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Friedman, Jeffrey Allan

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Jeffrey Allan

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Friedman, Jeffrey Allan

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2012 - 20132

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Author's Publications: search.filters.isAuthorOfPublication.1fe81084-b1b2-46d9-8e67-c0e84602e6df

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    Cumulative Dynamics and Strategic Assessment: U.S. Military Decision Making in Iraq, Vietnam, and the American Indian Wars
    (2013-09-18) Friedman, Jeffrey Allan; Walt, Stephen Martin; Bates, Robert; Toft, Monica; Zeckhauser, Richard
    This dissertation examines why military decision makers struggle to evaluate their policies and why they often stick to unsuccessful strategies for so long. The core argument is that strategic assessment involves genuine analytic challenges which contemporary scholarship typically does not take into account. Prominent theoretical frameworks predict that the longer decision makers go without achieving their objectives, the more pessimistic they should become about their ability to do so, and the more likely they should be to change course. This dissertation challenges those ideas and explains why we should often expect the very opposite. The theoretical crux of this argument is that standard models of learning and adaptation (along with many people’s basic intuitions) revolve around the assumption that decision makers are observing repeated processes, similar to the dynamics of slot machines and roulette wheels – but in war and other contexts, decision makers often confront cumulative processes that have very different dynamics, along with a different logic for how rational actors should form and revise their expectations. Empirically, this dissertation examines U.S. decision making in Iraq, Vietnam, and the American Indian Wars. These cases demonstrate how cumulative dynamics affect strategic assessment and how understanding these dynamics can shed light on prominent theoretical frameworks, ongoing policy debates, and salient historical experience.
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    Assessing Uncertainty in Intelligence
    (John F. Kennedy School of Government, Harvard University, 2012) Friedman, Jeffrey Allan; Zeckhauser, Richard
    This article addresses the challenge of managing uncertainty when producing estimative intelligence. Much of the theory and practice of estimative intelligence aims to eliminate or reduce uncertainty, but this is often impossible or infeasible. This article instead argues that the goal of estimative intelligence should be to assess uncertainty. By drawing on a body of nearly 400 declassified National Intelligence Estimates as well as prominent texts on analytic tradecraft, this article argues that current tradecraft methods attempt to eliminate uncertainty in ways that can impede the accuracy, clarity, and utility of estimative intelligence. By contrast, a focus on assessing uncertainty suggests solutions to these problems and provides a promising analytic framework for thinking about estimative intelligence in general.