# On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

arXiv: https://arxiv.org/abs/2001.07362 · HTML: https://mlsanity.com/paper/2001.07362/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems](https://mlsanity.com/paper/2106.08307/) (2021)
- [Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing](https://mlsanity.com/paper/2405.13205/) (2024)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search](https://mlsanity.com/paper/2202.13003/) (2022)
- [Artificial Intelligence for Emergency Response](https://mlsanity.com/paper/2306.10068/) (2023)
- [Decision-making of Emergent Incident based on P-MADDPG](https://mlsanity.com/paper/2203.12673/) (2022)
- [Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems](https://mlsanity.com/paper/2601.03476/) (2026)

## Abstract (arXiv)

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 textitafter 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.