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Algorithmic Decision Making With Imperfect Information and Practical Irrationality
Algorithmic Decision Making With Imperfect Information and Practical Irrationality
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152800
- ISBN
- 9798384463597
- DDC
- 004
- 저자명
- Cai, Linda.
- 서명/저자
- Algorithmic Decision Making With Imperfect Information and Practical Irrationality
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 404 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Weinberg, Matt.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Market algorithms are ubiquitous in modern life, both online and offline. Historically, markets have been fundamental in matching supply with demand, where designers optimize regulations with global goals and participants optimize strategies with individual goals. Decision theory and mechanism design have evolved to study and prescribe the behavior and structure of these markets. This thesis addresses key questions at the intersection of market design and algorithmic decision-making: How do strategic decision-makers navigate choices, and how do resource producers allocate limited resources among strategic participants?The advent of information technology and increased data availability has revolutionized markets, enabling unprecedented scale, efficiency, and control. This development allows mechanism designers to create or modify market conditions, provided we understand their impact on the designer's objectives. In this thesis, we explore market algorithms in dynamic environments and under practical irrationality, analyzing how deviations from ideal models affect the utility of both designers and participants. We examine environmental changes such as market imbalance, resource augmentation, and competition faced by dominant sellers. For participants, we consider agents who are computationally bounded, behaviorally biased, or using learning algorithms. Our work aims to provide insights into the robustness and adaptability of market algorithms amid practical complexities and diverse participant behaviors.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Data structure
- 키워드
- Decision theory
- 키워드
- Mechanism design
- 키워드
- Decision-making
- 기타저자
- Princeton University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384463597
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aCai, Linda.▼0(orcid)0009-0008-6512-6671
■24510▼aAlgorithmic Decision Making With Imperfect Information and Practical Irrationality
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a404 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Weinberg, Matt.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aMarket algorithms are ubiquitous in modern life, both online and offline. Historically, markets have been fundamental in matching supply with demand, where designers optimize regulations with global goals and participants optimize strategies with individual goals. Decision theory and mechanism design have evolved to study and prescribe the behavior and structure of these markets. This thesis addresses key questions at the intersection of market design and algorithmic decision-making: How do strategic decision-makers navigate choices, and how do resource producers allocate limited resources among strategic participants?The advent of information technology and increased data availability has revolutionized markets, enabling unprecedented scale, efficiency, and control. This development allows mechanism designers to create or modify market conditions, provided we understand their impact on the designer's objectives. In this thesis, we explore market algorithms in dynamic environments and under practical irrationality, analyzing how deviations from ideal models affect the utility of both designers and participants. We examine environmental changes such as market imbalance, resource augmentation, and competition faced by dominant sellers. For participants, we consider agents who are computationally bounded, behaviorally biased, or using learning algorithms. Our work aims to provide insights into the robustness and adaptability of market algorithms amid practical complexities and diverse participant behaviors.
■590 ▼aSchool code: 0181.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aData structure
■653 ▼aDecision theory
■653 ▼aMechanism design
■653 ▼aMarket algorithms
■653 ▼aDecision-making
■690 ▼a0984
■690 ▼a0511
■690 ▼a0464
■71020▼aPrinceton University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163844▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
