Veranstaltungen

The idea that inventions are solutions to specific technical problems is foundational to innovation research—yet, so far, there is no systematic way of measuring which problem an invention addresses. The problem that a battery overheats, for instance, may be solved through inventions in electrochemistry, materials science, or mechanical engineering. In existing patent data, these solutions would appear unrelated, because the data are organized around the technology employed, not the problem addressed. This missing problem dimension means we cannot observe which technical problems attract competing solutions from different fields, how broadly firms search across alternative solution paths, or how inventive labor is divided between those who recognize problems and those who develop solutions. This project constructs a new dataset that makes the link between inventions and the problems they address visible at scale.

Ansprechpartnerin: Elisabeth Hofmeister

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

We examine how artificial intelligence impacts three core pillars of collaboration—performance enhancement, expertise integration, and social engagement—through a pre-registered field experiment with 791 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings show that (1) AI significantly enhances performance: individuals with AI matched the performance of teams without AI, suggesting that AI can effectively replicate certain benefits of human collaboration. Moreover, (2) AI helps bridge functional silos: without AI, R&D professionals tended to suggest more technical solutions, while Commercial professionals leaned toward commercially-oriented proposals. Professionals using AI produced more balanced solutions, regardless of their professional background. (3) AI’s language-based interface prompted more positive self-reported emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. Finally, decomposing the innovation process suggests that AI primarily enhances the quality of generated ideas, shifting the distribution of creative output upward, while human judgment retains value in evaluative selection. This finding highlights the multiple and complementary roles that human and AI partners can play in new product development tasks and creative problem solving. More generally, our results suggest that AI adoption in knowledge work affects not only performance but also how expertise and sociality appear within teams, offering insights into the impact of GenAI on collaborative work within organizations.

Ansprechpartnerin: Marina Chugunova

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

The Munich Center for Transformative Technologies and Societal Change (TransforM) invites you to our first installment of the TranforM Futures public event series.

Let's talk about MOONSHOT INNOVATIONS! This event will feature a panel discussion with experts at the nexus of academia and industry with a focus on when, why and how moonshot innovations emerge, scale-up and effect real societal change. Following the discussion, there will be a chance for audience interaction and networking.

Registration

Mehr Information auf der SIPLA Webseite

The workshop will cover the MSI’s three focus areas:

• Digitalization, Strategy and Organization
• Innovation and Entrepreneurship
• Law & Economics of Intellectual Property, Innovation & Digitalization

Like the Munich Summer Institute, the MSI Ph.D. Workshop will focus on quantitative empirical research. In the workshop, participants will present their working papers, receive comments from senior scholars, and discuss their papers with other participants. The number of participants is limited. Discussants will be senior scholars who participate in the Munich Summer Institute’s main conference.

Program

Das Munich Summer Institute (MSI) wird veranstaltet von Center for Law & Economics der ETH Zürich, der Universität Lausanne, der Cornell University, dem Imperial College, dem Lehrstuhl für Technologie- und Innovationsmanagement der TUM, dem Lehrstuhl für Innovationsökonomie der TUM, dem Institut für Strategie, Technologie und Organisation (ISTO) der LMU München und dem Max-Planck-Institut für Innovation und Wettbewerb.

Weitere Informationen auf der Webseite des MSI

This paper studies the relationship between climate policy, market power and innovation. Using data on patenting and firms' balance sheets, I document four stylized facts. Most importantly, I find that firms with a higher degree of market power are, on average, more invested in dirty technologies than their direct competitors. Motivated by the empirical evidence, I develop a model of directed technical change with strategic innovation incentives. A carbon tax affects market power within industries due to technology lock-in and firm heterogeneity. Strategic incentives lead some firms to respond to climate policy by increasing their dirty innovation investments. In the calibrated model, a carbon tax lowers aggregate markups and increases clean innovation along the green transition, highlighting the importance of firms' strategic decisions for climate policy.

Ansprechpartner: Fernando Loaiza

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

This paper studies how a major advance in artificial intelligence reshapes the production of scientific knowledge. I exploit the public release of AlphaFold2—a deep-learning system that predicts protein structures with near-experimental accuracy—as a sharp and field-wide reduction in the cost of structural information in structural biology. I construct a new protein-level dataset linking the universe of proteins to their experimental structural characterization, AlphaFold2 coverage, and the complete corpus of associated scientific publications. Following the release, proteins receiving larger informational shocks experience substantial increases in scientific activity: the probability of publication rises by 60–80 percent, and publication counts more than double relative to pre-release trends. These gains are not uniform. Publication responses are strongest for proteins that had partial, but incomplete, experimental structural information prior to AlphaFold2, indicating increasing returns to existing scientific capital. Rather than displacing experimentation, AlphaFold2 complements wet-lab research: experimental validation activity increases for proteins with high predicted coverage. The composition and organization of research teams also shift. Projects on high-coverage proteins involve more specialized and computationally oriented contributors, and resulting publications engage more intensively with structural and computational questions while maintaining experimental inquiry.

Ansprechpartner: Michael Rose

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Zum Programm 

Zu REGIS: Reading Group in Innovation Studies