期刊
SOFT COMPUTING
卷 25, 期 12, 页码 7947-7956出版社
SPRINGER
DOI: 10.1007/s00500-021-05617-4
关键词
Fuzzy cognitive map; Nonlinear Hebbian learning; Public medical insurance; Medical service satisfaction
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education of Korea [NRF-2019R1I1A1A01046810]
- Institute of ICT Planning and Evaluation (IITP) - Ministry of Science and ICT of Korea [2020-0-01343]
- BK21 FOUR (Fostering Outstanding Universities for Research) - Ministry of Education
- National Research Foundation of Korea
The study examines the effects of policy reforms on public medical insurance on households using fuzzy cognitive map. A hybrid approach is adopted to construct maps for low-income households and general households separately. Results show that government subsidy increases have the largest impacts on households, demonstrating the flexibility and extensibility of FCM.
We examine the effects of policy reforms on public medical insurance on households. We employ fuzzy cognitive map (FCM) since it allows for a parsimonious structure and still yields reliable simulation results. We adopt a hybrid approach to construct FCM and separately design maps for low-income households and general households. We further examine three scenarios in which government subsidies on public medical insurance, insurance coverage rates, and registration rates increase respectively. Our simulation based on the constructed FCM shows that government subsidy increases have the largest impacts on households. We demonstrate both flexibility and extensibility of FCM by assessing different scenarios.
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