  Emergency Response Management (ERM) is a critical problem faced by
communities across the globe. Despite this, it is common for ERM systems to
follow myopic decision policies in the real world. Principled approaches to aid
ERM decision-making under uncertainty have been explored but have failed to be
accepted into real systems. We identify a key issue impeding their adoption ---
algorithmic approaches to emergency response focus on reactive, post-incident
dispatching actions, i.e. optimally dispatching a responder \textit{after}
incidents occur. However, the critical nature of emergency response dictates
that when an incident occurs, first responders always dispatch the closest
available responder to the incident. We argue that the crucial period of
planning for ERM systems is not post-incident, but between incidents. This is
not a trivial planning problem --- a major challenge with dynamically balancing
the spatial distribution of responders is the complexity of the problem. An
orthogonal problem in ERM systems is planning under limited communication,
which is particularly important in disaster scenarios that affect communication
networks. We address both problems by proposing two partially decentralized
multi-agent planning algorithms that utilize heuristics and exploit the
structure of the dispatch problem. We evaluate our proposed approach using
real-world data, and find that in several contexts, dynamic re-balancing the
spatial distribution of emergency responders reduces both the average response
time as well as its variance.
