Veranstaltungen

Gemeinsame Veranstaltung des Max-Planck-Institut für Innovation und Wettbewerb mit juristischen Fakultät der Universität von São Paulo (FDUSP)

Zeitangaben: Ortszeit São Paulo (GMT-3)

Live-Übertragung auf YouTube

Konferenzprogramm als pdf

This paper analyzes the unexpected success of AlphaFold V1, an AI-based software program representing a publicly available and novel methodological advance within the “protein folding” (PF) subfield of computational biology. As a novel tool that represented a significant advance, AlphaFold V1 influenced the size and the expertise composition of academic research labs already producing similar software in the PF subfield. Specifically, AlphaFold V1 caused the principal investigators of established labs to reconsider the size, and the depth and breadth of expertise necessary to capitalize on the advance; and even to decide whether their labs should continue work in the PF subfield altogether. Results show that impacted labs adapted to the new methodological advance by becoming larger and broader in expertise. This study contributes findings on: (i) the mechanisms of new opportunity and increased competition that shape lab composition; (ii) the complexity of science at the frontier of human expertise and artificial intelligence; and (iii) the role of companies in the collective effort to produce scientific knowledge. The results carry implications for further research on the integration of disciplinary insights, the scaled computational capabilities enabled by artificial intelligence, and the organization of science itself.

Ansprechpartner: Michael E. Rose

Das Max-Planck-Institut für Innovation und Wettbewerb und die Technische Universität München organisieren den jährlichen Workshop “The Organisation, Economics and Policy of Scientific Research”.

Die Teilnahme an der Veranstaltung ist offen und kostenlos: Bitte senden Sie eine E-Mail an michael.rose(at)ip.mpg.de.

Zum Programm

Mehr Informationen auf der Workshop-Webseite

Firms apply for patents for a variety of reasons. In addition to trying to prevent others from copying a commercialized patented invention, firms also patent one technology to support the commercialization of their other innovations (which we call “pre-emptive patents”). At the same time, many patent applications fail to yield commercialized products. A variety of policy debates revolve around the relative rates of these different uses of patents.  In particular, there is a concern that a large number of patents are not commercialized (with a common folk statistic stating that 10% or fewer are commercialized), raising concerns of over-patenting, with possible adverse effects on the innovation system. However, it has been difficult to get systematic evidence on the relative incidences of commercialized versus pre-emptive or failed patents. This paper applies advanced natural language processing (NLP) and machine learning (ML) methods to US patent documents to estimate the rates of patent uses of various types, over time at scale. We first use a survey of US inventors on the commercialization and other outcomes of their patents as independently labeled data for training our ML models. Using a combination of context embedding codings of the patent text (based on BERT) and bibliometric indicators from the patent documents, we develop a random forest model predicting different outcomes for patented inventions. We find that adding BERT coding of patents’ text contents offers new information beyond commonly used numeric and categorical variables reflecting patent characteristics (technology class, number of claims, patent class span, etc.) or token-based text analytics indicators, highlighting the benefits of adding context embedding NLP when categorizing patents. We check the validity of the trained model using external data on Virtual Patent Marking, and show that our model predicts high commercialization rates among patents that are indeed associated with commercialized products. We apply this trained model to the universe of all granted US patents, 1981-2015 to estimate the probabilities of various uses of the patents in this population. These estimates of usage probabilities are publicly available for other researchers to use in follow-on studies. Among US granted patents 1981-2015, the mean probability of commercialization is .59, and the mean probability of pre-emptive use is .19. Finally, we show how these probabilities vary by year, patent class, firm size, and government interest. The paper makes several contributions to understanding the uses of patents, as well as how to use ML to analyze patent data. In particular, we show that estimated rates of commercialized patents are substantially higher than is often asserted in policy discussion.

Ansprechpartnerin: Elisabeth Hofmeister

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

In this talk, Ali Aslan Gümüsay intends to bring together multiple projects around innovative templates, circular forms of organizing, and futures. As new forms of organizing enter a field, they may need to co-create innovative templates to tackle institutional tensions. Equally, they may question and innovate organizational purpose, processes and practices. Given planetary boundaries and infringement on academic jurisdictions, there is a need to not only study these forms of organizing, but also reflect upon the role of scholarship to possibly co-create desirable futures. This includes to think about how to theorize data before it exists – moving from post-factual to pre-factual – and how to (re)organize scholarship itself.

Ansprechpartnerin: Anna-Sophie Liebender-Luc

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Panel 1: The Ukrainian Pharmaceutical Industry: Strategic and Industrial Policy Perspectives
Moderation: Prof. Dietmar Harhoff, Ph.D.

Volodymyr Bortnytskyi, Ph.D., Deputy Head of the Social and Humanitarian Security of the Staff of the National Security and Defense Council of Ukraine, online

Prof. Dr. Liudmyla Petrenko, Department of Business Economics and Entrepreneurship, Kyiv National Economic University named after Vadym Hetman, Ukraine, in person

Panel 2: Drug Research and Development in Ukraine
Moderation: Anastasiia Lutsenko

Prof. Ivan Vyshnyvetskyy, MD, Ph.D., Managing Director Ukraine at FutureMeds, President of the Ukrainian Association for Clinical Research, Associate Professor at National Medical University (Kyiv), online

Prof. Nino Patsuria, Taras Shevchenko National University of Kyiv, online

Panel 3: A Regulatory Framework Outlook for the Ukrainian Pharmaceutical Sector
Moderation: Dr. Daria Kim

