Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research

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dc.contributor.authorLiu, Shiyongen
dc.contributor.authorTriantis, Konstantinos P.en
dc.contributor.authorZhao, Lien
dc.contributor.authorWang, Youfaen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2019-06-04T16:57:39Zen
dc.date.available2019-06-04T16:57:39Zen
dc.date.issued2018-04-25en
dc.description.abstractBackground In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. Methodology In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. Results In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. Conclusions This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.en
dc.description.notesThe study was supported by research grants from the Chinese National Social Science Foundation (12CGL103) and the Fundamental Research Funds for the Central Universities to SL, and the Southwestern University of Finance and Economics, the US National Institutes of Health (NIH) (research grants 1R01HD064685-01A1 and U54HD070725 to YW from the Eunice Kennedy Shriver National Institute of Child Health & Human Development [NICHD]). The U54 project is co-funded by the NICHD and the NIH Office of Behavioral and Social Sciences Research (OBSSR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscripten
dc.description.sponsorshipChinese National Social Science Foundation [12CGL103]; Fundamental Research Funds for the Central Universities; Southwestern University of Finance and Economics; US National Institutes of Health (NIH) from the Eunice Kennedy Shriver National Institute of Child Health & Human Development [NICHD] [1R01HD064685-01A1, U54HD070725]; NICHD; NIH Office of Behavioral and Social Sciences Research (OBSSR)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0194687en
dc.identifier.eissn1932-6203en
dc.identifier.issue4en
dc.identifier.othere0194687en
dc.identifier.pmid29694364en
dc.identifier.urihttp://hdl.handle.net/10919/89742en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCapturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems researchen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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