Group for Research in Decision Analysis

G-2014-50

A dynamic game of emissions pollution with uncertainty and learning

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We introduce learning in a dynamic game of international pollution, with ecological uncertainty. We characterize and compare the feedback non-cooperative emissions strategies of players when the players do not know the distribution of ecological uncertainty but they gain information (learn) about it. We then compare our learning model with the benchmark model of full information, where players know the distribution of ecological uncertainty. We find that uncertainty due to anticipative learning induces a decrease in total emissions. Further, depending on the beliefs distribution and bias, the effect of structural uncertainty could be either an increase, decrease or even no change in the emissions of individual players and in the total emissions. Moreover, we obtain that if a player's beliefs change toward more optimistic views or if she feels that the situation is less risky, then she increases her emissions while others react to this change and decrease their emissions; however, the latter effect never overtakes the former and, as a result, total emissions increase.

, 22 pages