  Most people struggle with prioritizing work. While inexact heuristics have
been developed over time, there is still no tractable principled algorithm for
deciding which of the many possible tasks one should tackle in any given day,
month, week, or year. Additionally, some people suffer from cognitive biases
such as the present bias, leading to prioritization of their immediate
experience over long-term consequences which manifests itself as
procrastination and inefficient task prioritization. Our method utilizes
optimal gamification to help people overcome these problems by incentivizing
each 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 added
services for helping people decide which tasks should and should not be done
when there is not enough time to do everything. To improve the efficiency and
scalability of the to-do list solver, we designed a hierarchical procedure that
tackles the problem from the top-level goals to fine-grained tasks. We test the
accuracy of the incentivised to-do list by comparing the performance of the
strategy with the points computed exactly using Value Iteration for a variety
of case studies. These case studies were specifically designed to cover the
corner cases to get an accurate judge of performance. Our method yielded the
same performance as the exact method for all case studies. To demonstrate its
functionality, we released an API that makes it easy to deploy our method in
Web and app services. We assessed the scalability of our method by applying it
to to-do lists with increasingly larger numbers of goals, sub-goals per goal,
hierarchically nested levels of subgoals. We found that the method provided
through 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.
