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Algorithmic Bayesian Epistemology
Algorithmic Bayesian Epistemology
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151000
- ISBN
- 9798382263021
- DDC
- 004
- 저자명
- Neyman, Eric.
- 서명/저자
- Algorithmic Bayesian Epistemology
- 발행사항
- [Sl] : Columbia University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 386 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Roughgarden, Tim.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2024.
- 초록/해제
- 요약One aspect of the algorithmic lens in theoretical computer science is a view on other scientific disciplines that focuses on satisfactory solutions that adhere to real-world constraints, as opposed to solutions that would be optimal ignoring such constraints. The algorithmic lens has provided a unique and important perspective on many academic fields, including molecular biology, ecology, neuroscience, quantum physics, economics, and social science.This thesis applies the algorithmic lens to Bayesian epistemology. Traditional Bayesian epistemology provides a comprehensive framework for how an individual's beliefs should evolve upon receiving new information. However, these methods typically assume an exhaustive model of such information, including the correlation structure between different pieces of evidence. In reality, individuals might lack such an exhaustive model, while still needing to form beliefs. Beyond such informational constraints, an individual may be bounded by limited computation, or by limited communication with agents that have access to information, or by the strategic behavior of such agents. Even when these restrictions prevent the formation of a perfectly accurate belief, arriving at a reasonably accurate belief remains crucial. In this thesis, we establish fundamental possibility and impossibility results about belief formation under a variety of restrictions, and lay the groundwork for further exploration.
- 일반주제명
- Computer science
- 일반주제명
- Statistics
- 일반주제명
- Applied mathematics
- 키워드
- Online learning
- 키워드
- Scoring rules
- 기타저자
- Columbia University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151000
■006m o d
■007cr#unu||||||||
■020 ▼a9798382263021
■035 ▼a(MiAaPQ)AAI30993857
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aNeyman, Eric.
■24510▼aAlgorithmic Bayesian Epistemology
■260 ▼a[Sl]▼bColumbia University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a386 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Roughgarden, Tim.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2024.
■520 ▼aOne aspect of the algorithmic lens in theoretical computer science is a view on other scientific disciplines that focuses on satisfactory solutions that adhere to real-world constraints, as opposed to solutions that would be optimal ignoring such constraints. The algorithmic lens has provided a unique and important perspective on many academic fields, including molecular biology, ecology, neuroscience, quantum physics, economics, and social science.This thesis applies the algorithmic lens to Bayesian epistemology. Traditional Bayesian epistemology provides a comprehensive framework for how an individual's beliefs should evolve upon receiving new information. However, these methods typically assume an exhaustive model of such information, including the correlation structure between different pieces of evidence. In reality, individuals might lack such an exhaustive model, while still needing to form beliefs. Beyond such informational constraints, an individual may be bounded by limited computation, or by limited communication with agents that have access to information, or by the strategic behavior of such agents. Even when these restrictions prevent the formation of a perfectly accurate belief, arriving at a reasonably accurate belief remains crucial. In this thesis, we establish fundamental possibility and impossibility results about belief formation under a variety of restrictions, and lay the groundwork for further exploration.
■590 ▼aSchool code: 0054.
■650 4▼aComputer science
■650 4▼aStatistics
■650 4▼aApplied mathematics
■653 ▼aBayesian epistemology
■653 ▼aForecast aggregation
■653 ▼aForecast elicitation
■653 ▼aInformation structures
■653 ▼aOnline learning
■653 ▼aScoring rules
■690 ▼a0984
■690 ▼a0796
■690 ▼a0463
■690 ▼a0364
■71020▼aColumbia University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160338▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
