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Natalie Fridman. Modeling Crowd Behavior Based On Social Comparison Theory.
Master's Thesis, Bar Ilan University,2007.
Modeling crowd behavior is an important challenge for cognitive modelers. Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are close geographically or logically states, and are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychology models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often simplistic, treating agents as simple deterministic particles. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950's. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several crowd behavior scenarios such as pedestrian movement, gathering and imitational behavior. We show that our SCT model produces improved results compared to base models from the literature. We describe two possible implementations of SCT process in an architectural level. The first, which seems to follow directly from Festinger's Social Comparison theory, treats the SCT process as an uncertainty-resolution method. The second, takes a different approach, in which an SCT process is constantly active, in parallel to any problem solving activity. We present the implementation of these approaches in Soar cognitive architecture. Moreover, we examine these approaches in the context of crowd behavior simulations and argue that surprisingly, it is the second approach which is correct.
@MastersThesis{natalie-msc,
author = {Natalie Fridman},
title = {Modeling Crowd Behavior Based On Social Comparison Theory},
school = {{B}ar {I}lan {U}niversity},
year = {2007},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {},
OPTannote = {},
wwwnote = {},
abstract = {Modeling crowd behavior is an important challenge for cognitive
modelers. Models of crowd behavior facilitate analysis and
prediction of human group behavior, where people are close
geographically or logically states, and are affected by each other's
presence and actions. Existing models of crowd behavior, in a
variety of fields, leave many open challenges. In particular,
psychology models often offer only qualitative description, and do
not easily permit algorithmic replication, while computer science
models are often simplistic, treating agents as simple deterministic
particles. We propose a novel model of crowd behavior, based on
Festinger's Social Comparison Theory (SCT), a social psychology
theory known and expanded since the early 1950's. We propose a
concrete algorithmic framework for SCT, and evaluate its
implementations in several crowd behavior scenarios such as
pedestrian movement, gathering and imitational behavior. We show
that our SCT model produces improved results compared to base models
from the literature. We describe two possible implementations of SCT
process in an architectural level. The first, which seems to follow
directly from Festinger's Social Comparison theory, treats the SCT
process as an uncertainty-resolution method. The second, takes a
different approach, in which an SCT process is constantly active, in
parallel to any problem solving activity. We present the
implementation of these approaches in Soar cognitive architecture.
Moreover, we examine these approaches in the context of crowd
behavior simulations and argue that surprisingly, it is the second
approach which is correct. },
}
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