PhD Seminar Series: Astrid Thomsen and Johan Rath

From the Ivory Tower to Capitol Hill: Which Academics Get a Voice in Congress?

Speaker: Astrid Thomsen (Visiting Student from CBS)

Abstract: Examining academics’ engagement with public policymakers, this paper studies how technological changes affect the composition of expert witnesses, in the context of testimony before the U.S. Congress. Leveraging a COVID-19 policy change that temporarily shifted congressional hearings from in-person to online formats, we show that reducing opportunity costs significantly increased the participation of female academics as expert witnesses. Historically, female economists have seen low representation among congressional witnesses. Following the introduction of online hearings, the probability that a witness is female increased significantly, with the strongest effect observed among geographically remote female scholars. These findings suggest that digital tools promoting flexibility can alleviate disparities in high-impact academic dissemination and, in turn, expand policymakers’ access to high-quality expertise drawn from a more diverse pool of ideas, perspectives, and knowledge.

Abductive Reasoning and Awareness Growth

Speaker: Johan Rath (Bocconi University)

Abstract: Strategic decision making is constrained by what firms can know when they commit resources, yet strategy and entrepreneurship research often treats Knightian “uncertainty” as a single category that conflates two epistemic limits: uncertainty about likelihoods within a known possibility space and unawareness, where relevant outcomes or causal pathways are not yet conceivable. Because Bayesian reasoning presumes an exhaustive state space, it offers a powerful account of learning under uncertainty—belief revision by updating probabilities over fixed hypotheses—but provides no guidance when awareness itself changes. This paper develops a theory of entrepreneurial reasoning under “growing awareness,” in which surprises expose gaps in existing causal models and expand what actors can represent. We argue that learning in such contexts is governed by abductive reasoning: entrepreneurs generate and select plausible new explanations that restore intelligibility when Bayesian updating cannot proceed. The framework distinguishes anomalies that indicate model incompleteness (prompting incremental frame expansion) from those that indicate model invalidity (requiring model abandonment and replacement). Entrepreneurial learning is thus cyclical: Bayesian refinement operates within a stable frame until anomalies trigger a pre-Bayesian abductive phase of model revision or reconstruction, after which probabilistic updating can resume within the newly constituted hypothesis space.