Common cold outbreaks: A network theory approach

In this study, at first we evaluated the network structure in social encounters by which respiratory diseases can spread. We considered common-cold and recorded a sample of human population and actual encounters between them. Our results show that the database structure presents a great value of clustering. In the second step, we evaluated dynamics of disease spread with SIR model by assigning a function to each node of the structural network. The rate of disease spread in networks was observed to be inversely correlated with characteristic path length. Therefore, the shortcuts have a significant role in increasing spread rate. We conclude that the dynamics of social encounters’ network stands between the random and the lattice in network spectrum. Although in this study we considered the period of common-cold disease for network dynamics, it seems that similar approaches may be useful for other airborne diseases such as SARS.     

Nonlinear microscale interactions in the kinetic theory of active particles

The aim of this note is to examine the sources of nonlinearity arising in the kinetic theory of active particles. We show how nonlinearities enter the different terms of the theory, giving rise to possible developments toward the modeling of different types of complex systems, mainly living and social ones, where proliferative–destructive processes must be included. Finally, some research perspectives are discussed.     

NETEST: Estimating a Terrorist Network's Structure—Graduate Student Best Paper Award, CASOS 2002 Conference

Since the events of September 11, 2001, the United States has found itself engaged in an unconventional and asymmetric form of warfare against elusive terrorist organizations. Defense and investigative organizations require innovative solutions that will assist them in determining the membership and structure of these organizations. Data on covert organizations are often in the form of disparate and incomplete inferences of memberships and connections between members. NETEST is a tool that combines multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network's structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network's size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose what if scenarios to destabilize a network and predict its evolution over time.     

The ecology of instructional teacher leadership

Instructional teacher leadership, in which classroom teachers intentionally influence the practice of their colleagues, is a complex social dynamic. In this article, we argue for the use of an analytic framework that acknowledges this complexity, and we apply it to three cases of teacher leaders, all in the context of elementary and middle grades mathematics instruction. In each case, Urie Bronfenbrenner’s ecological systems theory, complemented by social network analysis, proves useful for understanding the unique circumstances and the leadership activities in which the individual is able to engage. This comprehensive framework accounts for factors ranging from those internal to the individual to those inherent in the society at large, viewing the teacher leader as part of a complex social ecosystem of other individuals, institutions, policies and cultural norms. Following a brief overview of the theory, we apply it to the three cases in sequence. We conclude with implications for the field, both those who study instructional teacher leadership and those who train and support teacher leaders.     

A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

On maximal entries in the principal eigenvector of graphs

For an undirected, connected graph it is well known that an eigenvector belonging to the principal eigenvalue of G can be given such that all entries are positive. We ask whether this vector carries information on the structure of the graph and approach this question by investigating the values that can occur in its maximal entry.     

Methodological Issues in Studying Effects of Networks in Organizations

Three methodological issues are discussed that are important for the analysis of data on networks in organizations. The first is the two-level nature of the data: individuals are nested in organizations. This can be dealt with by using multilevel statistical methods. The second is the complicated nature of statistical methods for network analysis. The third issue is the potential of mathematical modeling for the study of network effects and network evolution in organizations. Two examples are given of mathematical models for gossip in organizations. The first example is a model for cross-sectional data, the second is a model for longitudinal data that reflect the joint development of network structure and individual behavior tendencies.     

Destabilization of covert networks

Covert networks are often difficult to reason about, manage and destabilize. In part, this is because they are a complex adaptive system. In addition, this is due to the nature of the data available on these systems. Making these covert networks less adaptive, more predictable, more consistent will make it easier to contain or constrain their activity. But, how can we inhibit adaptation? Herein, covert networks are characterized as dynamic multi-mode multi-plex networks. Dynamic network analysis tools are used to assess their structure and identify effective destabilization strategies that inhibit the adaptivity of these groups.     

A class of mixture of experts models for general insurance: Theoretical developments

In the Property and Casualty (P&C) ratemaking process, it is critical to understand the effect of policyholders’ risk profile to the number and amount of claims, the dependence among various business lines and the claim distributions. To include all the above features, it is essential to develop a regression model which is flexible and theoretically justified. Motivated by the issues above, we propose a class of logit-weighted reduced mixture of experts (LRMoE) models for multivariate claim frequencies or severities distributions. LRMoE is interpretable, as it has two components: Gating functions, which classify policyholders into various latent sub-classes; and Expert functions, which govern the distributional properties of the claims. Also, upon the development of denseness theory in regression setting, we can heuristically interpret the LRMoE as a “fully flexible” model to capture any distributional, dependence and regression structures subject to a denseness condition. Further, the mathematical tractability of the LRMoE is guaranteed since it satisfies various marginalization and moment properties. Finally, we discuss some special choices of expert functions that make the corresponding LRMoE “fully flexible”. In the subsequent paper (Fung et al., 2019b), we will focus on the estimation and application aspects of the LRMoE.     

Equation-Free multiscale computational analysis of individual-based epidemic dynamics on networks

The surveillance, analysis and ultimately the efficient long-term prediction and control of epidemic dynamics appear to be some of the major challenges nowadays. Detailed individual-based mathematical models on complex networks play an important role towards this aim. In this work, it is shown how one can exploit the Equation-Free approach and optimization methods such as Simulated Annealing to bridge detailed individual-based epidemic models with coarse-grained, system-level analysis within a pair-wise representation perspective. The proposed computational methodology provides a systematic approach for analyzing the parametric behavior of complex/multiscale epidemic simulators much more efficiently than simply simulating forward in time. It is shown how steady state and (if required) time-dependent computations, stability computations, as well as continuation and numerical bifurcation analysis can be performed in a straightforward manner. The approach is illustrated through a simple individual-based SIRS epidemic model deploying on a random regular connected graph. Using the individual-based simulator as a black box coarse-grained timestepper and with the aid of Simulated Annealing I compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level.     

Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection

We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions arising from Bayesian variable selection problems. Point-mass mixture priors are commonly used in Bayesian variable selection problems in regression. However, for generalized linear and nonlinear models where the conditional densities cannot be obtained directly, the resulting mixture posterior may be difficult to sample using standard MCMC methods due to multimodality. We introduce an adaptive MCMC scheme that automatically tunes the parameters of a family of mixture proposal distributions during simulation. The resulting chain adapts to sample efficiently from multimodal target distributions. For variable selection problems point-mass components are included in the mixture, and the associated weights adapt to approximate marginal posterior variable inclusion probabilities, while the remaining components approximate the posterior over nonzero values. The resulting sampler transitions efficiently between models, performing parameter estimation and variable selection simultaneously. Ergodicity and convergence are guaranteed by limiting the adaptation based on recent theoretical results. The algorithm is demonstrated on a logistic regression model, a sparse kernel regression, and a random field model from statistical biophysics; in each case the adaptive algorithm dramatically outperforms traditional MH algorithms. Supplementary materials for this article are available online.     

复杂非线性系统的控制综合与动态分析

随着当代科学和技术的发展,复杂非线性系统和控制理论研究不断深入,应用领域不断扩大,已影响了科技和社会领域的许多方面.针对当今该领域的研究难点和重点,本文集中讨论了复杂非线性系统控制研究中的3个关键问题,包括非光滑系统控制、网络化系统控制、临界态分析与控制.一方面是对在相关研究领域中的部分科研成果做一个简要的总结;另一方面,想借此机会在自动化学会控制理论专业委员会成立50周年之际向专业委员会表示感谢和祝贺.     

Generalized Moment Theory and Bayesian Robustness Analysis for Hierarchical Mixture Models

In applications of Bayesian analysis one problem that arises is the evaluation of the sensitivity, or robustness, of the adopted inferential procedure with respect to the components of the formulated statistical model. In particular, it is of interest to study robustness with respect to the prior, when this latter cannot be uniquely elicitated, but a whole class Γ of probability measures, agreeing with the available information, can be identified. In this situation, the analysis of robustness consists of finding the extrema of posterior functionals under Γ. In this paper, we provide a theoretical framework for the treatment of a global robustness problem in the context of hierarchical mixture modeling, where the mixing distribution is a random probability whose law belongs to a generalized moment class Γ. Under suitable conditions on the functions describing the problem, the solution of this latter coincides with the solution of a linear semi-infinite programming problem.     

Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available

A strain-specific vaccine is unlikely to be available in the early phases of a potential H5N1 avian influenza pandemic. It could be months and at the current production rate may not provide timely protection to the population. Intervention strategies that control the spread of infection will be necessary in this situation, such as the use of the US stockpile of antiviral medication coupled with a 6-month school closure. The agent-based simulation model, EpiSimS, was used to assess the impact of this intervention strategy followed by three different vaccine approaches: (1) 2-dose, 80% effective, (2) 1-dose, 30% effective, and (3) 1 dose, 80% effective. Simulations show that the combination of antivirals, school closures, and a strain-specific vaccine can reduce morbidity and mortality while in effect. A significant second infection wave can occur with current vaccine technology once school closures are relaxed, though an ideal vaccine is able to contain it. In our simulations, worker absenteeism increases in all cases mostly attributed to household adults staying home with children due to the school closures.

Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models

Time series are found widely in engineering and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time series. We model the domain as a dynamic multiply sectioned Bayesian network (DMSBN) and populate the domain by a set of proprietary, cooperative agents. We propose an algorithm suite that allows the agents to perform one-step forecasts with distributed probabilistic inference. We show that as long as the DMSBN is structural time-invariant (possibly parametric time-variant), the forecast is exact and its time complexity is exponentially more efficient than using dynamic Bayesian networks (DBNs). In comparison with independent DBN-based agents, multiagent DMSBNs produce more accurate forecasts. The effectiveness of the framework is demonstrated through experiments on a supply chain testbed.     

Computational modeling for reasoning about the social behavior of humans

The number of computationally-based models of human social behavior is growing rapidly. In fact, the current ease of programming is resulting in a plethora of tools with impressive interfaces but little theoretical power under the hood. Further, the overabundance of new toolkits for building models is facilitating the excessively rapid growth of simple proof-of-concept, or intellective, models. The current state of models range from the simplistic to the elaborate, from the conceptual to the empirical, and from the purely notional to the validatable. This review briefly describes the state of human social behavioral modeling. Key issues surrounding analysis and validation are discussed.

基于复杂网络理论的含分布式发电的电力网络脆弱度评估

基于复杂网络理论研究含分布式发电(DG, Distributed Generation)的电力网络脆弱度评估问题,有针对性地提出三类脆弱度评估指标,其中基于结构的脆弱度指标能够体现网络拓扑和节点功率对系统供电效率的影响;攻击脆弱度指标可用于评估系统抵御节点和线路移除的能力;基于运行方式的脆弱度指标能够反映整个电网有功功率在传输距离上的均衡度.仿真算例验证了所提指标的有效性和DG对于改善系统功率传输性能与提高抗干扰能力方面的作用.