Ignorance: Lessons from the Laboratory of Literature
Devjani Roy and
Richard Zeckhauser ()
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Devjani Roy: Harvard University
Working Paper Series from Harvard University, John F. Kennedy School of Government
Traditional decision theory distinguishes between risk and uncertainty. With risk, the probabilities of possible outcomes are known; with uncertainty, those outcomes are known, but not their probabilities. We introduce the concept of ignorance, a third, less tractable category. With ignorance, even the possible outcomes cannot be identified. Ignorance takes importance when high payoffs are associated with the unidentified outcomes. Thus we focus on consequential amazing developments, or CADs. CADs spring upon societies as well as individuals. In the policy realm, the 2008 financial meltdown and the Arab Spring would represent CADs, major unanticipated events. For an individual, a CAD might be the discovery that a faithful spouse of many years has a secret second family, or that our trusted business partner has been pilfering corporate secrets all along. Authors depict the implications of consequential ignorance in some of the greatest of literary works: Hamlet's ignorance of his father's killer, Macbeth's unawareness of outcomes when he attempts to seize the Scottish crown, Odysseus's journey back to Ithaca involving a series of consequential adventures, all unknowable. Consequential ignorance cannot be studied in a controlled laboratory setting, since its payoffs are high, its time delays often long, and merely introducing the subject tends to give away the game. Thus we study ignorance through great works of literature, from antiquity to the present day, positing that great writers understand how humans make decisions. We distinguish between unrecognized and recognized ignorance. In the latter category, we identify specific cognitive biases at work. We provide a formula for calculating consequential ignorance that incorporates the expected magnitudes and assessed base rates for CADs. Finally, we propose steps towards measured decision making under ignorance.
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