A Tri-reference-point Decision-making Method for Major Epidemic Prevention and Control Strategies Based on Bottom-line Thinking

Decision-making regarding prevention and control strategies for major pandemics concerns people’s safety and social stability, and it represents a typical high-risk decision-making problem. As a fundamental guideline for preventing and resolving major risks, bottom-line thinking holds significant theoretical value and practical guiding significance for scientific decision-making in pandemic response. To this end, guided by the bottom-line thinking, a fundamental principle for preventing and mitigating major risks, a three-reference-point decision-making model for major epidemic prevention and control strategies was constructed. This method overcomes the limitation of traditional prospect theory, which relies solely on the “status quo” as a single reference point, by introducing two key reference points—“bottom line” and “target”—to form a decision-making framework together with the “status quo”. This framework more accurately characterizes the risk psychology and behavioral traits of decision-makers when confronting extreme losses (breaching the bottom line) and ideal gains (achieving the target). To more realistically reflect the heightened sensitivity of decision-makers to extreme risks under bottom-line thinking, the probability weighting function was extended from a single-reference-point to a three-reference-point framework, significantly enhancing the ability to depict differences in decision-makers’ subjective probability perceptions. A case analysis was conducted to verify the effectiveness and feasibility of the proposed method. Parameter sensitivity analysis further demonstrates that the setting of the “bottom line” significantly increases decision-makers’ loss aversion, while the setting of the “target” directly influences their expected value of gains, thereby highlighting the central role of bottom-line thinking in the model. This research provides theoretical and methodological support for the quantitative application of bottom-line thinking in high-risk decision-making contexts such as major epidemic prevention and control.