Agent-based modeling for a complex world. Part 2

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: Russian Federation, Moscow

Albert Bakhtizin

Occupation: Director, CEMI RAS; Professor at the Moscow State University
Affiliation:
Central Economics and Mathematics Institute of the Russian Academy of Sciences
Moscow State University
Address: Russian Federation, Moscow

Joshua M. Epstein

Occupation: Professor of Epidemiology at the NYU School of Global Public Health; founding Director of the NYU Agent-Based Modeling Laboratory, with affiliated faculty appointments to the Courant Institute of Mathematical Sciences, and the Department of Politics
Affiliation: New York University
Address: United States, New York

Journal name

Ekonomika i matematicheskie metody

Edition

Volume 58 Issue 2

Pages

7-21

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.

Keywords

agent-based models, epidemiology, pedestrian traffic, demographic processes, transport systems, ecological forecasting, land use, urban dynamics, historical episodes, conflict simulation, social networks, economic systems

Acknowledgment

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.”

Received

10.12.2021

Publication date

18.06.2022

Number of characters

40431

Cite

GOST	Makarov V., Bakhtizin A., Epstein J. Agent-based modeling for a complex world. Part 2 // Ekonomika i matematicheskie metody – 2022. – V. 58. – Issue 2 C. 7-21 [Electronic resource]. URL: https://emm.jes.su/S042473880020009-8-1 (circulation date: 19.04.2024). DOI: 10.31857/S042473880020009-8
MLA	Makarov, Valery, Bakhtizin, Albert, Epstein, Joshua M "Agent-based modeling for a complex world. Part 2." Ekonomika i matematicheskie metody 58.2 (2022):7-21. DOI: 10.31857/S042473880020009-8
APA	Makarov V., Bakhtizin A., Epstein J. (2022). Agent-based modeling for a complex world. Part 2. Ekonomika i matematicheskie metody 58(2), pp.7-21 DOI: 10.31857/S042473880020009-8

100 rub.

When subscribing to an article or issue, the user can download PDF, evaluate the publication or contact the author. Need to register.

Number of purchasers: 2, views: 435

Readers community rating: votes 0

1. Agents of change (2010). Conventional economic models failed to foresee the financial crisis. Could agent-based modelling do better? The Economist, July 24. Available at: https://www.economist.com/finance-and-economics/2010/07/22/agents-of-change

2. Аgents of creation (2003). Artificial “agents” can model complex systems. The Economist, October 11. Available at: https://www.economist.com/science-and-technology/2003/10/09/agents-of-creation

3. Axtell R.L., Epstein J.M., Dean J.S., Gumerman G.J., Swedlund A.C., Harburger J., Chakra-varty S., Hammond R., Parker J., Parker M. (2002). Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences, May, 99 (suppl. 3), 7275–7279. DOI: 10.1073/pnas.092080799

4. Axtell R.L. (2015). Endogenous dynamics of multi-agent firms. SSRN Electronic Journal. DOI: 10.2139/ssrn.2827059

5. Axtell R.L. (2016). 120 million agents self-organize into 6 million firms: A model of the U.S. pri-vate sector. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS '16). International Foundation for Autonomous Agents and Multiagent Systems. Richland, SC, 806–816. Available at: https://dl.acm.org/doi/10.5555/2936924.2937042

6. Axtell R.L. (2001). Zipf distribution of U.S. Firm Sizes. Science, 293 (5536), 1818–1820.

7. Bakhtizin A.R., Ilyin N.I., Khabriev B.R., Makarov V.L., Sushko E.D. (2020). Software and analytical complex “MÖBIUS” — a tool for planning, monitoring and forecasting the socio-economic system of Russia. Artificial Societies, 15 (4). DOI: 10.18254/S207751800012303-2 (in Russian).

8. Bakhtizin A.R., Makarov V.L., Maksakov A.A., Sushko E.D. (2021). Demographic agent-based model of Russia and assessment of its applicability for solving practical management prob-lems. Artificial Societies, 16, 2 (in Russian). DOI: 10.18254/S207751800015357-1

9. Bravo G., Squazzoni F., Boero R. (2012). Trust and partner selection in social networks: An experimentally grounded model. Social Networks, 34, 4, 481–492. DOI: 10.1016/j.socnet.2012.03.001

10. Cederman L.E. (2003). Modeling the size of wars: From billiard balls to sand piles. American Po-litical Science Review, 97 (1), 135–150.

