• describe and explain a set of core theoretical insights from research on how strategists can evaluate ideas to cope with big challenges
• apply these insights successfully in entrepreneurial and corporate settings

Today's big challenges, such as climate change, inflation, or new political instabilities, not just matter for political decision-making, but also for decision-making in start-up and corporate contexts. For instance, which start-up ideas create successful, and swift responses to the current energy crisis? And which ones will create lasting value, beyond the current crisis? How can this be evaluated? Moreover, how can corporates develop sound foresight about business models that will newly emerge, for instance, against the backdrop of climate change?
Addressing these questions requires coping with radical uncertainty, and in particular, the skill to re-learn when environments changed profoundly in little time. This form of uncertainty is different from uncertainty (and complexity in decision-making) per se, as contingencies are often unknowable. Courses of action that appear rational today, but that may be rendered obsolete tomorrow: Re-learning is key.
The unique contribution of this elective is to offer rigorous insights from cutting-edge research into successful strategies for coping with radical uncertainty, combined with case-based learning to make the insights vivid.
From a theoretical perspective, this course offers insights into the state-of-the-art of research into the psychology of radical uncertainty and the theory-based learning approach. Theory-based learning is a systematic and normative approach to evaluating big ideas that are postulated to solve big challenges. It rests on asking what must be true to make a big idea true, that is, on formulating a theory, experimenting with subproblems, and learning from feedback on subproblems along with arguments against the theory. The specialty of the approach is that it enables re-learning: In case of failure with subproblems, theories do not have to be discarded in toto, but can be revised using normative rules. That also differentiates theory-based learning from scenario planning and related approaches.
Theory-based learning offers the backbone for a study of concrete cases (some of which will be “real-time” cases, of which the outcome is not known at the time of the elective). Thus, the course contains a significant experiential element where students can learn via cases that may affect their actual decision-making in 2023 and 2024. These hands-on experiences will then be reflected through academic literature and in-class case discussion. The final exam combines these theoretical and case elements.
 
The course will emphasize open discussions and will use a mixture of theory and case studies to engage students in the classroom. To have an optimal mix between theory and practice, 11 lectures of each 2 hours will be accompanied by 9 case presentation sessions of 1 hour each. Topics and readings for each session are published as course outlines via Canvas.

Ehrig, T. & Schmidt, J. (2022) Theory-based learning and experimentation: How strategists can systematically generate knowledge at the edge between the known and the unknown. Strategic Management Journal, https:/​/​doi.org/​10.1002/​smj.3381

Felin, T., & Zenger, T. R. (2009). Entrepreneurs as theorists: on the origins of collective beliefs and novel strategies. Strategic Entrepreneurship Journal, 3(2), 127-146.

Felin, T., & Zenger, T. R. (2017). The theory-based view: Economic actors as theorists. Strategy Science, 2(4), 258-271. 

Zellweger, T. & Zenger, T. (2021) Entrepreneurs as scientists: A pragmatist approach to producing value out of uncertainty, Academy ofManagement,  https:/​/​doi.org/​10.5465/​amr.2020.0503