  Human decision-making deviates from the optimal solution, that maximizes
cumulative rewards, in many situations. Here we approach this discrepancy from
the perspective of bounded rationality and our goal is to provide a
justification for such seemingly sub-optimal strategies. More specifically we
investigate the hypothesis, that humans do not know optimal decision-making
algorithms in advance, but instead employ a learned, resource-bounded
approximation. The idea is formalized through combining a recently proposed
meta-learning model based on Recurrent Neural Networks with a resource-bounded
objective. The resulting approach is closely connected to variational inference
and the Minimum Description Length principle. Empirical evidence is obtained
from a two-armed bandit task. Here we observe patterns in our family of models
that resemble differences between individual human participants.
