Sociology

Mathematical economics

Cognitive science

Epistemology

Machine learning

Enhanced Data Rates for GSM Evolution

Philosophy

Artificial intelligence

Psychology

Economics

Computer science

Strategic Management Journal

Research summary We present a framework for theory-based learning and experimentation under uncertainty. Strategists' assumptions about how an envisioned future can be reached are likely incomplete and possibly wrong, for instance, if critical contingencies have been overlooked. We explain how strategists can learn from thinking about and testing necessary conditions for an envisioned future to materialize. By logically linking assumptions to consequences our framework allows drawing inferences from experiments with testable assumptions about elements of a strategy that cannot be tested without major investments. Our framework contains the first formal model of learning from arguments in the strategy literature. By using our framework, strategists can maintain focus on an envisioned future while at the same time systematically seeking out reasons and evidence for why they are wrong. Managerial summary We develop a framework that helps strategists to learn and understand what it takes to reach ambitious goals when there is substantial uncertainty. We ask strategists to formulate their assumptions as a theory: what needs to be true for their goal to materialize. Our framework enables strategists to scrutinize and improve their assumptions by raising objections against their theory and by pointing them to critical experiments to learn whether their assumptions hold. Using our results, strategists can in particular identify overlooked critical contingencies. Overall, we suggest how strategists should revise their beliefs about what it takes to be successful in the light of evidence and arguments for and against their strategy.

Theory‐based learning and experimentation: How strategists can systematically generate knowledge at the edge between the known and the unknown