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Fixed Investments and Macroeconomic Agent-Based Modeling

https://doi.org/10.25205/2542-0429-2021-21-1-5-28

Abstract

The significant progress observed in the field of artificial economy opens up new possibilities for modeling economic growth. Agent-based models (ABM) allow leaving the concept of a representative agent in the past and linking investment decisions of economic agents at the micro level with long-term macroeconomic growth. Modern ABMs offer new algorithms for modeling expectations, agent interaction, technical progress, pricing, and production planning. Our article analyzes the current state of modeling investment in fixed assets in operating macroeconomic ABMs. The subject of the review is the families of models Eurace, CATS, KS, Jamel, Lagom. The authors also present the investment block of the agent-based multiregional input-output model (ABMIOM) being developed. Comparative analysis demonstrates that modern ABMs, as a rule, implement the principle of stock-flow consistency. Modeling the investment process requires detailing the commodity nomenclature, so that the initially adopted two-sector division into investment and consumer goods is replaced by more detailed structures, which gives rise to the problem of accounting for inter-sectoral relations in production and consumption. The Leontief production function copes with this problem, which is confirmed by its widespread use in ABM. The size of firms' investments is often derived from the need to expand capacity in accordance with the current production plan, so that planning turns out to be myopic, and long-term aspects in ABM are still largely unrealized. Nevertheless, already now ABMs reproduce many phenomena associated with the economic cycle. The developed ABMIOM provides horizontal consistency of cash flows between agents and analysis of results using input-output tables. ABMIOM represents a step forward in reflecting intersectoral and interregional flows. The model reproduces the growth and contraction of the economy as a result of independent investment decisions of individual firms and households, which is reflected in the sectoral and spatial structure of the economy. Further development of ABMIOM is associated with the modeling of savings, intrafirm finance, money market, innovation and technical progress.

About the Authors

A. A. Tsyplakov
Institute of Economics and Industrial Engineering SB RAS; Novosibirsk State University
Russian Federation


L. V. Melnikova
Institute of Economics and Industrial Engineering SB RAS; Novosibirsk State University
Russian Federation


References

1. Abiad A., Furceri D., Topalova P. The Macroeconomic Effects of Public Investment: Evidence from Advanced Economies. IMF Working Paper WP/15/95, 2015, 26 p.

2. Nunez-Serrano A., Velazquez F. J. Is Public Capital Productive? Evidence from a Meta-analysis. Applied Economic Perspectives and Policy, 2017, vol. 39, no. 2, p. 313-345.

3. Ballot G., Mandel A., Vignes A. Agent-based Modeling and Economic Theory: Where Do We Stand? Journal of Economic Interaction and Coordination, 2015, vol. 10, no. 2, p. 199-220.

4. Макаров В. Л., Бахтизин А. Р. Социальное моделирование - новый компьютерный прорыв (агент-ориентированные модели). М.: Экономика, 2013. 295 с.

5. Dawid H., Delli Gatti D. Agent-Based Macroeconomics. In: Hommes C., LeBaron B. (eds.). Handbook of Computational Economics. Amsterdam, North Holland, 2018.

6. Albrecht J. et al. MOSES Code. Stockholm, IUI, 1989, 354 p.

7. Eliasson G. Modeling the Experimentally Organized Economy: Complex Dynamics in an Empirical Micro-Macro Model of Endogenous Economic Growth. Journal of Economic Behavior and Organization, 1991, vol. 16, no. 1-2, p. 153-182.

8. Eliasson G. Visible Costs, Invisible Benefits. Springer, 2017, 461 p.

9. Cincotti S., Raberto M., Teglio A. The EURACE Macroeconomic Model and Simulator. In: Aoki M., Binmore K., Deakin S., Gintis H. (eds.). Complexity and Institutions: Markets, Norms and Corporations. New York, Palgrave Macmillan, 2012, p. 81-106.

10. Ponta L., Raberto M., Cincotti S. An Agent-Based Stock-Flow Consistent Model of the Sustainable Transition in the Energy Sector. Ecological Economics, 2018, vol. 145, p. 274-300.

11. Raberto M., Ozel B., Ponta L. Teglio A., Cincotti S. From Financial Instability to Green Finance: The Role of Banking and Monetary Policies in the EURACE Model. Journal of Evolutionary Economics, 2019, vol. 29, no. 1, p. 429-465.

12. Teglio A., Mazzocchetti A., Ponta L., Raberto M., Cincotti S. Budgetary Rigour with Stimulus in Lean Times: Policy Advices from an Agent-Based Model. Journal of Economic Behavior and Organization, 2019, vol. 157, p. 59-83.

13. Dawid H., Harting P., Neugart M. Economic Convergence: Policy Implications from a Heterogeneous Agent Model. Journal of Economic Dynamics and Control, 2014, vol. 44, p. 54-80.

14. Harting P. Macroeconomic Stabilization and Long-Term Growth: The Role of Policy Design. Macroeconomic Dynamics, 2019, p. 1-46.

15. Dawid H. et al. Agent-Based Macroeconomic Modeling and Policy Analysis: The Eurace@Unibi Model. In: Chen S., Kaboudan M., Du Y. (eds.). The Oxford Handbook on Computational Economics and Finance. Oxford University Press, 2018, ch. 17, p. 490-519.

16. Dawid H., Harting P., Neugart M. Fiscal Transfers and Regional Economic Growth. Review of International Economics, 2018, vol. 26, p. 651-671.

