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    Strategic Foresight as Dynamic Capability: A New Lens on Knightian Uncertainty
    02 Apr 2020Working Paper Summaries

    Strategic Foresight as Dynamic Capability: A New Lens on Knightian Uncertainty

    by J. Peter Scoblic
    Creating strategy under conditions of uncertainty is not easy, so many managers rely on a single analogy to past experience to guide their decisions. This paper argues, by contrast, that imagining multiple possible futures can be a more useful guide to uncertainty, improving judgment and adaptability in the face of change.
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    Author Abstract

    This paper proposes to treat strategic foresight as a dynamic capability, providing a new theoretical lens on managerial judgment. Formulating strategy under uncertainty is a central challenge facing the modern firm. Analogy is thought to help managers make sense of novel situations through comparison to past experience. However, from a Knightian perspective, uncertainty precludes analogy because such situations are unique. Even if one relaxes this conclusion, acknowledging “degrees of uniqueness,” analogy remains an unreliable guide because it triggers biases that encourage the adoption of difficult-to-change hypotheses about the future. Nevertheless, analogical reasoning suggests how managers might better respond to uncertainty. This paper argues that, just as we learn from the past by imagining counterfactuals, so too can we learn from the future by simulating experiences—a process that reduces bias and renders us more perceptive, flexible, and adaptable to environmental change. That is the premise of strategic foresight methods like scenario planning, yet such methods have struggled to find theoretical purchase in the general management literature. Noting that the basis of strategic foresight aligns with the microfoundations of dynamic capabilities, this paper integrates research on individual decision-making with firm-level perspectives to suggest a new theoretical approach to managerial judgment under uncertainty.

    Paper Information

    • Full Working Paper Text
    • Working Paper Publication Date: March 2020
    • HBS Working Paper Number: HBS Working Paper #20-093
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