  This paper studies an instance of the multi-armed bandit (MAB) problem,
specifically where several causal MABs operate chronologically in the same
dynamical system. Practically the reward distribution of each bandit is
governed by the same non-trivial dependence structure, which is a dynamic
causal model. Dynamic because we allow for each causal MAB to depend on the
preceding MAB and in doing so are able to transfer information between agents.
Our contribution, the Chronological Causal Bandit (CCB), is useful in discrete
decision-making settings where the causal effects are changing across time and
can be informed by earlier interventions in the same system. In this paper, we
present some early findings of the CCB as demonstrated on a toy problem.
