In this project, we investigate to which extent seemingly irrational planning decisions are a consequence of how people individually experience the costs and benefits of deliberate decision-making. We start from the empirically-grounded assumption that many sub-optimal decisions arise from being short-sighted when balancing the costs and benefits of a particular decision. To achieve this, we leverage Bayesian Inverse Reinforcement Learning [Ramachandran and Amir, IJCAI 2007] to infer experienced reward functions. In future work, we will investigate personalized interventions based on differences in these experienced costs. This work may result in insights about human decision-making, applicable to a wide range of domains such as public policy, psychiatric treatment, and the field of education.