Hierarchical and Asymptotic Optimal Control Models for Economic Sustainable Development

  • Alain B. Haurie
Chapter
Part of the Advances in Computational Management Science book series (AICM, volume 7)

Abstract

In this brief paper one shows the relevance of asymptotic control theory to the study of economic sustainable development. One also proposes a modeling framework where sustainable economic development is represented through a paradigm of optimal stochastic control with two time-scales. This shows that several contributions of Prof. Sethi, in the domain of hierarchical and multi-level control models in manufacturing and resource management can also serve to better understand the stakes of sustainability in economic growth and to assess long term environmental policies.

Keywords

Discount Rate Climate Mode Stochastic Control Problem Economic Sustainable Development Optimal Control Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brock W. and J. Scheinkman, Global asymptotic stability of optimal control systems with application to the theory of economic growth, Journal of Economic Theory Vol. 12, pp. 164–190, 1976.CrossRefGoogle Scholar
  2. Brock W. and A. Haurie, On existence of overtaking optimal trajectories over an infinite time horizon, Mathematics of Operations Research Vol. 1, pp. 337–346, 1976.Google Scholar
  3. Carlson D., A. Haurie and A. Leizarowitz, Infinite Horizon Optimal Control: Deterministic and stochastic Systems, Springer Verlag, 1994.Google Scholar
  4. Cass D., Optimum growth in aggregative model of capital accumulation, Review of Economic Studies Vol. 32, pp. 233–240, 1965.Google Scholar
  5. Drouet L., N. Edwards and A. Haurie, Coupling Climate and Economic Models in a Cost-Benefit Framework: A Convex Optimization Approach, Environmental Modeling and Assesment, to appear.Google Scholar
  6. Feinstein C.D and D.G. Luenberger, Analysis of the asymptotic behavior of optimal control trajectories, SIAM Journal on Control and Optimization, Vol. 19, pp. 561–585, 1981.CrossRefGoogle Scholar
  7. Filar J.A., V. Gaitsgory and A. Haurie, Control of Singularly Perturbed Hybrid Stochastic Systems, IEEE Transactions on Automatic Control, Vol. 46, no. 2, pp 179–190, 2001.CrossRefGoogle Scholar
  8. Filar J.A. and A. Haurie, Singularly perturbed hybrid stochastic Systems, in G. Yin and Qing Zhang Eds. Mathematics of Stochastic Manufacturing Systems, Lectures in Applied Mathematics, American Mathematical Society, Vol. 33, pp. 101–126, 1997.Google Scholar
  9. Haurie A., Integrated assessment modeling for global climate change: an infinite horizon viewpoint, Environmental Modeling and Assesment, 2003.Google Scholar
  10. Haurie A., Turnpikes in multi-discount rate environments and GCC policy evaluation, in G. Zaccour ed. Optimal Control and Differential Games: Essays in Honor of Steffen Jørgensen, Kluwer Academic Publisher, 2002.Google Scholar
  11. McKenzie L., Turnpike theory, Econometrica, Vol. 44, pp. 841–866, Nov. 1976.Google Scholar
  12. Nordhaus W.D., An optimal transition path for controlling greenhouse gases, Science, Vol. 258, pp. 1315–1319, Nov. 1992.Google Scholar
  13. Nordhaus W.D., Managing the Global Commons: The Economics of Climate Change, MIT Press, Cambridge, Mass., 1994.Google Scholar
  14. Nordhaus W.D. and J. Boyer, Warming the World: Economic Models of Global Warming, MIT Press, Cambridge, Mass., 2000.Google Scholar
  15. Portney P.R. and Weyant J., eds., Discounting and Intergenerational Effects, Resources for the Future, Washington, DC, 1999.Google Scholar
  16. Puterman M., eds., Markov Decision Processes, Wiley Interscience, 1994.Google Scholar
  17. Ramsey F., A mathematic theory of saving, Economic Journal: Vol. 38, pp. 543–549, 1928.Google Scholar
  18. Rockafellar T., Saddle points of hamiltonian systems in convex problems of Lagrange, Journal of Optimization Theory and Applications, Vol. 12, pp 367–399, 1973.CrossRefGoogle Scholar
  19. Sethi J.P. and Q. Zhang., Hierarchical Decision Making in Stochastic Manufacturing Systems, Birkhaäuser Boston, Cambridge MA, 1995.Google Scholar
  20. Sethi J.P. and Q. Zhang., Multilevel hierarchical decision making in stochastic marketing-production systems, SIAM Journal on Control and Optimizations, Vol. 33, 1995.Google Scholar
  21. Sethi J.P., Some insights into near-optimal plans for stochastic manufacturing systems, in G. Yin and Qing Zhang Eds. Mathematics of Stochastic Manufacturing Systems, Lectures in Applied Mathematics, American Mathematical Society, Vol. 33, pp. 287–316, 1997.Google Scholar
  22. Veinott A.F., Production planning with convex costs: a parametric study, Management Science, Vol. 10, pp 441–460, 1964.Google Scholar
  23. Weitzman, M.L., Why the Far-Distant Future Should Be Discounted at Its Lowest Possible Rate, Journal of Environmental Economics and Management: pp. 201–208, 1998.Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Alain B. Haurie
    • 1
  1. 1.Logilab-HECUniversity of GenevaSwitzerland

Personalised recommendations