Digitization and datafication have the potential to influence almost every aspect of our life, from firms to markets, to cities and politics. However, we need to pay attention to possible undesired consequences of datafication on social welfare and ask what regulation is needed.|
This course will start by focusing on innovation as a key input for technological progress, economic growth, and human development. Students will understand how the Intellectual Property (IP) system that is in place in Europe, the US, and many other countries works, what its strengths and weaknesses and alternatives are, and how innovation depends on IP law and competition law. Students will then be made aware of the peculiarities for innovation systems that arise through digital technologies and the recent trend towards datafication in many new and traditional industries and how to evaluate the pros and cons of such innovations.
The course will give students a working understanding of three of the main challenges for governance, regulation and policy raised by datafication and the use of machine learning and artificial intelligence. The first are incorrect automated decisions, propagation of biases and inequalities stemming from machine learning on mis-labeled or mis-sampled datasets: students will gain a high-level perspective on machine learning to better understand the fairness concerns it raises. The second challenge is the scope for unintended consequences of datafication either under benevolent or malicious use – including inequality, job substitution, misinformation, and mass surveillance: students will be exposed with the current academic research and debate on these topics. The third challenge relates to market power and the potential for markets to become less competitive: students will be presented with the relevant economic research on industrial organization and the latest regulatory changes.
About one fourth of the lectures will draw on the following textbook:
Scotchmer, Suzanne. 2004. Innovation and Incentives. MIT Press: Cambridge, MA.
Material taught in the remaining lectures is taken from academic journals, policy documents, and newspapers, supplemented by the presentations of the instructors and, potentially, a guest lecturer.
Before entering the course, students should have a good knowledge of intermediate microeconomics, game theory, and industrial organization. We assume that students know basics such as the Cournot, Bertrand, or Hotelling competition models. Data science techniques or deeper modeling skills are NOT required
The course discusses how the creation of knowledge and artistic, literary and musical works are supported in a competitive economy, especially in the digital age. We adopt an economic governance perspective, recognizing that various institutions exist that may be able to solve any specific implementation problem: private vs. public, formal vs. informal, centralized vs. decentralized institutions. Within the domain of public ordering, this includes a discussion of intellectual property law (patents, copyrights, trade secrets, trade marks and geographic indications), recognizing that intellectual property is only one way to reward inventors and innovators. We will then then consider the design of incentive mechanisms such as prizes and intellectual property, different models of cumulative innovation, and the relationship of competition (law) to IP licensing and joint ventures.
We cover specific topics that are relevant for competition policy and regulation and depend on late technological progress in the digital age. We take a skeptical look at the promise and challenges of machine learning and artificial intelligence, examine the potential applications of datafication on the main aspects of economic activity, namely the changing nature of firms, work, and market power.
We will also look at the implications of datafication for the broader society, ranging from the transformation of cities to politics in democratic and in authoritarian regimes alike.
Finally, we will analyze the effects of the rise of big data as a driver of competition in the presence of indirect network effects and, as another consequence, the problems for consumers’ privacy and surveillance both by private and public entities.
To this end, the course will consist of a combination of regular classes, homework assignments, and student presentations (in teams). Formal lectures teaching the theory of innovation law & economics are combined with insights from recent newspaper articles and empirical research. Intensive class discussions, potentially a guest lecture, and student presentations of case studies complement and apply the theoretical knowledge gained to real-world issues driven by competitive forces or in the context of diverse political systems.
Type of instructions
4 hours of interactive lectures each for 9 weeks
Type of exams
Assignments & Participation (30%) + Written Exam (70%). The resit exam grade substitutes the final exam grade (if better) but not the grade for Assignments & Participation. To pass the course, each part has to be graded at least 5.5.
|Course available for exchange students|
|Master level, conditions apply|
|Written test opportunities|
|Written test opportunities (HIST)|
|Schriftelijk / Written||EXAM_01||SM 1||1||19-12-2019|
|Schriftelijk / Written||EXAM_01||SM 1||2||23-01-2020||Required materials-Recommended materials-Tests|