11. Crevald M. van (1977). Supplying war: Logistics from Wallenstein to Patton. New York: Cambridge University Press.

12. Deissenberg C., Hoog S. van der, Dawid H. (2008). EURACE: A massively parallel agent-based model of the European economy. Applied Mathematics and Computation, 204, 541–552. DOI: 10.1016/j.amc.2008.05.116

13. Diamond J. (2002). Life with the artificial Anasazi. Nature, 419, 567–568. DOI: 10.1038/419567a

14. Dou Y., Millington J.D.A, Bicudo Da Silva R.F., McCord P., Viña A., Song Q., Yu Q., Wu W., Batistella M., Moran E., Liu J. (2019). Land-use changes across distant places: design of a telecoupled agent-based model. Journal of Land Use Science, 14, 3, 191–209. DOI: 10.1080/1747423X.2019.1687769

15. Epstein J.M. (2013). Agent_Zero: Toward neurocognitive foundations for generative social science. Princeton: Princeton University Press. 249 p.

16. Epstein J.M. (2002). Modeling civil violence: An agent-based computational approach. Proceed-ings of the National Academy of Sciences. May, 99 (suppl. 3), 7243–7250. DOI: 10.1073/pnas.092080199

17. Epstein J.M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press. 352 p.

18. Epstein J.M., Axtell R. (1996). Growing artificial societies: Social science from the bottom up. Washington: Brookings Institution Press. The MIT Press.

19. Farmer J., Foley D. (2009). The economy needs agent-based modelling. Nature, 460, 685–686. DOI: 10.1038/460685a

20. Fleischmann A. (2005). A model for a simple Luhmann economy. Journal of Artificial Societies and Social Simulation, 8, 2. Available at: https://www.jasss.org/8/2/4.html

21. Fosset P., Banos A., Beck E., Chardonnel S., Lang C., Marilleau N., Piombini A., Leysens T., Conesa A., Andre-Poyaud I., Thevenin T. (2016). Exploring intra-urban accessibility and impacts of pollution policies with an agent-based simulation platform: GaMiroD. Systems, 4 (1), 5. Available at: https://doi.org/10.3390/systems4010005

22. Glimcher P.W., Fehr E. (eds.) (2013). Neuroeconomics: Decision making and the brain. Second Edition. Academic Press. 577 p. ISBN-13: 978-0124160088.

23. Halaška M., Šperka R. (2018). Is there a need for agent-based modelling and simulation in busi-ness process management? Organizacija, 51 (4), 255–269. DOI: 10.2478/orga-2018-0019

24. Haldon J.F. (ed.) (2005). General issues in the study of medieval logistics: Sources, problems, methodologies. Leiden, Boston: Brill.

25. Hamill L., Gilbert N. (2009). Social circles: A simple structure for agent-based social network models. Journal of Artificial Societies and Social Simulation, 12 (2), 3. Available at: http://jasss.soc.surrey.ac.uk/12/2/3.html

26. Hommes C., LeBaron B. (Eds.) (2018). Computational economics: Heterogeneous agent model-ing. Handbook in Economics. Elsevier.

27. Huynh N., Perez P., Berryman M., Barthélemy J. (2015). Simulating transport and land use in-terdependencies for strategic urban planning — an agent based modelling approach. Systems, 3 (4), 177–210. Available at: https://doi.org/10.3390/systems3040177

28. Kirman A.P. (1992). Whom or what does the representative individual represent? Journal of Eco-nomic Perspectives, 6 (2), 117–136.

29. Kohler T.A., Gumerman G.J. (2000). Dynamics in human and primate societies: Agent-based modeling of social and spatial processes. Oxford University Press. Published to Oxford Scholarship Online. November. DOI: 10.1093/oso/9780195131673.001.0001

30. Lansing J.S., Kremer J.N. (1993). Emergent properties of Balinese water temple networks: 2 coa-daptation on a rugged fitness landscape. American Anthropologist, 95, 97–114.

31. LeBaron B. (2006). Agent-based Computational Finance. In: H.M. Amman, D.A. Kendrick, J. Rust (Eds). Handbook of Computational Economics. Vol. 2. Ch. 24, 1187–1233. Elsevier. Available at: https://EconPapers.repec.org/RePEc:eee:hecchp:2-24

32. LeBaron B. (2019). Microconsistency in simple empirical agent-based financial models. Computational Economics, 1, 19. DOI: 10.1007/s10614-019-09917-8

33. Lemos C. (2017). Agent-based modeling of social conflict: From mechanisms to complex behavior. Berlin: Springer Verlag.

34. Lemos C., Coelho H., Lopes R.J. (2013). Agent-based modeling of social conflict, civil violence and revolution: State-of-the-art-review and further prospects. CEUR Workshop Proceedings, 1113, 124–138.

