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

• URL: http://arxiv.org/abs/2006.06510v2
• Date: Sun, 5 Jul 2020 22:43:47 GMT
• Title: Understanding the Dynamics of Information Flow During Disaster Response Using Absorbing Markov Chains
• Authors: Yitong Li and Wenying Ji
• Abstract summary: This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of disaster response. At the core of the model is a specialized absorbing Markov chain that models the process of delivering federal assistance to the community.
• Score: 15.97186478109836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of disaster response. 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. Using the proposed model, the probability of community satisfaction is computed to reflect the effectiveness of disaster response. A hypothetical example is provided to demonstrate the applicability and interpretability of the derived quantitative model. Practically, the research provides governmental stakeholders interpretable insights for evaluating the impact of information flow on their disaster response effectiveness so that critical stakeholders can be targeted proactive actions for enhanced disaster response.

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