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Most people struggle with prioritizing work. While inexact heuristics havebeen developed over time, there is still no tractable principled algorithm fordeciding which of the many possible tasks one should tackle in any given day,month, week, or year. Additionally, some people suffer from cognitive biasessuch as the present bias, leading to prioritization of their immediateexperience over long-term consequences which manifests itself asprocrastination and inefficient task prioritization. Our method utilizesoptimal gamification to help people overcome these problems by incentivizingeach task by a number of points that convey how valuable it is in the long-run.We extend the previous version of our optimal gamification method with addedservices for helping people decide which tasks should and should not be donewhen there is not enough time to do everything. To improve the efficiency andscalability of the to-do list solver, we designed a hierarchical procedure thattackles the problem from the top-level goals to fine-grained tasks. We test theaccuracy of the incentivised to-do list by comparing the performance of thestrategy with the points computed exactly using Value Iteration for a varietyof case studies. These case studies were specifically designed to cover thecorner cases to get an accurate judge of performance. Our method yielded thesame performance as the exact method for all case studies. To demonstrate itsfunctionality, we released an API that makes it easy to deploy our method inWeb and app services. We assessed the scalability of our method by applying itto to-do lists with increasingly larger numbers of goals, sub-goals per goal,hierarchically nested levels of subgoals. We found that the method providedthrough our API is able to tackle fairly large to-do lists having a 576 tasks.This indicates that our method is suitable for real-world applications.

Optimal To-Do List Gamification for Long Term Planning