Related papers: Understanding the Dynamics of Information Flow Dur…
We introduce a continuous-time Markov chain framework for estimating population size from multi-list data, which allows directional interactions to be modelled and can accommodate absorbing lists, such as death records, or more general data…
In this paper we present a versatile method for the investigation of interaction networks and show how to use it to assess effects of indirect interactions and feedback loops. The method allows to evaluate the impact of optimization…
The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries' positioning in global value chains. We…
Situational awareness is crucial for effective disaster management. However, obtaining information about the actual situation is usually difficult and time-consuming. While there has been some effort in terms of incorporating the affected…
The paper is devoted to the problem of disaster mitigation. It develops an emergent mechanism of resource redistribution aimed at recovering of a socio-technological system affected by a large scale disaster. The basic requirements to the…
This paper establishes a Markov chain model as a unified framework for understanding information consumption processes in complex networks, with clear implications to the Internet and big-data technologies. In particular, the proposed model…
We present longitudinal analysis of the evolution of inter-organizational disaster coordination networks during natural disasters. We suggest that social networks are a useful paradigm for exploring this complex phenomenon from both…
Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential…
The concepts of probability, statistics and stochastic theory are being successfully used in structural engineering. Markov Chain modelling is a simple stochastic process model that has found its application in both describing stochastic…
The importance of considering contextual probabilities in shaping response patterns within psychological testing is underscored, despite the ubiquitous nature of order effects discussed extensively in methodological literature. Drawing from…
We study the effectiveness of recovery strategies for a dynamic model of failure spreading in networks. These strategies control the distribution of resources based on information about the current network state and network topology. In…
Dynamical systems are often subject to forcing or changes in their governing parameters and it is of interest to study how this affects their statistical properties. A prominent real-life example of this class of problems is the…
The success of a disaster relief and response process is largely dependent on timely and accurate information regarding the status of the disaster, the surrounding environment, and the affected people. This information is primarily provided…
This paper presents a case for the adoption of an information-centric architecture for a global disaster management system. Drawing from a case study of the 2010/2011 Queensland floods, we describe the challenges in providing every…
In classical Markov Decision Processes (MDPs), action costs and transition probabilities are assumed to be known, although an accurate estimation of these parameters is often not possible in practice. This study addresses MDPs under cost…
In this paper, we explore the formation of network relationships among disaster relief agencies during the process of responding to an unexpected event. The relationship is investigated through variables derived from the policy network…
Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient…
Inferring how an epidemic will progress and what actions to take when presented with limited information is of critical importance for epidemiologists and health professionals. In real world settings, epidemiology data can be scarce or…
Natural disasters affect hundreds of millions of people worldwide every year. Early warning, humanitarian response and recovery mechanisms can be improved by using big data sources. Measuring the different dimensions of the impact of…
Spreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumor spreading are among the most…