Abstract
Models of crowd behavior facilitate analysis and prediction
of human group behavior, where people are affected by each
other's presence. Unfortunately, existing models leave many
open challenges. In particular, psychology models often offer
only qualitative description, while computer science models
are often simplistic, and are not reusable from one simulated
phenomenon to the next. We propose a novel model
of crowd behavior, based on Festinger's Social Comparison
Theory (SCT). We propose a concrete algorithmic framework
for SCT, and evaluate its implementation in several crowd
behavior scenarios. Results from task measures and human
judges evaluation shows that the SCT model produces improved
results compared to base models from the literature.
| Original language | American English |
|---|---|
| Title of host publication | The national conference on artificial intelligence |
| State | Published - 2007 |
Bibliographical note
Place of conference:CanadaFingerprint
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