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Improving Choice by Automatically Restructuring Decision Environments
Improving Choice by Automatically Restructuring Decision Environments
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151120
- ISBN
- 9798382807768
- DDC
- 153
- 저자명
- Hardy, Mathew D.
- 서명/저자
- Improving Choice by Automatically Restructuring Decision Environments
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 137 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Griffiths, Thomas L.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interactions and cognitive offloading is one of the most distinctive features of human intelligence. This dissertation explores ways of improving choice and augmenting cognition by automatically restructuring people's decision environments and social networks. This approach uses psychological models developed by researchers as engineering tools, and allows individuals to benefit from increasingly powerful artificial systems. In a series of studies, we show how this approach can lead people to better decisions and reduce harmful side effects of traditional "static" offloading. Crucially, this approach can also give individuals greater autonomy and control over how their decisions are guided and shaped. Chapter 2 introduces a novel formal framework for modeling and evaluating the effects of "nudges" based on the insights that nudges change the problem of how to make a decision without changing the decision itself. We then show how this model can be used to optimize choice environments and automatically construct optimal nudges that best improve choice. Chapter 3 shows how Bayesian and psychometric modeling can be used to develop a new model of group decision-making in settings with repeated population turnover. We then show that this model can be used to automatically restructure people's networks so that people benefit from social observation without it increasing their bias. Chapter 4 shows how restructuring environments can be extended by using modern text-to-image AI models to help people better imagine alternative futures. Crucially, we show that this approach can be used to increase support for real-world policies and proposals. Chapter 5 concludes by discussing the broader implications of this work, limitations of the studies and models discussed here, and opportunities for future work.
- 일반주제명
- Cognitive psychology
- 일반주제명
- Behavioral psychology
- 일반주제명
- Computer science
- 일반주제명
- Psychology
- 키워드
- Machine learning
- 키워드
- Nudging
- 기타저자
- Princeton University Psychology
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382807768
■035 ▼a(MiAaPQ)AAI31146221
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a153
■1001 ▼aHardy, Mathew D.
■24510▼aImproving Choice by Automatically Restructuring Decision Environments
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a137 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Griffiths, Thomas L.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aMany of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interactions and cognitive offloading is one of the most distinctive features of human intelligence. This dissertation explores ways of improving choice and augmenting cognition by automatically restructuring people's decision environments and social networks. This approach uses psychological models developed by researchers as engineering tools, and allows individuals to benefit from increasingly powerful artificial systems. In a series of studies, we show how this approach can lead people to better decisions and reduce harmful side effects of traditional "static" offloading. Crucially, this approach can also give individuals greater autonomy and control over how their decisions are guided and shaped. Chapter 2 introduces a novel formal framework for modeling and evaluating the effects of "nudges" based on the insights that nudges change the problem of how to make a decision without changing the decision itself. We then show how this model can be used to optimize choice environments and automatically construct optimal nudges that best improve choice. Chapter 3 shows how Bayesian and psychometric modeling can be used to develop a new model of group decision-making in settings with repeated population turnover. We then show that this model can be used to automatically restructure people's networks so that people benefit from social observation without it increasing their bias. Chapter 4 shows how restructuring environments can be extended by using modern text-to-image AI models to help people better imagine alternative futures. Crucially, we show that this approach can be used to increase support for real-world policies and proposals. Chapter 5 concludes by discussing the broader implications of this work, limitations of the studies and models discussed here, and opportunities for future work.
■590 ▼aSchool code: 0181.
■650 4▼aCognitive psychology
■650 4▼aBehavioral psychology
■650 4▼aComputer science
■650 4▼aPsychology
■653 ▼aBayesian modeling
■653 ▼aCognitive science
■653 ▼aMachine learning
■653 ▼aNudging
■690 ▼a0633
■690 ▼a0384
■690 ▼a0984
■690 ▼a0621
■71020▼aPrinceton University▼bPsychology.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160811▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
