Uncertainty and learning in sequential decision-making : the case of climate policy

The debate over a policy response to global climate change has been and continues to be deadlocked between 1) the view that the impacts of climate change are too uncertain and so any policy response should be delayed until we learn more, and 2) the view that we cannot wait to resolve the uncertainty because climate change is irreversible so we must take precautionary measures now. The objective of this dissertation is to sort out the role of waiting for better information in choosing an appropriate level of emissions abatement activities today under uncertainty. In this dissertation, we construct two-period sequential decision models to represent the choice of a level of emissions abatement over the next decade and another choice for the remainder of this century, both empirical models based on a climate model of intermediate complexity, and analytical dynamic programming models. Using the analytical models, we will show that for learning to have an influence on the decision before the learning occurs, an interaction must be present between strategies in the two decision periods. We define an "interaction" as the dependence of the marginal cost or marginal damage of the future decision on today's decision. When an interaction is present and is uncertain, the ability to learn will introduce a bias in the optimal first period strategy, relative to the optimal strategy if the uncertainty would never be reduced. In general, the bias from learning can be either in the direction of higher or lower emissions, depending on the sign of the interaction and the probability distribution over damage losses relative to abatement costs.

Department dc:contributor.department

Massachusetts Institute of Technology. Technology, Management, and Policy Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2000

Author dc:creator

• Webster, Mort David

Advisor dc:contributor.advisor

• Henry D. Jacoby, Gordon M. Kaufman and Gregory J. McRae.

• Technology, Management, and Policy Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/8870

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8870