서브메뉴
검색
Agents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models
Agents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150917
- ISBN
- 9798381976793
- DDC
- 001
- 저자명
- Head, Bryan.
- 서명/저자
- Agents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models
- 발행사항
- [Sl] : Northwestern University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 319 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Wilensky, Uri.
- 학위논문주기
- Thesis (Ph.D.)--Northwestern University, 2024.
- 초록/해제
- 요약Agent-based modeling (ABM) plays a critical role in complex systems research by allowing researchers to examine how individual-to-individual interactions collectively give rise to group-level and system-level behavior. However, fields ranging from socio-environmental systems to tumor biology to traffic modeling have increasingly sought to model interactions between systems of different scales. Multi-level agent-based modeling (ML-ABM) extends classic ABM techniques to meet this need. Despite this growing demand, multi-level modeling techniques introduce significant complexity into the modeling process and have yet to see widespread adoption among ABM practitioners. We introduced the LevelSpace extension for the widely used NetLogo ABM platform to make ML-ABM easily accessible to ABM researchers by leveraging NetLogo's core "low floor, high ceiling" approach.This dissertation builds on that work, showing how researchers can model multi-level phenomena by creating nested hierarchies of agent-based models. It accomplishes this through a series of novel and illustrative case studies. Each begins with a classic, single-level agent-based model, and then extends it to a multi-level modeling system. In each case, we explore the different kinds of relationships that can connect models, with a particular focus on the amount of coupling and re-usability of the component models involved. We perform in-depth experiments and analyses of each model, demonstrating the techniques involved in analyzing ML-ABMs, comparing the behavior of ML-ABMs with single-level ABMs, and gaining new insights into the simulated systems. Finally, we demonstrate that ML-ABM offers powerful techniques for defining agent cognition in particular by allowing agents to model their environment and make decisions using subordinate ABMs. The case studies presented in this dissertation offer thorough yet accessible templates by which to guide other researchers in the design and analysis of multi-level agent-based models.
- 일반주제명
- Systems science
- 일반주제명
- Computer science
- 키워드
- Complex systems
- 키워드
- NetLogo
- 기타저자
- Northwestern University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017160140
■00520250211150917
■006m o d
■007cr#unu||||||||
■020 ▼a9798381976793
■035 ▼a(MiAaPQ)AAI30817219
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a001
■1001 ▼aHead, Bryan.▼0(orcid)0009-0004-0332-1368
■24510▼aAgents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models
■260 ▼a[Sl]▼bNorthwestern University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a319 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Wilensky, Uri.
■5021 ▼aThesis (Ph.D.)--Northwestern University, 2024.
■520 ▼aAgent-based modeling (ABM) plays a critical role in complex systems research by allowing researchers to examine how individual-to-individual interactions collectively give rise to group-level and system-level behavior. However, fields ranging from socio-environmental systems to tumor biology to traffic modeling have increasingly sought to model interactions between systems of different scales. Multi-level agent-based modeling (ML-ABM) extends classic ABM techniques to meet this need. Despite this growing demand, multi-level modeling techniques introduce significant complexity into the modeling process and have yet to see widespread adoption among ABM practitioners. We introduced the LevelSpace extension for the widely used NetLogo ABM platform to make ML-ABM easily accessible to ABM researchers by leveraging NetLogo's core "low floor, high ceiling" approach.This dissertation builds on that work, showing how researchers can model multi-level phenomena by creating nested hierarchies of agent-based models. It accomplishes this through a series of novel and illustrative case studies. Each begins with a classic, single-level agent-based model, and then extends it to a multi-level modeling system. In each case, we explore the different kinds of relationships that can connect models, with a particular focus on the amount of coupling and re-usability of the component models involved. We perform in-depth experiments and analyses of each model, demonstrating the techniques involved in analyzing ML-ABMs, comparing the behavior of ML-ABMs with single-level ABMs, and gaining new insights into the simulated systems. Finally, we demonstrate that ML-ABM offers powerful techniques for defining agent cognition in particular by allowing agents to model their environment and make decisions using subordinate ABMs. The case studies presented in this dissertation offer thorough yet accessible templates by which to guide other researchers in the design and analysis of multi-level agent-based models.
■590 ▼aSchool code: 0163.
■650 4▼aSystems science
■650 4▼aComputer science
■653 ▼aAgent-based modeling
■653 ▼aComplex systems
■653 ▼aMulti-level modeling
■653 ▼aNetLogo
■653 ▼aMulti-level agent-based models
■690 ▼a0790
■690 ▼a0800
■690 ▼a0984
■71020▼aNorthwestern University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0163
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160140▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
