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

TL;DR

This paper develops a quantitative model using absorbing Markov chains to evaluate how information flow impacts disaster response effectiveness, providing actionable insights for stakeholders.

Contribution

It introduces a novel Markov chain-based model to quantify information flow effects on disaster response, incorporating stakeholder interactions and uncertainty.

Findings

Model accurately predicts community satisfaction probabilities.

Provides a framework for evaluating information flow impact.

Demonstrates applicability through a hypothetical example.

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.