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Control Flow Information Analysis in Process Model Matching Techniques
Online Appendix to: "Analyzing Control Flow Information to Improve the E...
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Markov Chain Models of Refugee Migration Data
The application of Markov chains to modelling refugee crises is explored...
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Reduction of Markov Chains using a Value-of-Information-Based Approach
In this paper, we propose an approach to obtain reduced-order models of ...
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Markov chain aggregation and its application to rule-based modelling
Rule-based modelling allows to represent molecular interactions in a com...
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A metric on directed graphs and Markov chains based on hitting probabilities
The shortest-path, commute time, and diffusion distances on undirected g...
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Action and perception for spatiotemporal patterns
This is a contribution to the formalization of the concept of agents in ...
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Understanding the Dynamics of Information Flow During Disaster Response Using Absorbing Markov Chains
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.
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