We consider the climate-policy implications of non-constant time preferences when there is no commitment to future policies. The conceptual and quantitative results follow from the observation that, with time-declining discounting, the unusual delays and persistence of climate impacts provide a commitment device for climate policy-makers, overturning a fundamental climate-policy conclusion from...

Both climate change and technical change are uncertain. In this paper we show the importance of including both uncertainties when modeling for policy analysis. We then develop an approach for incorporating uncertainty of technical change into climate change policy analysis. We define and demonstrate a protocol for bottom-up expert assessments about prospects for technologies. We then describe a...

In Reinforcement Learning (RL) the goal is to find a policy π that maximizes the expected future return, calculated based on a scalar reward function R(·) ∈ R. The policy π determines what actions will be performed by the RL agent. Traditionally, the RL problem is formulated in terms of a Markov Decision Process (MDP) or a Partially Observable MDP (POMDP). In this formulation, the policy π is v...

Does Parental Leave Affect Fertility and Return-to-Work? Evidence from a “True Natural Experiment” We study the causal effects of changes in parental leave provisions on fertility and return-towork behavior. We exploit a policy change that took place in 1990 in Austria which extended the maximum duration of parental leave from the child’s first to the child’s second birthday. As parental leave ...

The performance of a given portfolio policy can in principle be evaluated by comparing its expected utility with that of the optimal policy. Unfortunately, the optimal policy is usually not computable, in which case a direct comparison is impossible. In this paper, we solve this problem by using the given portfolio policy to construct an upper bound on the unknown maximum expected utility. This...