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Computational Perspectives on Individual and Collective Decision-Making
Computational Perspectives on Individual and Collective Decision-Making
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152701
- ISBN
- 9798384051787
- DDC
- 004
- 서명/저자
- Computational Perspectives on Individual and Collective Decision-Making
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 437 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: A.
- 주기사항
- Advisor: Kleinberg, Jon.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약Individual decisions determine the success of companies (Pepsi or Coke?) and the structure of our social networks (Alice or Bob?), while collective decisions determine the composition of our governments and the outcomes of criminal trials, among countless other facets of our lives. As such, understanding the factors that contribute to these decisions is crucial, both for predicting future decisions and for designing interventions. In this dissertation, we use computational techniques to address two core questions towards this end. First, can we learn about how people make choices from individual decision-making data? Second, how do we aggregate group preferences in collective decision-making and what are the consequences of different aggregation mechanisms?After a brief introduction in Part I, Part II describes several methods to learn the effects of social and contextual factors on preferences in individual discrete choice settings, synthesizing tools from interpretable machine learning, causal inference, and graph learning. In Part III, we turn to collective decisions, focusing on theoretically characterizing the behavior of two commonly used voting systems, plurality and instant runoff voting (IRV). In particular, we explore what happens under IRV when voters are forced to submit top-truncated preferences, prove that IRV favors moderate candidates in a way plurality does not, and examine the dynamics of candidate policies under a boundedly-rational imitative model. We conclude in Part IV with closing thoughts and directions for future work.
- 일반주제명
- Computer science
- 일반주제명
- Behavioral psychology
- 일반주제명
- Political science
- 키워드
- Social choices
- 키워드
- Voting
- 키워드
- Social networks
- 기타저자
- Cornell University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-03A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152701
■006m o d
■007cr#unu||||||||
■020 ▼a9798384051787
■035 ▼a(MiAaPQ)AAI31487946
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aTomlinson, Kiran.▼0(orcid)0000-0002-0300-0845
■24510▼aComputational Perspectives on Individual and Collective Decision-Making
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a437 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: A.
■500 ▼aAdvisor: Kleinberg, Jon.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aIndividual decisions determine the success of companies (Pepsi or Coke?) and the structure of our social networks (Alice or Bob?), while collective decisions determine the composition of our governments and the outcomes of criminal trials, among countless other facets of our lives. As such, understanding the factors that contribute to these decisions is crucial, both for predicting future decisions and for designing interventions. In this dissertation, we use computational techniques to address two core questions towards this end. First, can we learn about how people make choices from individual decision-making data? Second, how do we aggregate group preferences in collective decision-making and what are the consequences of different aggregation mechanisms?After a brief introduction in Part I, Part II describes several methods to learn the effects of social and contextual factors on preferences in individual discrete choice settings, synthesizing tools from interpretable machine learning, causal inference, and graph learning. In Part III, we turn to collective decisions, focusing on theoretically characterizing the behavior of two commonly used voting systems, plurality and instant runoff voting (IRV). In particular, we explore what happens under IRV when voters are forced to submit top-truncated preferences, prove that IRV favors moderate candidates in a way plurality does not, and examine the dynamics of candidate policies under a boundedly-rational imitative model. We conclude in Part IV with closing thoughts and directions for future work.
■590 ▼aSchool code: 0058.
■650 4▼aComputer science
■650 4▼aBehavioral psychology
■650 4▼aPolitical science
■653 ▼aDiscrete choice models
■653 ▼aSocial choices
■653 ▼aVoting
■653 ▼aSocial networks
■653 ▼aInstant runoff voting
■690 ▼a0984
■690 ▼a0384
■690 ▼a0800
■690 ▼a0615
■71020▼aCornell University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-03A.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163383▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
