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Measuring Preferences for Uncertainty.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Measuring Preferences for Uncertainty./
Author:	Mislavsky, Robert Aron.
Description:	1 online resource (96 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Behavioral sciences. -
Online resource:	click for full text (PQDT)
ISBN:	9780438037045

Measuring Preferences for Uncertainty.
Mislavsky, Robert Aron.

Measuring Preferences for Uncertainty. - 1 online resource (96 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2018.

Includes bibliographical references

Understanding decision making under uncertainty is crucial for researchers in the social sciences, policymakers, and anyone trying to make sense of another's (or their own) choices. In this dissertation, my coauthors and I make three contributions to understanding preferences for uncertainty regarding (a) how preferences are measured, (b) how these preferences may (or may not) manifest in a consequential real-world context, and (c) how different types of advice influence opinions about uncertain events. In Chapter 1, we examine methods that researchers use to study preferences for uncertainty. We find that the presence of uncertainty is often confounded with the presence of "weird" transaction features, dramatically overstating the presence of uncertainty aversion in these experiments. In Chapter 2, we show that extreme uncertainty does not exist in the context of corporate experimentation, despite speculation by pundits and researchers. In fact, people judge experiments similarly to how they would judge simple gambles, with the experiment being judged near the "expected value" of the policies it implements. In Chapter 3, we find that the format in which uncertainty is presented impacts how people combine forecasts from multiple sources. Numeric probability forecasts are averaged, while verbal forecasts are combined additively, with people making more extreme judgments as they see additional forecasts.

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

Mode of access: World Wide Web

ISBN: 9780438037045Subjects--Topical Terms:

1148596
Behavioral sciences.
Index Terms--Genre/Form:

554714
Electronic books.

Measuring Preferences for Uncertainty.
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100 1 $a Mislavsky, Robert Aron. $3 1192925
245 1 0 $a Measuring Preferences for Uncertainty.
264 0 $c 2018
300 $a 1 online resource (96 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Uri Simonsohn.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2018.
504 $a Includes bibliographical references
520 $a Understanding decision making under uncertainty is crucial for researchers in the social sciences, policymakers, and anyone trying to make sense of another's (or their own) choices. In this dissertation, my coauthors and I make three contributions to understanding preferences for uncertainty regarding (a) how preferences are measured, (b) how these preferences may (or may not) manifest in a consequential real-world context, and (c) how different types of advice influence opinions about uncertain events. In Chapter 1, we examine methods that researchers use to study preferences for uncertainty. We find that the presence of uncertainty is often confounded with the presence of "weird" transaction features, dramatically overstating the presence of uncertainty aversion in these experiments. In Chapter 2, we show that extreme uncertainty does not exist in the context of corporate experimentation, despite speculation by pundits and researchers. In fact, people judge experiments similarly to how they would judge simple gambles, with the experiment being judged near the "expected value" of the policies it implements. In Chapter 3, we find that the format in which uncertainty is presented impacts how people combine forecasts from multiple sources. Numeric probability forecasts are averaged, while verbal forecasts are combined additively, with people making more extreme judgments as they see additional forecasts.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Behavioral sciences. $3 1148596
650 4 $a Behavioral psychology. $3 1179418
650 4 $a Economics. $3 555568
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0384
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710 2 $a University of Pennsylvania. $b Operations and Information Management. $3 1192926
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10807925 $z click for full text (PQDT)