35. Levitsky S., Ziblatt D. (2018). How democracies die. New York: Broadway Books.

36. Luhmann N. (1988). Die Wirtschaft der Gesellschaft (Suhrkamp taschenbuch wissenschaft). Frankfurt: Suhrkamp Verlag. 112 p.

37. Makarov V.L., Bakhtizin A.R., Beklaryan G.L., Akopov A.S. (2019). Development of software framework for large-scale agent-based modeling of complex social systems. Software Engi-neering (Programmnaya Ingeneria), 10, 4, 167–177 (in Russian).

38. Makarov V.L., Bakhtizin A.R., Khabriev B.R. (2018). Performance evaluation of the mechanisms strengthening the state sovereignty of Russia. Finance: Theory and Practice, 22 (5), 6–26 (in Russian). DOI: 10.26794/2587-5671-2018-22-5-6-26 (in Russian).

39. Makarov V.L, Bakhtizin A.R., Sushko E.D., Ageeva A.F. (2017). Agent-based approach for modelling the labour migration from China to Russia. Economy of Region, 13, 331–341 (in Russian). DOI: 10.17059/2017-2-1 (in Russian).

40. Makarov V.L., Bakhtizin A.R., Sushko E.D. (2020). Agent-based model as a tool for controlling environment of the region. Journal of the New Economic Association, 45, 151–171. DOI : 10.31737/2221-2264-2020-45-1-6 (in Russian).

41. Matthews R.B., Gilbert N.G., Roach A., Polhill J.G., Gotts N.M. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22, 1447–1459. DOI : 10.1007/s10980-007-9135-1

42. Murgatroyd P., Craenen B., Theodoropoulos G., Gaffney V., Haldon J. (2012). Modelling me-dieval military logistics: An agent-based simulation of a Byzantine army on the march. Computational and Mathematical Organization Theory, December, 18, 4, 488–506.

43. Murray-Rust D., Rieser V., Robinson D.T., Miličič V., Rounsevell M. (2013). Agent-based modelling of land use dynamics and residential quality of life for future scenarios. Environ-mental Modelling & Software, 46, 75–89. DOI : 10.1016/j.envsoft.2013.02.011

44. Nabinejad S., Schüttrumpf H. (2017). An agent-based model for land use policies in coastal areas. Coastal Engineering Proceedings, 1 (35), Management. 9. DOI : 10.9753/icce.v35.management.9

45. Peckham R. (2013). Economies of contagion: Financial crisis and pandemic. Economy and Society, 42, 2, 226–248. DOI: 10.1080/03085147.2012.718626

46. Polhill J.G., Parker D., Brown D., Grimm V. (2008). Using the ODD protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, 11 (2), 3. Available at: http://jasss.soc.surrey.ac.uk/11/2/3.html

47. Raberto M., Cincotti S., Teglio A. (2014). Economic policy and the financial crisis. Routledge Frontiers of Political Economy. Taylor & Francis, ch. 9.

48. Rand W., Rust R.T. (2011). Agent-based modeling in marketing: Guidelines for rigor. Interna-tional Journal of Research in Marketing, 28, 3, September, 181–193. DOI: 10.1016/j.ijresmar.2011.04.002

49. Stepanenko V.M., Bopape M.J., Glazunov A.V., Gritsun A.S., Lykosov V.N., Mortikov E.V., Porto F., Rivin G.S., Sithole H., Tolstykh M.A., Vilfand R.M., Volodin E.M., Voevodin V.V. (2020). HPC and Weather/Climate/Environment applications: Global challenges and opportunities for BRICS-cooperation. Presentation on 4th Meeting of the BRICS Working group on Information and Communication Technology and High Performance Computing. Nizhny Novgorod, Russia, 2020 October 8–9 Available at: https://istina.msu.ru/conferences/presentations/338451642/

50. Teglio A., Mazzocchetti A., Ponta L., Raberto M., Cincotti S. (2015). Budgetary rigor with sti-mulus in lean times: Policy advices from an agent-based model. Working Papers 2015/04. Economics Department. Universitat Jaume I, Castellón (Spain). Available at: https://ideas.repec.org/p/jau/wpaper/2015-07.html

51. Tesfatsion L. (2002). Agent-based computational economics: Modelling economies as complex adaptive systems. December. Available at: http://www.econ.iastate.edu/tesfatsi (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.64.7064&rep=rep1&type=pdf).

52. Will M., Groeneveld J., Frank K., Müller B. (2020). Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review. Socio-Environmental Systems Modelling, 2, 16325. DOI: 10.18174/sesmo.2020a16325

53. Yakovenko V.M., Rosser J.B. (2009). Colloquium: Statistical mechanics of money, wealth, and income. Rev. Mod. Phys. 81, 1703 (December 2).