  According to the subjective Bayesian interpretation of quantum theory
(QBism), quantum mechanics is a tool that an agent would be wise to use when
making bets about natural phenomena. In particular, the Born rule is understood
to be a decision-making norm, an ideal which one should strive to meet even if
usually falling short in practice. What is required for an agent to make
decisions that conform to quantum mechanics? Here we investigate how a
realistic (hence non-ideal) agent might deviate from the Born rule in its
decisions. To do so we simulate a simple agent as a reinforcement-learning
algorithm that makes `bets' on the outputs of a symmetric
informationally-complete measurement (SIC) and adjusts its decisions in order
to maximize its expected return. We quantify how far the algorithm's
decision-making behavior departs from the ideal form of the Born rule and
investigate the limiting factors. We propose an experimental implementation of
the scenario using heralded single photons.
