Events

Open Source AI: Strategic Motives for the Selective Revealing of AI System Components
Presenter: Leonard Hanschur (TUM)
Discussant: Ulrike Morgalla (MPI)

The recent open revealing of numerous AI systems challenges the notion that the exclusivity of an AI system’s data and model constitutes a source of competitive advantage. We explore the mechanisms behind revealing AI and the characteristics of AI systems associated with it. Specifically, we examine two dimensions of the selective revealing of AI systems: Its completeness, describing which components are revealed (none, the model, or model and data), and its degree determined by the license type (proprietary, restrictive, permissive). Employing a mixed-methods approach, we draw on 24 interviews with decision-makers at AI-focused organizations and prior theory to construct hypotheses that we empirically test on a sample of 659 AI systems. We hypothesize, and find supported in the data, that organizations tend to reveal larger and more innovative models to a lesser degree and less completely. Further, we find that data modality shapes revealing completeness, and that model size moderates this association. These findings suggest, in line with our qualitative findings, that revealing AI system components serves to promote their adoption and to establish a lock-in across AI system versions rather than collaborative development. Our study contributes to the academic discourse on open innovation and competitive advantage. For strategists and policymakers, we provide guidance in navigating their pathways toward opening AI.

Strategic Reserves: Shelved Innovation as a Real Option
Presenter: Elisabeth Hofmeister (MPI)
Discussant: Denzel Glandel (ISTO)

I investigate the role of shelved innovation - R&D projects suspended despite promising results - in firms’ strategy, drawing on evidence from the pharmaceutical industry. Initially, I construct a novel dataset linking clinical trials to their published results, enabling the systematic identification of shelved drug development projects. Using the exogenous nature of trial failures, I evaluate whether firms restart shelved projects following project failures in the same market. The results show that firms restart shelved projects in response to late-stage failures in Phase III clinical trials. Further, I find that the decision to restart is moderated by the thickness of the market for technology and the firm’s level of co-specialized complementary assets. Overall, these findings demonstrate that shelved innovation is not merely an incidental byproduct of the R&D process but a strategically managed asset.

Selective Promotion of Complements on Online Auction Platforms: Evidence from the Automotive Industry
Presenter: Alexey Rusakov (ISTO)
Discussant: Tim Hahne (TUM)

Platforms can steer demand by selectively promoting complements in platform markets. But how does selective promotion affect the overall demand when the products are idiosyncratic, such as on auction platforms, and does this effect differ for similar products and a competing platform? By studying a car auction platform with unique cars, we find that promotional car reviews on YouTube positively affect the prices and bid volumes of reviewed cars in the same category. However, the latter effect is largely due to short-term attention spillovers, while the sentiment of the reviews can have unexpected consequences for the bid prices. Moreover, the effect on the competing platform is rather limited and probably only occurs when users first visit the focal platform and then switch to the competing platform.

Contact person: Elisabeth Hofmeister

Open source is key to innovation yet is assumed to be done largely through intrinsic motivation. How can we incentivize it? In this paper, we examine the impact of a program providing monetary incentives to motivate innovators to contribute to open source. The Sponsors program was introduced by GitHub in May 2019 and enabled organizations and individuals alike to pay developers for their open source work. We study this program by collecting fine-grained data on nearly 100,000 GitHub users, their activities, and sponsorship events. We first, using a difference-in-differences approach, document two main effects. One, developers who opted into the program, an action that does not itself entail a financial reward, increased their output after the program’s launch. Two, the actual receipt of a financial sponsorship has a long-lasting negative effect on two measures of innovation –repository creation and community-oriented tasks– but not in coding effort. Despite a net positive effect on innovation, sponsorship appears to crowd out intrinsic motivation, shifting effort toward self-promoting activities. Results from a pre-registered survey and experiment reinforce these findings, showing that modest sponsorship (USD 20) deters collaborative contributions compared to no compensation, larger rewards (USD 1000), or company sponsorships.

Contact person: Daehyun Kim

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

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

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.

Title and abstract will follow.

Contact person: Elisabeth Hofmeister

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.