NDLI: An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

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An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Content Provider	Springer Nature Link
Author	Webster, Mort Santen, Nidhi Parpas, Pas
Copyright Year	2012
Abstract	Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.
Starting Page	339
Ending Page	362
Page Count	24
File Format	PDF
ISSN	1619697X
Journal	Computational Management Science
Volume Number	9
Issue Number	3
e-ISSN	16196988
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-05-09
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Climate policy analysis Approximate dynamic programming Decision dependent uncertainty Stochastic dynamic programming Endogenous uncertainty Operations Research/Decision Theory Optimization
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research Management Information Systems Information Systems Statistics, Probability and Uncertainty

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