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@PhdThesis{natalie-phd, 
author = {Natalie Fridman}, 
title = {Modeling Crowd Behavior}, 
school = {{B}ar {I}lan {U}niversity}, 
year = {2012}, 
  wwwnote = {}, 
OPTannote = {} ,
  abstract = {Modeling crowd behavior is an important challenge for cognitive modelers, 
  multi-agent systems and social simulation. In this dissertation we explore 
  several techniques for modeling crowd behaviors and address important challenges concerning each technique. 
  In the first part of the work we use the agent based approach for modeling crowd behavior.  
  We present the extended Social Comparison model (SCT), which we believe is a general  
  cognitive process underlying the social behavior of each individual in a crowd. 
  In this work we provide a qualitative evaluation of the SCT model, as well as others, in contrast to human 
  pedestrian behavior. The results clearly demonstrate that the SCT model 
  is superior to others in its fidelity to human pedestrian behavior. Moreover, 
  we have focused on an open question which has arisen from the SCT model, 
  namely when should the SCT process be used at the architectural level in 
  order to guide action-selection of agents. We have extended the SCT model 
  to address this open question. We argue that comparisons take place all 
  the time (i.e., differences are perceived and processed), but the cognitive 
  architecture limits actions taken to minimize differences in cases where the 
  comparisons yield significant differences. In addition we examine the impact 
  of cultural differences on the macro level behavior produced in pedestrian 
  and evacuation domains. In this work we have advanced by treating culture 
  as a first-class object in models of physical crowds and have extended the 
  SCT model accordingly. We introduced cultural individual-level parameters 
  into the simulations, and then examined the effects of these individual level 
  parameters on the emergent crowd dynamics.
In the second part of the work we use the qualitative reasoning approach  
for modeling demonstrations. We present a first attempt to use qualitative 
reasoning techniques in order to model crowd behaviors. To the best of our 
knowledge, such techniques have never been applied to modeling and reasoning 
regarding crowd behaviors, nor in particular demonstrations. We have 
developed qualitative models consistent with the partial, qualitative social 
science literature, which has enabled us to model the interactions between 
different factors that influence violence in demonstrations. We then utilized 
the qualitative simulations 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 graphs were analyzed to determine the factors 
that are most important in influencing the outcome. These factors can be 
used to support decision-makers.
 },
}