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Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com


Balanced Growth in the Structural Dynamic Economic Model SDEM-2

Discontinuity, Nonlinearity, and Complexity 3(3) (2014) 237--253 | DOI:10.5890/DNC.2014.09.003

Dmitry V. Kovalevsky

Nansen International Environmental and Remote Sensing Centre, 14th Line 7, office 49, Vasilievsky Island, 199034 St. Petersburg, Russia

Saint Petersburg State University, Ulyanovskaya 3, 198504 St. Perersburg, Russia

Nansen Environmental and Remote Sensing Center, Thormøhlens gate 47, N-5006 Bergen, Norway

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Abstract

The Structural Dynamic Economic Model SDEM-2, a follow-up of the model SDEM developed earlier, is essentially an actor-based, systemdynamic model of a closed economy evolving under conditions of conflict of interests of two powerful aggregated actors: entrepreneurs and wageearners. We derive the model equations applicable to both the balanced and unbalanced growth paths, and then study the balanced growth (with neither idle physical capital nor unemployment). Wefirst consider an inflexible control strategy of entrepreneurs for deterministic and stochastic cases, and then turn to a more sophisticated nonlinear control strategy. We also solve a simple optimization problem by calculating the (time-independent) value of model control parameter maximizing the discounted dividend of entrepreneurs. In view of simplicity of model equations, exact analytical solutions can be obtained in many cases, other cases being studied semianalytically. Even the simplest versions of SDEM-2 are able to produce rather versatile trajectories of the economy, dependent on the values of model parameters and initial conditions.

Acknowledgments

The author is indebted to Klaus Hasselmann for helpful comments. This study was supported by the Russian Foundation for Basic Research (Projects No. 10-06-00369-a and 12-06-00381-a). An earlier version of this paper appeared as a conference paper (Ref. [16]) but was then substantially updated.

References

  1. [1]  Barro, R.J. and Sala-i-Martin, X. (1995), Economic Growth, McGraw-Hill.
  2. [2]  Hasselmann, K. (2009), Simulating human behavior in macroeconomic models applied to climate change, European Climate Forum, ISBN: 978-3-941663-03-9. [http://www.globalclimateforum.org/fileadmin/ecfdocuments/ publications/ecf-working-papers/hasselmann simultating-human-behavior-in-macroeconomic-modelsapplied- to-climate-change.pdf]
  3. [3]  Kovalevsky, D.V. and Hasselmann, K. (2014),A hierarchy of out-of-equilibrium actor-based system-dynamic nonlinear economic models, Discontinuity, Nonlinearity, and Complexity, 3(3), 303-319.
  4. [4]  Barth, V. (2003), Integrated assessment of climate change using structural dynamicmodels, Ph.D. Thesis,Max-Planck-Institut für Meteorologie, Hamburg, 2003. [http://www.mpimet.mpg.de/fileadmin/publikationen/Ex91.pdf]
  5. [5]  Kovalevsky, D.V. (2011), Macroeconomic dynamics modeled in SDEM-2: Can self-interested business prefer stagnation to growth? Business-Inform (Ukraine), Iss. 5(1), 14-17.
  6. [6]  Hasselmann, K. (2013), Detecting and responding to climate change, Tellus B, 65, 20088-20104.
  7. [7]  Hasselmann, K. (2010), The climate change game, Nature Geoscience, 3, 511-512.
  8. [8]  Hasselmann, K. and Kovalevsky, D.V. (2013), Simulating animal spirits in actor-based environmental models, Environmental Modelling & Software, 44, 10-24.
  9. [9]  Hasselmann, K. and Voinov, A. (2011), The actor driven dynamics of decarbonization, pp. 131-159, in Reframing the Problem of Climate Change. From Zero Sum Game to Win-Win Solutions, eds. K. Hasselmann, C. Jaeger, G. Leipold, D. Mangalagiu, J.D. Tabara, Earthscan, 272 pp.
  10. [10]  Weber, M., Barth V., and Hasselmann, K. (2005), A multi-actor dynamic integrated assessment model (MADIAM) of induced technological change and sustainable economic growth, Ecological Economics, 54, 306-327.
  11. [11]  Beyond Economic Growth. Meeting the Challenges of Global Development. The World Bank Group, 2000. [http://www.worldbank.org/depweb/beyond/beyond.htm]
  12. [12]  Folloni, G. and Vittadini, G. (2010), Human capital measurement: a survey, Journal of Economic Surveys, 24(2), 248-279.
  13. [13]  Agiomirgianakis, G., Asteriou, D. and Monastiriotis, V. (2002), Human capital and economic growth revisited: a dynamic panel data study, International Advances in Economic Research, 8(3), 177-187.
  14. [14]  Jones, G. and Schneider, W.J. (2006), Intelligence, human capital, and economic growth: a Bayesian averaging of classical estimates (BACE) approach, Journal of Economic Growth, 11(1), 71-93.
  15. [15]  Tallman, E.W. and Wang P. (1994), Human capital and endogenous growth: evidence from Taiwan, Journal of Monetary Economics, 34, 101-124.
  16. [16]  Kovalevsky, D.V. (2011), Deterministic and stochastic growth in the Structural Dynamic Economic Model SDEM-2. Proceedings of XVI Conference on Dynamics, Economic Growth, and International Trade (DEGIT-XVI), St. Petersburg, Russia, 8-9 September 2011. [http://www.degit.ifw-kiel.de/papers/folder.2011-09-12.2623700498/c016 043.pdf]
  17. [17]  Petrov, Yu.P. (1987), Synthesis of Optimal Control Systems under Incompletely Known Perturbing Forces, Leningrad, LGU Publishing House, (in Russian).
  18. [18]  Merton, R.C. (1975), An asymptotic theory of growth under uncertainty, The Review of Economic Studies, 42(3), 375-393.
  19. [19]  Pugachev, V.S. and Sinitsyn, I.N. (1985), Stochastic Differential Systems, Moscow, Nauka, (in Russian).