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Gal A. Kaminka and Natalie Fridman. Using Qualitative Reasoning for Social Simulation of Crowds: A Preliminary Report. In 25th International Workshop on Qualitative Reasoning, 2011.
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 theinteractions between different factors that influenceviolence 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.
@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},
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year = {2011},
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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. }
}
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