  With the introduction of the graph-theoretic time-inconsistent planning model
due to Kleinberg and Oren, it has been possible to investigate the
computational complexity of how a task designer best can support a
present-biased agent in completing the task. In this paper, we study the
complexity of finding a choice reduction for the agent; that is, how to remove
edges and vertices from the task graph such that a present-biased agent will
remain motivated to reach his target even for a limited reward. While this
problem is NP-complete in general, this is not necessarily true for instances
which occur in practice, or for solutions which are of interest to task
designers. For instance, a task designer may desire to find the best task graph
which is not too complicated.
  We therefore investigate the problem of finding simple motivating subgraphs.
These are structures where the agent will modify his plan at most $k$ times
along the way. We quantify this simplicity in the time-inconsistency model as a
structural parameter: The number of branching vertices (vertices with
out-degree at least $2$) in a minimal motivating subgraph.
  Our results are as follows: We give a linear algorithm for finding an optimal
motivating path, i.e. when $k=0$. On the negative side, we show that finding a
simple motivating subgraph is NP-complete even if we allow only a single
branching vertex --- revealing that simple motivating subgraphs are indeed hard
to find. However, we give a pseudo-polynomial algorithm for the case when $k$
is fixed and edge weights are rationals, which might be a reasonable assumption
in practice.
