@COMMENT This file was generated by bib2html.pl version 0.94 @COMMENT written by Patrick Riley @COMMENT This file came from Gal A. Kaminka's publication pages at @COMMENT http://www.cs.biu.ac.il/~galk/publications/ @InProceedings{qr11, author = {Gal A. Kaminka and Natalie Fridman}, title = {Using Qualitative Reasoning for Social Simulation of Crowds: A Preliminary Report}, booktitle = {25th International Workshop on Qualitative Reasoning}, OPTcrossref = {}, OPTkey = {}, OPTpages = {}, year = {2011}, OPTeditor = {}, OPTvolume = {}, OPTnumber = {}, OPTseries = {}, OPTaddress = {}, OPTmonth = {}, OPTorganization = {}, OPTpublisher = {}, OPTnote = {}, OPTannote = {}, OPTurl = {}, OPTdoi = {}, OPTissn = {}, OPTlocalfile = {}, abstract = {We report on the use of qualitative reasoning (QR) for modeling the social behavior of large groups, in particular in demonstrations. We develop qualitative models consistent with the partial, qualitative social science literature, allowing us to model the interactions between different factors that influence violence in demonstrations. We then utilize qualitative simulation to predict the potential eruption of violence, at various levels, based on a description of the demographics, environmental settings, and police responses. In addition to providing predictions, the resulting qualitative simulation graph is analyzed to determine the factors that are most important in influencing the outcome. These factors can be used to support decision-makers. We make three separate contributions: first, we briefly show how the use of QR can be used to to contrast the predictions of different social science theories; second, we demonstrate that the QR technique has better explanatory power than a machine learning approach to prediction; and third, we use the analysis algorithm to determine important factors in specific real-world demonstrations. We show that the algorithm identifies factors that correspond to experts analysis of these events. } }