Prof. Vitalii Pashkov, Head of the Laboratory for the Study of National Security Problems in the Field of Public Health of the Аcademician Stashis Scientific Research Institute for the Study of Crime Problems, National Academy of Law Sciences of Ukraine, in person

Nataliya Gutorova, Yaroslav Mudryi National Law University, Ukraine, in person

Panel 4: Perspectives on Intellectual Property in the Pharmaceutical Industry in Ukraine
Moderation: Prof. Dr. Dr. h.c. Reto M. Hilty

As. Prof. Oksana Kashyntseva, Ph.D. Law, Head of the Department of IP rights and Human Rights in Healthcare of the SR Institute of Intellectual Property of National Academy of Law Sciences of Ukraine, Head of the NGO ‘Center of Harmonization of Human Rights’, in person

Sergiy Kondratyuk, ITPC Global IP and Access to Medicines Projects Manager, online

Dr. Yevgeniya Piddubna, Corporate Affairs Director, Farmak JSC, Chair of the Healthcare Committee at the Union of Ukrainian Entrepreneurs, in person

Dr. Kseniia Velychko, GR Manager, Pharmaceutical Company ‘Darnitsa’, in person

Zum vollständigen Programm

Business dynamism, i.e., the process of new efficient firms entering the market, grow and force less efficient competitors to contract and exit, is believed to be key both for productivity and employment growth. A particular role in that take young high growth firms with the potential to fundamentally disrupt the economy. Business dynamism and firm high growth, however, are in decline in many advanced countries, most prominently shown for the US, which has important long term effects on the transformation process of the economy. Based on firm-level data, originating from Germany’s largest credit rating agency (Creditreform), covering the period 2002–2019, we show some new insights in firm high growth dynamics. By that, we look at secular trends, heterogeneity in terms of firm size and age as well as sectoral differences.

Taken this as background information, the presentation will proceed by introducing a larger research project that we are currently setting up jointly with IWH (Halle Institute for Economic Research). Here, a unique new data infrastructure, linking the above treated firm-level data with establishment and employee data from IAB will be developed and exploited. This new data infrastructure will allow to respond to several crucial questions in the literature of entrepreneurship, business dynamism, and firm high-growth. More precisely, the research agenda comprises the analysis of who creates jobs in Germany (small vs. big vs. young), as well as the investigation of the conditions (rural arears vs. economic centres, IT infrastructure, closeness to scientific institutions) spurring firm high growth and regional development. Furthermore, we ask the question of the role of entrepreneur characteristics to high growth firm outcomes, such as age, experience, education and migrant status, and the role of inventors and workers. Lastly, the project also comprises the analysis of the effect of M&A activity on firm growth and the innovativeness of the economy.

Ansprechpartner: Albert Roger

Eintragung in den Einladungsverteiler und mehr Informationen auf Seminarseite.

Sophie Quach's work strives to advance our understanding of how users and firms can both benefit from their complementary objectives, roles and resources in joint innovation processes. For firms, adopting, perfecting, producing, and eventually broadly diffusing commercially viable freely revealed user innovations among customers is clearly a great business opportunity. As user innovations originate from different incentives and in different environments than producer innovations, user innovations “generally pioneer functionally new applications and markets prior to producers understanding the opportunity” (von Hippel, 2017). Firms’ commercialization of user innovations is also beneficial from a societal perspective, as it reduces what has been termed the “diffusion shortfall” of user innovations (de Jong et al., 2015, 2018). Many valuable user innovations remain underused because users lack both incentives and resources to produce and popularize them. 

Ansprechpartnerin: Svenja Friess

Social and environmental innovation are important for economic and societal development and to reach the Sustainable Development Goals of the United Nations. However, to date, we lack objective, validated, and non-survey based indicators to measure these two important forms of innovation. To what extent can patent and trademark data be used to construct such measures? Using data from several independent samples, we correlate different survey-based measures of social and environmental innovation with patent and trademark-based measures. Our results show that trademark-based measures can be used to identify social and environmental innovation. The results regarding patent data are mixed.

Ansprechpartner: David Heller

Eintragung in den Einladungsverteiler und mehr Informationen auf Seminarseite.

We analyze how the adoption of the California Consumer Privacy Act (CCPA), which limits the acquisition, processing, and trade of consumer personal data, heterogeneously affects firms with and without previously gathered customer data. Exploiting a novel and hand-collected data set of 11,436 conversational-AI firms with rich personal information on U.S. consumers, we find that the CCPA gives a strong protection and advantage to firms with previously accumulated (in-house) data. First, products of these firms generate more customer feedback and exhibit higher product ratings after the adoption of the CCPA. Second, publicly traded firms with in-house data exhibit higher valuations, profitability, asset utilization, and they invest more after the adoption of the CCPA. Third, earnings of such firms can be more accurately predicted by analysts. To rationalize these empirical findings, we build a general equilibrium model where firms produce intermediate goods using labor and data in the form of intangible capital. Data can be traded with other firms subject to a cost representing regulatory and technical challenges. Firms differ in their ability to collect data internally, driven by their business models and/or the size of their customer base, and reliance on data. When the introduction of the CCPA increases the cost of trading data, firms with a low ability to collect in-house data and high reliance on data suffer the most as they cannot adequately substitute the previously externally purchased data.

Ansprechpartnerin: Svenja Friess

Eintragung in den Einladungsverteiler und mehr Informationen auf Seminarseite.