Events

In China, most legal disputes concerning unauthorized data scraping from online platforms are adjudicated under the framework of the Anti-Unfair Competition Law, particularly through the application of its general clause. In earlier judicial practice, courts interpreted this general clause in a manner that effectively granted platform data a level of protection akin to property rights. However, recent developments indicate a shift: fostering innovation has emerged as a key consideration in determining whether unauthorized data scraping constitutes unfair competition. This suggests that courts are beginning to balance platform data protection against the imperative of encouraging innovation. This study examines recent judicial practices in China concerning data scraping from online platforms, explicates the courts’ reasoning, and argues that although the incorporation of innovation into the legal balancing framework is a promising step, a narrowly construed understanding of innovation undermines the ability to genuinely achieve that goal.

Ziwei Cheng is an Associate Professor at the Law School, Shenzhen University. Her research focuses on anti-unfair competition law, with particular emphasis on data related issues and digital market regulation.

Moderation: Dr. Klaus Wiedemann

Firms rely on academic science and actively participate in the production of scientific knowledge. However, the impact of industry on academic science remains unclear. This study utilizes the site selection decisions of “Million Dollar Plants” (MDPs) to estimate the causal effects of industry on academic science. I compare the responses of scientists in counties that successfully attracted MDPs (“winners”) with those in counties that narrowly missed out on these MDPs (“runners-up”). The arrival of an MDP in a “winner” county shifts research of local scientists toward topics relevant to the firm, but not at the expense of either the quantity or quality of their work. This shift in research direction is not primarily driven by direct funding or collaboration. Instead, it occurs immediately after the announcement but before the physical establishment of these plants and is more likely to affect scientists without prior experience in commercialization. These findings indicate that scientists are refocusing their attention toward more applied and firm-relevant research.

Contact person: Elisabeth Hofmeister

Subscription to the invitation mailing list and more information on the seminar page.

Causal machine learning (Causal ML) is an emerging branch in ML/AI research aimed data-driven decision-making by integrating robust causal inference with advanced predictive algorithms. A key advantage of Causal ML is the ability to prediction under intervention, that is, to predict the outcomes of a treatment at the individualized level while adjusting for various confounders. Causal ML can explicitly model how the treatment impact varies across subpopulations, thus uncovering rich, individual-level heterogeneity that can be leveraged for personalized targeting and more effective decisions. In this talk, we explore the methodological foundations of Causal ML, discuss critical guardrails necessary for its rigorous and responsible deployment, and explore applications in behavioral science and policy. In particular, we introduce the “AI Heterogeneity Explorer”, which allows to uncover the differential effectiveness of behavioral interventions and thus identify for whom interventions are effective. The “AI Heterogeneity Explorer” provides a systematic recipe for understanding the heterogeneity of behavioral interventions, optimizing the personalized delivery of interventions, validating the targeting strategy—which offers a powerful alternative to one-size-fits-all approaches often used in data-driven decision-making. Finally, we illustrate how this explorer can be leveraged in the context of climate interventions to advance behavioral and climate science.

Contact person: Malte Toetzke

Subscription to the invitation mailing list and more information on the seminar page.

On 15/16 December 2025, the Max Planck Institute for Innovation and Competition will host the 8^th Research on Innovation, Science and Entrepreneurship Workshop (RISE8), an annual workshop for Ph.D. students and Junior Postdocs in Economics and Management. 

The goal of the RISE8 Workshop is to stimulate an in-depth discussion of a select number of empirical research papers. It offers Ph.D. students and Junior Postdocs an opportunity to present their work and to receive feedback.

Keynote speaker of the RISE8 Workshop is Matt Marx (Cornell University)

Get the Call for Papers RISE8.

For more information see RISE Workshop.