7 ЭКОНОМИКА И МАТЕМАТИЧЕСКИЕ МЕТОДЫ, 2022, том 58, № 2, с. 7–21 6. GEOGRAPHY AND ENVIRONMENT Forecasting the state of the environment using an agent-based approach can be distinguished as a separate major area. An overview of the most well-known models simulating the processes of environmental pollu- tion due to human activities, the infuence of the environment condition on the morbidity and mortality of the population, as well as the processes of managing the environmental aggravations is given in a recent pub- lication by the staf of Central Economics and Mathematics Institute of the Russian Academy of Sciences (Makarov, Bakhtizin, Sushko, 2020). This article also reviews best practices in socio-ecological and eco- nomic agent-based modeling. The CEMI RAS Model includes two types of agents — people and enterprises. The authors thank Elena Boinovich and Milana Sidorenko for their assistance in the technical edition. The reported study was funded by Russian Science Foundation according to the research project no. 21-18-00136 “Development of a software and analytical complex for assessing the consequences of intercountry trade wars with an application for functioning in the system of distributed situational centers in Russia.” Abstract. The main goal of this paper is to summarize selected developments in the feld of artifcial societies and agent-based modeling and to suggest, how this fundamentally new toolkit can contribute to solving some of the most complex scientifc and practical problems of our time. The entire feld of agent-based modeling has expanded dramatically over the last quarter century, with applications across a remarkable array of felds, at scales ranging from molecular to global. The models described in this paper are a small part of worldwide scientifc and practical developments in the feld of agent-based modelling and related areas. We have attempted to give an impression of the vast range of application areas (epidemiology, economics, demography, environment, urban dynamics, history, confict, disaster preparedness), scales (from cellular to local to urban to planetary), and goals (simple exploratory models, optimization, generative explanation, forecasting, policy) of agent-based modeling. Agent- based models ofer a new and powerful alternative, or complement, to traditional mathematical methods for addressing complex challenges. Keywords: agent-based models, epidemiology, pedestrian trafc, demographic processes, transport systems, ecological forecasting, land use, urban dynamics, historical episodes, confict simulation, social networks, economic systems. JEL Classifcation: C63, D91. For reference: Makarov V. L., Bakhtizin A. R., Epstein J. M. (2022). Agent-based modeling for a complex world. Part 2. Economics and Mathematical Methods, 58, 2, 7–21. DOI: Received 10.12.2021 V. L. Makarov, Academician of the Russian Academy of Sciences, Russia’s largest (prominent) specialist in the feld of computer modeling of socio-economic processes; Scientifc Director of the Central Economics and Mathematics Institute of the Russian Academy of Sciences; President of the Russian School of Economics (New Economic School); Director of the Higher School of Public Administration of the Moscow State University, Moscow, Russia; e-mail: makarov@cemi.rssi.ru A. R. Bakhtizin, Corresponding Member of the Russian Academy of Sciences; Director of the Central Economics and Math- ematics Institute of the Russian Academy of Sciences; Professor at the Moscow State University; Certifed CGE Modeler (World Bank Institute Certifed); Holder of professional certifcates from Microsoft Company (Microsoft Certifed Professional, Microsoft Certifed Application Developer, Microsoft Certifed Solution Developer), Moscow, Russia; e-mail: albert.bakhtizin@gmail.com J. M. Epstein, Professor of Epidemiology at the New York University (NYU) School of Global Public Health, and founding Director of the NYU Agent-Based Modeling Laboratory, with afliated faculty appointments to the Courant Institute of Mathematical Sciences, and the Department of Politics. © 2022 V. L. Makarov, A. R. Bakhtizin, J. M. Epstein Agent-based modeling for a complex world. Part 2 ТЕОРЕТИЧЕСКИЕ И МЕТОДОЛОГИЧЕСКИЕ ПРОБЛЕМЫ