On methodological approaches to modeling the quality of life

In recent decades, it has become obvious that an ecological catastrophe is rapidly approaching civilization and the continued existence and development of mankind depends on it. It has long been understood in society that environmental factors affect the standard and quality of life of the population. Modeling the relationship of this influence is an interdisciplinary task that requires the removal of a number of methodological restrictions. Among them: effective data collection and monitoring, the choice of a measurement system, "noisiness" of data. The purpose of the work is to analyze the current state of methodological foundations and choose a method for building a model of the relationship of quality of life with components of the structure of quality of life of the population. Today, various systems of formalized description of the quality of life are proposed at the global level: the human development index, the environmental-economic accounting system, the real progress index and the sustainable economic well-being index, the happiness index, the quality of life index according to the Economist Intelligence Unit, the Sustainable Society Index. The modern stage of research on the problem of modeling the level and quality of life is represented by intercountry and interregional comparisons using a developed mathematical apparatus. The work presents general and methodological problems of quality of life research; analysis and selection of methods for solving problems of modeling the relationship of components and indicators of the level and quality of life of the population. The following are considered: methods of multicriterial evaluation, methods of multipurpose mathematical programming, statistical methods, methods of dynamic modeling, methods of simulation modeling. It is shown that in order to solve the applied problems of building a model of level and quality of life, it is advisable to use multidimensional statistical methods, where the initial necessary stage is the procedure of data conversion by standardizing (normalizing) data, namely, bringing all variables involved in the construction of an integral indicator to a single unified scale.