The purpose of the study is to develop a model for the co-evolution of the regional economy and economic institutions. The research methods used: abstract-logical for the study of theoretical aspects and the experience of modeling co-evolution; and economic-mathematical for the development of own model of coevolution. The results of the study: approaches to modeling the evolution of economic institutions, as well as the co-evolution of the regional economy and economic institutions are considered, strengths and weaknesses of existing approaches to modeling co-evolution are identified, on the basis of the logistic model and Lotka-Volterra equations, an own co-evolution model has been developed, which includes three entities: regional economy, “good” institution and “bad” institution. Three versions of the model have been developed: the co-evolution of the regional economy and the “good” institution, the co-evolution of the regional economy and the “bad institution,” and a variant of the co-evolution of all three entities simultaneously, in which the “good” and “bad” institutions interact according to the “predator-prey” model, and their the cumulative effect determines the development of the regional economy. Numerical experiments have been carried out in the MathLab, which have shown the capabilities of the model to reflect the results of the co-evolution of the economy of a resource-producing region and economic institutions. In the first variant, a “good” institution promotes economic growth in excess of the level determined by resource availability. In the second variant, the “bad” institution has a disincentive effect on the GRP, as a result of which the GRP falls below the level determined by the resource endowment. In the third variant, the interaction of “good” and “bad” institutions still contributes to economic growth above the level determined by resource availability, but causes cyclical fluctuations in the GRP.
A fast sparse spectral method for nonlinear integro-differential Volterra equations with general kernels