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Collective Intelligence in Crowd-Based Systems
Collective Intelligence in Crowd-Based Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151342
- ISBN
- 9798384017912
- DDC
- 004
- 서명/저자
- Collective Intelligence in Crowd-Based Systems
- 발행사항
- [Sl] : Northwestern University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 224 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Horvat, Emoke-Agnes.
- 학위논문주기
- Thesis (Ph.D.)--Northwestern University, 2024.
- 초록/해제
- 요약Online platforms have become integral to many aspects of modern life, serving as intermediaries in various activities such as work collaboration, career opportunities, entrepreneurial ventures, news consumption, and exposure to opinion-forming information. The complex communication networks that facilitate the spread of information and subsequent decision-making on these platforms often lead to unexpected collective behaviors. The emergent behaviors of crowds in online platforms can have profound and unintended impacts on individual and societal outcomes in crucial domains like science, entrepreneurship, and public affairs. Therefore, a key issue in online platforms is whether the collectives that form in these spaces exhibit intelligent collective behavior or are influenced by group-think and reinforcing biases.This dissertation represents a collection of studies that systematically investigate the conditions that promote superior collective decision-making and factors associated with collective intelligence in crowd-based systems, i.e., online platforms that facilitate communication, cooperation, and coordination among collectives. The studies are based on theory-driven investigations that rely on mining Big Data about collective behavior online, mock online experiments and surveys, and machine learning and statistical modeling frameworks. Supported by these novel combinations of approaches, I develop empirical measures that characterize individual and collective behavior in online settings. Building upon these measures, I seek to find and explain how, why, and when local individual behaviors give rise to emergent collective outcomes and vice versa. These investigations take into account the impacts of platform design choices and algorithmic influences as well as the causes and consequences of inter-individual differences in experience, goals, personal interests and motivations, prosocial behavior, altruism, and susceptibility to social influence on collective behavior.This dissertation not only provides new insights into theories of collective intelligence and complex systems but also offers practical contributions toward crowd-aware system design. In contrast to prevailing social psychology theories that downplay the potential of online crowds, I present empirical evidence from multiple online platforms to demonstrate that, under certain conditions, crowds can generate novel information and achieve superior collective decision-making outcomes, even surpassing those of experts. These significant contributions have implications for computational social science, network science, social computing, and human-computer interaction. I conclude by discussing the key concepts behind the crowd signals identified in this dissertation, its main findings and contributions, the methodological considerations that shaped the studies, and the dissertation's overarching limitations and potential for future research.
- 일반주제명
- Computer science
- 일반주제명
- Communication
- 일반주제명
- Behavioral psychology
- 일반주제명
- Social psychology
- 키워드
- Network science
- 키워드
- Online platforms
- 키워드
- Social computing
- 기타저자
- Northwestern University Technology and Social Behavior
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151342
■006m o d
■007cr#unu||||||||
■020 ▼a9798384017912
■035 ▼a(MiAaPQ)AAI31242306
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aDambanemuya, Henry Kudzanai.▼0(orcid)0000-0002-1358-42158-4215
■24510▼aCollective Intelligence in Crowd-Based Systems
■260 ▼a[Sl]▼bNorthwestern University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a224 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Horvat, Emoke-Agnes.
■5021 ▼aThesis (Ph.D.)--Northwestern University, 2024.
■520 ▼aOnline platforms have become integral to many aspects of modern life, serving as intermediaries in various activities such as work collaboration, career opportunities, entrepreneurial ventures, news consumption, and exposure to opinion-forming information. The complex communication networks that facilitate the spread of information and subsequent decision-making on these platforms often lead to unexpected collective behaviors. The emergent behaviors of crowds in online platforms can have profound and unintended impacts on individual and societal outcomes in crucial domains like science, entrepreneurship, and public affairs. Therefore, a key issue in online platforms is whether the collectives that form in these spaces exhibit intelligent collective behavior or are influenced by group-think and reinforcing biases.This dissertation represents a collection of studies that systematically investigate the conditions that promote superior collective decision-making and factors associated with collective intelligence in crowd-based systems, i.e., online platforms that facilitate communication, cooperation, and coordination among collectives. The studies are based on theory-driven investigations that rely on mining Big Data about collective behavior online, mock online experiments and surveys, and machine learning and statistical modeling frameworks. Supported by these novel combinations of approaches, I develop empirical measures that characterize individual and collective behavior in online settings. Building upon these measures, I seek to find and explain how, why, and when local individual behaviors give rise to emergent collective outcomes and vice versa. These investigations take into account the impacts of platform design choices and algorithmic influences as well as the causes and consequences of inter-individual differences in experience, goals, personal interests and motivations, prosocial behavior, altruism, and susceptibility to social influence on collective behavior.This dissertation not only provides new insights into theories of collective intelligence and complex systems but also offers practical contributions toward crowd-aware system design. In contrast to prevailing social psychology theories that downplay the potential of online crowds, I present empirical evidence from multiple online platforms to demonstrate that, under certain conditions, crowds can generate novel information and achieve superior collective decision-making outcomes, even surpassing those of experts. These significant contributions have implications for computational social science, network science, social computing, and human-computer interaction. I conclude by discussing the key concepts behind the crowd signals identified in this dissertation, its main findings and contributions, the methodological considerations that shaped the studies, and the dissertation's overarching limitations and potential for future research.
■590 ▼aSchool code: 0163.
■650 4▼aComputer science
■650 4▼aCommunication
■650 4▼aBehavioral psychology
■650 4▼aSocial psychology
■653 ▼aCollective intelligence
■653 ▼aEmpirical methods
■653 ▼aLarge-scale surveys
■653 ▼aNetwork science
■653 ▼aOnline platforms
■653 ▼aSocial computing
■690 ▼a0984
■690 ▼a0459
■690 ▼a0451
■690 ▼a0384
■71020▼aNorthwestern University▼bTechnology and Social Behavior.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0163
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161338▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
