Understanding the Dynamics of Information Flow During Disaster Response Using Absorbing Markov Chains

06/11/2020 ∙ by Yitong Li, et al. ∙ 0

This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of delivering federal assistance to the community. At the core of the model is a specialized absorbing Markov chain that models the process of delivering federal assistance to the community while considering stakeholder interactions and information flow uncertainty. Based on the model, the probability of community satisfaction is computed to reflect the effectiveness of the disaster response process. An illustrative example is provided to demonstrate the applicability and interpretability of the derived model. Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.



There are no comments yet.


page 8

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.