Simulation of the population's qualitative characteristics formation in the agent-based model of labor potential reproduction

The article proposes a procedure for simulating the population’s qualitative characteristics formation using agent-based modeling. The novelty and originality of the author's approach consists in using the concept of qualitative characteristics of population and filling the model with real sociological data of the labor potential quality monitoring of the Vologda region population. Composition of the labor potential quality includes eight components: physical and mental health, cognitive potential, creativity, sociability, cultural and moral levels, need for achievement. All the listed qualitative characteristics are measured on the basis of sociological monitoring and receive a numerical estimate in the form of indices in the interval from zero to one. Indices are calculated as the ratio of the actual number of points scored by the respondent in the block of questions to the maximum possible. It is believed that the behavior of an individual relative to each of the qualities can be destructive, neutral, or constructive. Provided destructive behavior of a human agent, the quality index is reduced. Constructive behavior implies that actions of an agent contribute to development of his potential. With neutral behavior, the index remains unchanged. Controllable parameters of the model are coefficients of the transition from one state to another. Behavior of agents is simulated in the AnyLogic simulation system and is represented by visual diagrams of states. Development of qualitative characteristics of the population is realized in the form of transitions from the state, accompanied by destructive behavior, to the neutral and constructive or vice versa. The application of the proposed approach to simulating the formation of qualitative characteristics of the population makes it possible to foresee probable changes in the quality of labor potential and to test various variants of administrative influences by selecting optimal ones.