Agent-based modeling for a complex world. Part 1

 
PIIS042473880018970-6-1
DOI10.31857/S042473880018970-6
Publication type Article
Status Published
Authors
Occupation: Scientific Director; President of the Russian School of Economics; Director of the Higher School of Public Administration
Affiliation:
Central Economics and Mathematics Institute of the Russian Academy of Sciences
Russian School of Economics
Higher School of Public Administration of the Moscow State University
Address: Moscow, Russian Federation
Occupation: Director, CEMI RAS; Professor
Affiliation:
Central Economics and Mathematics Institute of the Russian Academy of Sciences
Moscow State University
Address: Russian Federation
Affiliation:
NYU School of Global Public Health
NYU Agent-Based Modeling Laboratory
Address: United States of America
Journal nameEkonomika i matematicheskie metody
EditionVolume 58 Issue 1
Pages5-26
Abstract

The main goal of this paper is to summarize selected developments in the field of artificial societies and agent-based modeling and to suggest, how this fundamentally new toolkit can contribute to solving some of the most complex scientific and practical problems of our time. The entire field of agent-based modeling has expanded dramatically over the last quarter century, with applications across a remarkable array of fields, at scales ranging from molecular to global.The models described in this paper are a small part of worldwide scientific and practical developments in the field 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, conflict, 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 offer a new and powerful alternative, or complement, to traditional mathematical methods for addressing complex challenges.

Keywordsagent-based models, epidemiology, pedestrian traffic, demographic processes, transport systems, ecological forecasting, land use, urban dynamics, historical episodes, conflict simulation, social networks, economic systems
AcknowledgmentThe 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 centres in Russia.”
Received27.02.2022
Publication date18.03.2022
Number of characters64973
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