17. Dawid H., Harting P., Hoog S. van der, Neugart M. Macroeconomics with Heterogeneous Agent Models: Fostering Transparency, Reproducibility and Replication. Journal of Evolutionary Economics. 2019, vol. 29, p. 467-538.

18. Assenza T., Delli Gatti D., Grazzini J. Emergent Dynamics of a Macroeconomic Agent Based Model with Capital and Credit. Journal of Economic Dynamics and Control, 2015, vol. 50, p. 5-28.

19. Delli Gatti D., Gallegati M., Cirillo P., Desiderio S., Gaeo, E. Macroeconom-ics from the Bottom-up. Berlin, Springer-Verlag, 2011, 124 p.

20. Caiani A., Godin A., Caverzasi E., Gallegati M., Kinsella S., Stiglitz J. E. Agent Based-Stock Flow Consistent Macroeconomics: Towards a Benchmark Model. Journal of Economic Dynamics and Control, 2016, vol. 69, p. 375-408.

21. Dosi G., Fagiolo G., Roventini A. An Evolutionary Model of Endogenous Business Cycles. Computational Economics, 2006, vol. 27, no. 1, p. 3-34.

22. Dosi G., Fagiolo G., Roventini A. The Microfoundations of Business Cycles: an Evolutionary, Multi-Agent Model. Journal of Evolutionary Economics, 2008, vol. 18, no. 3-4, p. 413-432.

23. Napoletano M., Dosi G., Fagiolo G., Roventini A. Wage Formation, Investment Behavior and Growth Regimes: An Agent-Based Analysis. Revue de l’OFCE, 2012, vol. 124, p. 235-261.

24. Dosi G. et al. Fiscal and Monetary Policies in Complex Evolving Economies. Journal of Economic Dynamics and Control, 2015, vol. 52, p. 166-189.

25. Seppecher P. Flexibility Of Wages And Macroeconomic Instability In An Agent-Based Computational Model With Endogenous Money. Macroeconomic Dynamics, 2012, vol. 16, no. S2, p. 284-297.

26. Salle I., Seppecher P. Stabilizing an Unstable Complex Economy on the Limitations of Simple Rules. Journal of Economic Dynamics and Control, 2018, vol. 91, p. 289-317.

27. Seppecher P., Salle I., Lang D. Is the Market Really a Good Teacher? Journal of Evolutionary Economics, 2019, vol. 29, p. 299-335.

28. Seppecher P., Salle I., Lavoie, M. What Drives Markups? Evolutionary Pricing in an Agent-Based Stock-Flow Consistent Macroeconomic Model. Industrial and Corporate Change, 2018, vol. 27, no. 6, p. 1045-1067.

29. Mandel A. et al. Agent-Based Dynamics in Disaggregated Growth Models. Documents de travail du Centre d'Economie de la Sorbonne 10077, 2010, 34 p.

30. Mandel A. Agent-Based Dynamics in the General Equilibrium Model. Complexity Economics, 2012, vol. 1, p. 105-121.

31. Wolf S. et al. A Multi-Agent Model of Several Economic Regions. Environmental Modelling & Software, 2013, vol. 44, p. 25-43.

32. Balint T. et al. Complexity and the Economics of Climate Change: a Survey and a Look Forward. Ecological Economics, 2017, vol. 138, p. 252-265.

33. Суслов В. И., Доможиров Д. А., Ибрагимов Н. М., Костин В. С., Мельникова Л. В., Цыплаков А. А. Агент-ориентированная многорегиональная модель «затраты-выпуск» российской экономики // Экономика и математические методы. 2016. Т. 52, № 1. С. 112-131.

34. Суслов В. И., Доможиров Д. А., Костин B. C., Мельникова Л. В., Ибрагимов Н. М., Цыплаков А. А. Опыт агент-ориентированного моделирования пространственных процессов в большой экономике // Регион: экономика и социология. 2014. Т. 84, № 4. С. 32-54.

35. Доможиров Д. А., Ибрагимов Н. М., Мельникова Л. В., Цыплаков А. А. Интеграция подхода «затраты-выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы // Мир экономики и управления. 2017. Т. 17, № 1. С. 86-99.

36. Доможиров Д. А., Ибрагимов Н. М., Мельникова Л. В., Цыплаков А. А. Интеграция подхода «затраты-выпуск» в агент-ориентированное моделирование. Часть 2. Межрегиональный анализ в искусственной экономике // Мир экономики и управления. 2017. Т. 17, № 2. С. 15-25.

37. Гамидов Т. Г., Доможиров Д. А., Ибрагимов Н. М. Равновесные состояния открытой межрегиональной системы, порожденной оптимизационной межрегиональной межотраслевой моделью // Вестник НГУ. Серия: Социально-экономические науки. 2013. Т. 13, № 3. С. 81-94.

38. Суслов В. И., Новикова Т. С., Цыплаков А. А. Моделирование роли государства в пространственной агент-ориентированной модели // Экономика региона. 2016. Т. 12, вып. 3. С. 951-965.

39. Новикова Т. С., Цыплаков А. А. Социальная политика в многоотраслевой агент-ориентированной модели // Экономические и социальные перемены: факты, тенденции, прогноз. 2020. Т. 13, № 3. С. 129-142.


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For citations:


Tsyplakov A.A., Melnikova L.V. Fixed Investments and Macroeconomic Agent-Based Modeling. World of Economics and Management. 2021;21(1):5-28. (In Russ.) https://doi.org/10.25205/2542-0429-2021-21-1-5-28

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