Monte Carlo simulation in risk analysis of battle casualty forecasting in urban aggressive military action / 第二军医大学学报

ABSTRACT

Objective: To improve the present method for casualty forecasting using the risk analysis theory. Methods: Empirical data were extracted from 11 urban aggressive combats during and after WW II, and the distribution of daily casualty was determined. Based on quantitative judgment model, several factors such as the number of soldiers, terrain, weather, general situation, suddenness, and combat efficacy were configured; Monte Carlo simulation was applied for simulation,and a forecasting model was setup with Microsoft Excel and Crystal Ball 2000 software for risk analysis of the simulation outcome. Results: The distribution of daily casualty during urban aggressive military action could be represented by normal distribution. With the values of the aforementioned factors, the result of 1000 tests showed that the mean daily casualty rate was 0. 42%, with the standard deviation being 0. 21%. Conclusion: Monte Carlo simulation is an effective means to improve the present casualty forecasting method.