Micro-macro dynamics of the online opinion evolution: an asynchronous network model approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper investigates the complex relationship between endogenous and exogenous, deterministic and stochastic stimulating factors in public opinion dynamics. An asynchronous multi-agent network model is proposed to explore the interaction mechanism between individual opinions and the public opinion in online multi-agent network community, including both the micro and the macro patterns of opinion evolution. In addition, based on random network models, a novel algorithm is provided for opinion evolution prediction. The model property analysis and numerical experiments show that the proposed asynchronous multi-agent network model can assimilate and explain some interesting phenomena that are observed in the real world. Further case studies with numerical simulation and real-world applications confirm the feasibility and flexibility of the proposed model in public opinion analysis. The results challenge the common perception that mass media or opinion facilitators play the fundamental role in controlling the development trends of public opinion. This study shows that the formation and evolution of public opinion in the presence of opinion leaders depend also on an individual’s emotional inertia and conformity pressures from peers in the same topic group

Bayesian Network Representing System Dynamics in Risk Analysis of Nuclear Systems.

A dynamic Bayesian network (DBN) model is used in conjunction with the alternating conditional expectation (ACE) regression method to analyze the risk associated with the loss of feedwater accident coupled with a subsequent initiation of the feed and bleed operation in the Zion-1 nuclear power plant. The use of the DBN allows the joint probability distribution to be factorized, enabling the analysis to be done on many simpler network structures rather than on one complicated structure. The construction of the DBN model assumes conditional independence relations among certain key reactor parameters. The choice of parameter to model is based on considerations of the macroscopic balance statements governing the behavior of the reactor under a quasi-static assumption. The DBN is used to relate the peak clad temperature to a set of independent variables that are known to be important in determining the success of the feed and bleed operation. A simple linear relationship is then used to relate the clad temperature to the core damage probability. To obtain a quantitative relationship among different nodes in the DBN, surrogates of the RELAP5 reactor transient analysis code are used. These surrogates are generated by applying the ACE algorithm to output data obtained from about 50 RELAP5 cases covering a wide range of the selected independent variables. These surrogates allow important safety parameters such as the fuel clad temperature to be expressed as a function of key reactor parameters such as the coolant temperature and pressure together with important independent variables such as the scram delay time. The time-dependent core damage probability is calculated by sampling the independent variables from their probability distributions and propagate the information up through the Bayesian network to give the clad temperature. With the knowledge of the clad temperature and the assumption that the core damage probability has a one-to-one relationship to it, we have calculated the core damage probably as a function of transient time. The use of the DBN model in combination with ACE allows risk analysis to be performed with much less effort than if the analysis were done using the standard techniques.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89759/1/avarutta_1.pd

A dynamic bayesian network to represent discrete duration models

International audienceOriginally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in various fields of application have shown the relevancy of using Bayesian networks to model complex systems, namely stochastic systems with an underlying distribution that does not fit to a well-known parametric form. In this paper, the description of a specific dynamic Bayesian network, referred to as Graphical Duration Model (GDM), is given. A GDM aims to represent a wide range of duration models. Its structure allows especially to fit multi-state systems featuring complex sojourn-time distributions and contextual dependencies. To that end, a duration variable is explicitly introduced to the state transition model which is classically represented by a Markov chain. A recursive algorithm efficiently perform inference in this model is derived along with its proof of correctness and space and time complexity studies. Finally, this approach is illustrated with an application in survival analysis in which the proposed model is compared with the commonly used Markov chain modelling