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Envisioning the Improbable : = Distributional Knowledge and Judgment In Heavy-Tailed Contexts.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Envisioning the Improbable :/
其他題名:	Distributional Knowledge and Judgment In Heavy-Tailed Contexts.
作者:	Weston, Shellwyn L.
面頁冊數:	1 online resource (87 pages)
附註:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: A.
Contained By:	Dissertation Abstracts International75-01A(E).
標題:	Management. -
電子資源:	click for full text (PQDT)
ISBN:	9781303477737

Envisioning the Improbable : = Distributional Knowledge and Judgment In Heavy-Tailed Contexts.
Weston, Shellwyn L.

Envisioning the Improbable :Distributional Knowledge and Judgment In Heavy-Tailed Contexts. - 1 online resource (87 pages)

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

Heavy-tailed distributions often characterize contexts of great importance to managers (e.g. branded product sales, asset prices, and environmental phenomena) in which lowprobability/high-consequence events occur relatively frequently. Thus, if these contexts are mistakenly characterized as thin-tailed (i.e. contexts where extreme events are exceedingly rare rather than merely unusual), managers may undervalue or dismiss potential blockbuster opportunities, sell assets too cheaply, or fail to plan adequately for catastrophic events. This dissertation highlights the issue that because heavy-tailed phenomena exhibit a much greater than "normal" frequency and size of outliers (probability mass in the tails), they have a much greater than "normal" number of events (probability mass) clustered near the mean. That is, heavy-tailed phenomena often exhibit samples, and sample paths, that appear thin-tailed, owing to the absence of outliers, for large samples or for long periods. This research centers on the question: What judgments do individuals make regarding possible unusual (lowprobability/high-consequence) events in heavy-tailed contexts in the absence of representative experience? The first two experiments demonstrate that individuals overwhelmingly fail to distinguish between heavy- and thin-tailed contexts in the absence of experience. The work then introduces a typology and model of distributional knowledge and the third experiment confirms, using text analysis of individual reasoning statements, that contextual knowledge (the understanding that sample data may be misleading, precipitating a search for analogous contexts, broad categorizations, a generative mechanism, or more data) moderates the biased judgments found in Experiments 1 and 2. The fourth experiment develops and tests a new method, based on financial options theory (Black and Scholes, 1975), of eliciting confidence appropriate for heavy-tailed contexts. Counterintuitively, in the final experiment, individuals with knowledge of at least one distribution in addition to the normal distribution, demonstrate a directionally worse ability to distinguish between heavy- and thin-tailed contexts. This is the first work to develop a typology of distributional knowledge, to model and test individual ability to distinguish between heavy-and thin-tailed contexts, and to employ skewed payoff structures to assess perceptions of tail risk.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303477737Subjects--Topical Terms:

558618
Management.
Index Terms--Genre/Form:

554714
Electronic books.

Envisioning the Improbable : = Distributional Knowledge and Judgment In Heavy-Tailed Contexts.
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