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020 ▼a 9781088395462
035 ▼a (MiAaPQ)AAI22617047
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 614
1001 ▼a Jin, Zhu.
24510 ▼a Ontology for Agent-Based Modeling of Oral Health Disparities.
260 ▼a [S.l.]: ▼b State University of New York at Buffalo., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 118 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 ▼a Advisor: Metcalf, Sara.
5021 ▼a Thesis (Ph.D.)--State University of New York at Buffalo, 2019.
506 ▼a This item must not be sold to any third party vendors.
520 ▼a The diversity of dynamic modeling approaches creates challenges for ensuring structural legitimacy of the resulting models. Agent-based modelers have the flexibility to create, analyze, and conduct experiments, but face the problem of structural uncertainty that influences simulation results. The structural uncertainty, related with lack of knowledge of reality, arises from the absence of a general model structure or principle on model design. Ontology provides a structural foundation for agent-based models (ABM) that can improve their correspondence with reality. This dissertation research develops a novel approach to combining ABM and ontology that streamlines model design and implementation. An ABM ontology is developed as the conceptual framework that provides a general model structure with a clear definition of major ABM components and relations between them. This approach is demonstrated by developing comparative models of miasma and germ theories of disease. Having established the utility of the approach, it is used to develop an oral health model to simulate oral health behavior for racial and ethnic minority older adults in an urban environment through related factors and cumulative experiences of individuals in regards to their oral health and dental visits. This modeling effort contributes to inform implementation of effective and appropriate oral health policies and interventions to improve oral health equity by easing the oral disease burden for disadvantaged populations.
590 ▼a School code: 0656.
650 4 ▼a Geography.
650 4 ▼a Information science.
650 4 ▼a Health sciences.
690 ▼a 0366
690 ▼a 0723
690 ▼a 0566
71020 ▼a State University of New York at Buffalo. ▼b Geography.
7730 ▼t Dissertations Abstracts International ▼g 81-04B.
773 ▼t Dissertation Abstract International
790 ▼a 0656
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493442 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK