. For instance, while they can lower search and transaction costs or enable new market mechanisms, they can also mirror or even super-charge human biases or undesirable behavior. The net effect is often difficult to untangle.
Algorithms are trained and used by economic actors. As algorithms become more and more ubiquitous, the incentive schemes in play come under scrutiny. Some incentive schemes lead to beneficial algorithmic outcomes, some do not. Two examples:
Algorithmic decision-making is part of socioeconomic processes. For economists, it will be crucial not only to understand these processes, but to also actively shape these processes to deliver desirable economic outcomes.