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Maynard Thompson

Bio: Maynard Thompson is an academic researcher. The author has contributed to research in topics: Discrete event simulation & Quantitative ecology. The author has an hindex of 3, co-authored 4 publications receiving 242 citations.

Papers
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Book
06 Jan 2005
TL;DR: This book discusses model building, linear programming problems and Duality, Sensitivity, and Uncertainty, and the process of Constructing Mathematical Models.
Abstract: 1. BASIC PRINCIPLES. Overview of the Uses of the Term Model. The Process of Constructing Mathematical Models. Types of Mathematical Models. A Classic Example. Axiom Systems and Models. Simulation Models. Practical Aspects of Model Building. 2. MODEL BUILDING: SELECTED CASE STUDIES. Introduction. Mendelian Genetics. Models for Growth Processes. Social Choice. Moving Mobile Homes. A Stratified Population Model. Selected Simulations. Waiting in Line Again! Estimating Parameters and Testing Hypotheses. 3. MARKOV CHAINS. Introduction. The Setting and Some Examples. Basic Properties of Markov Chains. Regular Markov Chains. Absorbing Chains and Applications. 4. SIMULATION MODELS. Introduction. The Simulation Process. Discrete Random Variables. Discrete Event Simulation. Continuous Random Variables. Applications. 5. LINEAR PROGRAMMING MODELS. Introduction. Formulation of Linear Programming Problems. Linear Programming Problems and Duality. Duality, Sensitivity, and Uncertainty. Job Assignment. Networks and Flows. Appendix A: Projects and Presentations. Introduction. Types of Projects. Examples of Projects. Reports and Presentations. Evaluating Project Reports. Sources of Projects.

34 citations


Cited by
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Journal ArticleDOI
TL;DR: A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
Abstract: Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.

3,173 citations

Journal ArticleDOI
TL;DR: This work introduces a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet).
Abstract: We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

709 citations

Journal ArticleDOI
TL;DR: In this article, the authors suggest empirical multivariate studies of landscape change, modelling of individual landscape processes, explicit study of the effect of model scale on model behavior, and scaling-up results of studies, on smaller land areas, that have landscape relevance.
Abstract: Models of landscape change may serve a variety of purposes, from exploring the interaction of natural processes to evaluating proposed management treatments These models can be categorized as either whole landscape models, distributional landscape models, or spatial landscape models, depending on the amount of detail included in the models Distributional models, while widely used, exclude spatial detail important for most landscape ecological research Spatial models require substantial data, now more readily available, via remote sensing, and more easily manipulated, in geographical information systems In spite of these technical advances, spatial modelling is poorly developed, largely because landscape change itself is poorly understood To facilitate further development of landscape models I suggest (1) empirical multivariate studies of landscape change, (2) modelling of individual landscape processes, (3) explicit study of the effect of model scale on model behavior, and (4) ‘scaling-up’ results of studies, on smaller land areas, that have landscape relevance

510 citations

Journal ArticleDOI
TL;DR: Fundamental mechanisms to perform exploration of a city using the multiple transportation layers using random walks on multilayer networks are introduced, and it is shown how the topological structure, together with the navigation strategy, influences the efficiency in exploring the whole structure.
Abstract: Assessing the navigability of interconnected networks (transporting information, people, or goods) under eventual random failures is of utmost importance to design and protect critical infrastructures. Random walks are a good proxy to determine this navigability, specifically the coverage time of random walks, which is a measure of the dynamical functionality of the network. Here, we introduce the theoretical tools required to describe random walks in interconnected networks accounting for structure and dynamics inherent to real systems. We develop an analytical approach for the covering time of random walks in interconnected networks and compare it with extensive Monte Carlo simulations. Generally speaking, interconnected networks are more resilient to random failures than their individual layers per se, and we are able to quantify this effect. As an application––which we illustrate by considering the public transport of London––we show how the efficiency in exploring the multiplex critically depends on layers’ topology, interconnection strengths, and walk strategy. Our findings are corroborated by data-driven simulations, where the empirical distribution of check-ins and checks-out is considered and passengers travel along fastest paths in a network affected by real disruptions. These findings are fundamental for further development of searching and navigability strategies in real interconnected systems.

475 citations

Journal ArticleDOI
TL;DR: It is emphasized that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.

354 citations