Predictive Analysis for Social Processes I: Multi-Scale Hybrid System Modeling Export

@article{citeulike:5158256, abstract = {This two-part paper presents a new approach to predictive analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the appeal of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process predictive analysis.}, archivePrefix = {arXiv}, author = {Colbaugh, Richard and Glass, Kristin}, citeulike-article-id = {5158256}, citeulike-linkout-0 = {http://arxiv.org/abs/0907.2312}, citeulike-linkout-1 = {http://arxiv.org/pdf/0907.2312}, day = {14}, eprint = {0907.2312}, keywords = {analysis, dynamics, epidemics, modeling, politics, prediction, sir, social, social-modeling, social\_dynamics, sociology}, month = {Jul}, posted-at = {2009-10-21 06:37:42}, priority = {2}, title = {Predictive Analysis for Social Processes I: Multi-Scale Hybrid System Modeling}, url = {http://arxiv.org/abs/0907.2312}, year = {2009} }

TY - JOUR ID - citeulike:5158256 L3 - citeulike-article-id:5158256 N2 - This two-part paper presents a new approach to predictive analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the appeal of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process predictive analysis. TI - Predictive Analysis for Social Processes I: Multi-Scale Hybrid System Modeling KW - analysis KW - dynamics KW - epidemics KW - modeling KW - politics KW - prediction KW - sir KW - social KW - social-modeling KW - social_dynamics KW - sociology AU - Colbaugh, Richard AU - Glass, Kristin PY - 2009/Jul/14/ UR - http://arxiv.org/abs/0907.2312 ER -