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The Psychology of Belief Distributions: Manipulating and Measuring Consumer Uncertainty
The Psychology of Belief Distributions: Manipulating and Measuring Consumer Uncertainty
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151052
- ISBN
- 9798382830155
- DDC
- 150
- 저자명
- Hu, Beidi.
- 서명/저자
- The Psychology of Belief Distributions: Manipulating and Measuring Consumer Uncertainty
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 163 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Simmons, Joseph P.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약This dissertation explores consumers' judgments and decisions under uncertainty through the lens of belief distributions. Asking people to consider all possible outcomes and indicate their likelihoods - a practice referred to as "constructing a belief distribution" - has been on the rise in disciplines including marketing, management, psychology, and economics, finding its applications in diverse research topics and professional forecasting. It has been used as an elicitation method to measure people's beliefs over all possibilities of uncertain outcomes and has been proposed as a light-touch intervention to reduce people's overconfidence. Each chapter in this dissertation investigates a different aspect of this practice. Chapter 1 examines the effectiveness of belief distributions as an overconfidence intervention. Across different prediction domains, we find that constructing a belief distribution actually increases people's overconfidence. This is because the process of allocating probabilities to different outcomes is infused with confirmatory reasoning: People tend to allocate probabilities in a way that reinforces, rather than calls into question, their prior beliefs. Chapter 2 turns to an important question in using belief distributions as a measure: Do people construct the same belief distributions regardless of how they are elicited? We find that two functionally similar methods - Distribution Builder and Sliders, both eliciting people's belief distributions in a graphical way - lead to different results. In particular, the Distribution Builder consistently elicits more accurate responses than the Sliders, in part because those using Sliders tend to start from the first category and end up allocating excessive mass to the starting categories. Chapter 3 applies belief distributions to investigating the communication of uncertainty in time estimates. Across different domains, time durations, and underlying distributions, we find that time estimates presented as ranges increase consumer satisfaction relative to those presented as point estimates. This is in part because range estimates widen people's anticipated distributions of outcomes and thus expand the interval in which outcomes feel consistent with people's expectations relative to a counterfactual in which a point estimate has been provided. Together these three investigations shed light on the study of belief distributions, biases in judgments, and consumer decisions under uncertainty.
- 일반주제명
- Psychology
- 일반주제명
- Behavioral sciences
- 키워드
- Overconfidence
- 키워드
- Time perception
- 키워드
- Uncertainty
- 기타저자
- University of Pennsylvania Operations Information and Decisions
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017160632
■00520250211151052
■006m o d
■007cr#unu||||||||
■020 ▼a9798382830155
■035 ▼a(MiAaPQ)AAI31141574
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a150
■1001 ▼aHu, Beidi.
■24510▼aThe Psychology of Belief Distributions: Manipulating and Measuring Consumer Uncertainty
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a163 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Simmons, Joseph P.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aThis dissertation explores consumers' judgments and decisions under uncertainty through the lens of belief distributions. Asking people to consider all possible outcomes and indicate their likelihoods - a practice referred to as "constructing a belief distribution" - has been on the rise in disciplines including marketing, management, psychology, and economics, finding its applications in diverse research topics and professional forecasting. It has been used as an elicitation method to measure people's beliefs over all possibilities of uncertain outcomes and has been proposed as a light-touch intervention to reduce people's overconfidence. Each chapter in this dissertation investigates a different aspect of this practice. Chapter 1 examines the effectiveness of belief distributions as an overconfidence intervention. Across different prediction domains, we find that constructing a belief distribution actually increases people's overconfidence. This is because the process of allocating probabilities to different outcomes is infused with confirmatory reasoning: People tend to allocate probabilities in a way that reinforces, rather than calls into question, their prior beliefs. Chapter 2 turns to an important question in using belief distributions as a measure: Do people construct the same belief distributions regardless of how they are elicited? We find that two functionally similar methods - Distribution Builder and Sliders, both eliciting people's belief distributions in a graphical way - lead to different results. In particular, the Distribution Builder consistently elicits more accurate responses than the Sliders, in part because those using Sliders tend to start from the first category and end up allocating excessive mass to the starting categories. Chapter 3 applies belief distributions to investigating the communication of uncertainty in time estimates. Across different domains, time durations, and underlying distributions, we find that time estimates presented as ranges increase consumer satisfaction relative to those presented as point estimates. This is in part because range estimates widen people's anticipated distributions of outcomes and thus expand the interval in which outcomes feel consistent with people's expectations relative to a counterfactual in which a point estimate has been provided. Together these three investigations shed light on the study of belief distributions, biases in judgments, and consumer decisions under uncertainty.
■590 ▼aSchool code: 0175.
■650 4▼aPsychology
■650 4▼aBehavioral sciences
■653 ▼aBelief distributions
■653 ▼aConsumer behavior
■653 ▼aOverconfidence
■653 ▼aTime perception
■653 ▼aUncertainty
■690 ▼a0338
■690 ▼a0621
■690 ▼a0602
■71020▼aUniversity of Pennsylvania▼bOperations, Information and Decisions.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160632▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
