An agent-based approach to implementing the model of forest restoration

The timber complex of the Russian Federation occupies an important place in the country’s economy. Russian forests are one of the most valuable renewable natural resources. They constitute more than a quarter of world reserves of wood biomass and perform significant environmental and protective functions. The existing forest resources in Russian both provide current and future domestic demand for timber and processed products, and significantly expand exports of forest products. But at the same time long-term extensive forest exploitation aimed at withdrawal of stocks of softwood carried out without due attention to the issue of the replacement of coniferous species with soft-wooded broadleaved species, has led to the fact that over the past ninety years, the share of coniferous forests decreased from 90 to 50 %. The solution to the commodity issue requires increased attention to the issues of forest restoration. Given the fact that the real result of all activities on forest restoration expressed both in quantitative and qualitative composition of the forest cover, can be achieved only after several decades, a comprehensive solution to the problem seems difficult to achieve without creating a scientifically proven system of decision-making support which is expected to be implemented with the use of a system of agent-based models. The article describes general principles of agent-based models, including the description of the algorithm of agent’s choice of behavior pattern. We developed a context diagram and a general functional scheme of a forest restoration model. We identify the main agents acting in the model; describe their main characteristics, including their goals, motives and behavior scenarios, the interaction of agents with each other and the external environment. Further model development implies detailed elaboration and specification of agents’ characteristics, conditions of scenario choice, assessment of mutual influence of this choice on the environment and agents’ characteristics.