Veranstaltungen

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

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 use quasi-random local variation in the number of young men who died as a result of World War I to estimate the impact of this demographic shock on innovation and structural change in France. Our analysis shows that excess mortality led to an increase in patenting activity in counties with high pre-war education levels, driven predominantly by innovations in labor-saving technologies. Our estimates imply that an additional 6,000 patents were filed in the 15 years following the war, amounting roughly to the average annual number of patents filed pre-war.We find a positive association between war-related mortality and wage growth as well as with the adoption of machines in the agricultural sector, providing additional evidence that incentives to escape labor scarcity are driving the innovation response to mortality.

Ansprechpartner: Dominik Asam

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

We develop a method to estimate producers’ productivity beliefs in settings where output quantities and input prices are unobservable, and we use it to evaluate allocative efficiency in the market for science. Our model of researchers’ labor supply shows that their willingness to pay for their two key inputs, funding and time, reveals their underlying productivity beliefs. We estimate the model’s parameters using data from a nationally representative survey of research-active professors from all major fields of science. We find that the distribution of research productivity is highly skewed. Using these estimates, we assess the market’s allocative efficiency by comparing actual input allocations to optimal allocations given various objectives. Overall, the market for science is moderately efficient at maximizing output and researchers’ utility: actual input levels are positively correlated with the optimal levels implied by the model. However, the wedge between researchers’ actual and optimal input levels is often significant and difficult to predict. Our estimates imply that total budgets would need to increase by roughly 40% under actual allocations in order to achieve the same growth in scientific output that we predict under alternative allocations of the current budget. Scaling to the population level, this equates to billions of dollars in funding — there are substantial gains from developing new ways of identifying and supporting talented scientists.

Ansprechpartner: Jordan Bisset 

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Modern societies rely on synthetic chemicals for progress, yet many of these substances pose serious risks to human health and the environment. The SUSTECH project tackles this paradox by examining how hazardous compounds emerge, persist, and succeed in markets – and how innovation pathways might be redirected toward safer alternatives. Combining computational chemistry and machine learning with the economics of innovation and patent analysis, SUSTECH develops tools to forecast hazard profiles and uncover the incentives shaping chemical innovation. In this seminar, I will outline SUSTECH’s broader research agenda and reflect on our interdisciplinary collaboration, intellectual convergence, and strategic decisions that led to the award of an ERC Synergy Grant. The talk offers both a scientific perspective on steering chemical innovation toward sustainability and a candid account of building a successful high-risk, high-reward research consortium.

Ansprechpartner: Elisabeth Hofmeister

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

VRi’inLG Dr. Schwager, Ri’inLG Mattes, Ri’inLG Hahn (Landgericht München I, 42.)
und
Prof. Dr. Franz Hofmann, LL.M. (Cambridge) (Universität Erlangen)

Einladung als pdf

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.

Ansprechpartner: Malte Toetzke

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Venture capitalists, business angels, funding agencies, and incubators evaluate ventures, a difficult task where decision uncertainty is high. We examine how the degree of cognitive uncertainty affects judges’ admission recommendations at an incubator. Judges read an application, use preset criteria to score it, and form an intuitive overall judgment to accept or reject the application. We model how cognitive uncertainty affects this judgment through a Bayesian classification model. We test how judge expertise and venture complexity affect classification accuracy and cognitive uncertainty, the key mechanism of the model that produces different judgments. Judges demonstrate moderate accuracy in evaluating venture quality, performing better than random but with substantial room for improvement. Bayesian models of judgment capture much of the decision process but struggle to fully explain judgments that are less clear-cut. Complexity raises uncertainty and lowers classification accuracy, while expertise reduces uncertainty and improves accuracy; the expertise premium is largest at intermediate complexity levels.

Ansprechpartner: Daehyun Kim

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

PR-Netzwerktreffen der Kommunikator*innen der Max-Planck-Institute der Region München zum Austausch über den A-HA Science Communication Hub.

Cancer is a leading cause of death in developed countries, and cancer treatments are the top category of pharmaceutical spending in the United States and Europe. This paper assesses (1) whether the use of novel cancer therapies are associated with a reduction in mortality, and (2) the cost per statistical life year saved. Using panel data from 28 countries, we study the relationship between mortality attributed to a specific cancer site and the use of pharmaceutical treatments approved to treat that site. The cross-country and cross-site variation over time allows us to isolate the decline in mortality attributable to new drugs from that due to changes in lifestyle and environmental factors, and we distinguish between the effects of treatments based on their evaluated therapeutic benefits by an important health technology assessor. We correct for the endogeneity of mortality and the availability of new treatments using instrumental variables. On average, our results show a decline in mortality associated with the use of innovative treatments for a cancer site. The gains vary across countries and cancer sites. (Joint work with Pierre Dubois)

Ansprechpartnerin: Elisabeth Hofmeister

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.