Gal A. Kaminka's Publications

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Modeling Pedestrian Crowd Behavior Based on a Cognitive Model of Social Comparison Theory

Natalie Fridman and Gal A. Kaminka. Modeling Pedestrian Crowd Behavior Based on a Cognitive Model of Social Comparison Theory. Computational and Mathematical Organizational Theory, 16(4):348–372, 2010. Special issue on Social Simulation from the Perspective of Artificial Intelligence

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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, 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 not tied to cognitive theory and often focus only on a specific phenomenon (e.g., flocking, bi-directional pedestrian movement), and thus must be switched depending on the goals of the simulation. 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 1950s. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several pedestrian movement phenomena such as creation of lanes in bidirectional movement and movement in groups with and without obstacle. Compared to popular models from the literature, the SCT model was shown to provide improved results. We also evaluate the SCT model on general pedestrian movement, and validate the model against human pedestrian behavior. The results show that SCT generates behavior more in-tune with human crowd behavior then existing non-cognitive models.

Additional Information

The article's official web page is at: http://www.springerlink.com/content/r33785714467l274/.

BibTeX

@Article{cmot10,
 author={Natalie Fridman and Gal A. Kaminka},
 title = {Modeling Pedestrian Crowd Behavior Based on a Cognitive Model of Social Comparison Theory},
 journal = {Computational and Mathematical Organizational Theory},
 year = {2010},
OPTkey = {},
volume = {16},
number = {4},
note = {Special issue on Social Simulation from the Perspective of Artificial Intelligence},
pages = {348--372},
OPTmonth = {November},
  wwwnote = {}, 
OPTannote = {},
 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, 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 not tied to cognitive theory and
                  often focus only on a specific phenomenon (e.g.,
                  flocking, bi-directional pedestrian movement), and
                  thus must be switched depending on the goals of the
                  simulation. 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 1950s. We propose a
                  concrete algorithmic framework for SCT, and evaluate
                  its implementations in several pedestrian movement
                  phenomena such as creation of lanes in bidirectional
                  movement and movement in groups with and without
                  obstacle. Compared to popular models from the
                  literature, the SCT model was shown to provide
                  improved results. We also evaluate the SCT model on
                  general pedestrian movement, and validate the model
                  against human pedestrian behavior. The results show
                  that SCT generates behavior more in-tune with human
                  crowd behavior then existing non-cognitive models. },
}

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