Which information is worth considering depends on how much effort it would take to acquire and process it. Fromthis perspective peoples tendency to neglect considering the long-term consequences of their actions (present bias) mightreflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validatethe use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costsof planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) byparameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomesmay be associated with both the present bias and acting without planning. Our results highlight testing the causal effectsof the cost of planning on both present bias and mental effort avoidance as a promising direction for future work.

Teaching clever heuristics is a promising approach to improvedecision-making. We can leverage machine learning to dis-cover clever strategies automatically. Current methods requirean accurate model of the decision problems people face inreal life. But most models are misspecified because of lim-ited information and cognitive biases. To address this prob-lem we develop strategy discovery methods that are robustto model misspecification. Robustness is achieved by model-ing model-misspecification and handling uncertainty about thereal-world according to Bayesian inference. We translate ourmethods into an intelligent tutor that automatically discoversand teaches robust planning strategies. Our robust cognitivetutor significantly improved human decision-making when themodel was so biased that conventional cognitive tutors were nolonger effective. These findings highlight that our robust strat-egy discovery methods are a significant step towards leverag-ing artificial intelligence to improve human decision-makingin the real world.