Understanding Police Force Resource Allocation using Adversarial Optimal Transport with Incomplete Information
<|reference_start|>Understanding Police Force Resource Allocation using Adversarial Optimal Transport with Incomplete Information: Adversarial optimal transport has been proven useful as a mathematical formulation to model resource allocation problems to maximize the efficiency of transportation with an adversary, who modifies the data. It is often the case, however, that only the adversary knows which nodes are malicious and which are not. In this paper we formulate the problem of seeking adversarial optimal transport into Bayesian games. We construct the concept of Bayesian equilibrium and design a distributed algorithm that achieve those equilibria, making our model applicable to large-scale networks. Keywords: game theory, crime control, Markov games<|reference_end|>
@article{hu2024understanding,
    title={Understanding Police Force Resource Allocation using Adversarial Optimal
  Transport with Incomplete Information},
    author={Yinan Hu, Juntao Chen, Quanyan Zhu},
    journal={arXiv preprint arXiv:2403.00972},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00972},
    primaryClass={cs.GT cs.SY eess.SY}
}