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

摘要: Background In practical research, it was found that most people made health-related decisions not based on numerical data but 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 sake understanding mechanisms affect implementations interventions, we employ fuzzy variables to quantify variable in healthcare modeling where an integrated system dynamics agent-based model. Methodology a nonlinear causal-driven simulation environment driven by feedback loops, mathematically demonstrate how interventions at aggregate level are captured agents interactions among agents, same time, formation different clusters(groups) targeted specific interventions. Results this paper, provide innovative framework capture multi-stage uncertainties manifested interacting heterogeneous (individuals) intervention homogeneous (groups individuals) hybrid model combines (ABM) models (SDM). Having built platform incorporate high-dimension ABM/SDM model, paper demonstrates one can obtain state behaviors SDM ABM. Conclusions This research provides way model. This only enriches application set theory capturing associated with lead also informs implementation enabling incorporation both individual institutional levels, which makes unstructured meaningful quantifiable environment. help practitioners decision makers gain better complexities precision healthcare. It aid improvement optimal allocation resources for group (s) achievement maximum utility. As technology becomes more mature, design policy flight simulators policy/intervention designers test variety assumptions when they evaluate alternatives interventions.