Dr. Florian Jentsch, Dr. D. J. Kaup, Dr. Linda C. Malone, Holly Blasko-Drabik, & Rex Oleson

1. Social and Behavioral Individual-Difference Variables in Crowd Simulations: A Literature Review and Theoretical Framework Dr. Florian Jentsch, Dr. D. J. Kaup, Dr. Linda C. Malone, Holly Blasko-Drabik, & Rex Oleson University of Central Florida, Orlando, FL, U.S.A. Presenter: Dr. Linda C. Malone, +1-407-823-2833, lmalone@mail.ucf.edu

2. Crowd Modeling and Simulation • Aspects of crowd models: • Assembly • Movement • Aggression • Dispersion • Current focus mostly on: • Pedestrian behavior • Emergency exit • Important for: • Architecture • Law enforcement • Emergency management

3. Background and Purpose • Crowd modeling and simulation are areas that are increasing exponentially • Models and technology may outpace theory and literature • Literature review • UCF • National Science Foundation (NSF) Grant No. BCS-0527545

4. Methodology • Structured literature search, conducted March – July 2007 • Using Google Scholar, PsychInfo, Engineering Index • Search terms: • Crowd: • Model, modeling, movement, distribution • Pedestrian: • Model, Movement • Age, elderly, youth, children • Culture, ethnicity, race • Terrain, topography, land • Emergency, panic, threat, terror

5. Methodology (cont.) Key questions we asked: • What is the purpose for the crowd modeling? • Determine how individuals or the entire crowd would behave based on a certain task objectives • Model overall crowd patterns such as queuing and herding behavior • Are the variables applied to real world situations or tested only in a virtual environment? • Which variables are modeled? • E.g., emotions, gender, age, or previous and current knowledge

6. Quantitative Results • 172 journal articles, book chapters, and proceedings papers • 22 did not have abstracts available • Of the remaining 150: • 52 discarded as crowd modeling was not focus • 34 put aside as they were too generic • 64 formed the final sample

7. Quantitative Results • Although 64 results files appeared to discuss individual differences, of these, a large proportion (> 50%) more-or-less mentioned individual difference variables as influence factors, but did not actually model them

8. Results - Output • Created annotated bibliography • Included salient points or issues from each article. • Created executive summaries for the most specific and potentially useful articles: • Method • Results • Conclusions • Further directions of the research • Annotated bibliography available upon request

9. Finding 1: The literature is fragmented • Crowd vs. Individual • Is impact of individual-difference variables studied at the crowd level or at the individual level? • Macroscopic vs. microscopic models • Macroscopic: Fluid Dynamics • Microscopic: Psychology • How will the crowd react? vs. How will the individuals in the crowd react?

10. Finding 1: The literature is fragmented • Crowd vs. Individual • Constructive vs. VE: • What is the output of the crowd simulation? • VE simulations have high visual appeal, but that does not make them more correct • Constructive simulation uses symbolic representation which may facilitate recognition of macroscopic crowd behaviors

11. Finding 2: Models with individual-difference variables improve but are not yet convincing • Use of real-world validations for models of individual difference variables in crowds: • Although we identified several articles that used large, real-world samples, none of them specifically looked at individual differences • O'Connor et al. (2005) and Casburn (2006), however, reported to have collected such data and that they intended to present it in future papers.

12. Finding 2: Models with individual-difference variables improve but are not yet convincing • A lot of the results still focused on emergency decision making, behavior, motion • Fatigue, emotional connection, and social networks seem to be the most often used individual difference variables • These can be “disguised” as culture and other variables.

13. Finding 3: Social Force Models provide a suitable point of departure • Social Force Models are based on the ‘generalized behavior concept’ • The specific Social Force Model proposed by Helbing, Molnar, Farkas and Vicsek (HMFV model, 2002) is a self-driven, many particles model using push-pull effects to describe pedestrian behavior in crowds. • HMFV model allows consideration of social phenomena such as herding and flocking

14. Finding 3: Social Force Models provide a suitable point of departure • We are modifying and extending the Social Force Model proposed by Helbing, Molnar, Farkas and Vicsek (2002). • The intent is to allow a distribution of individual characteristics within any crowd. • Example: Age distribution impacts… • Personal Space • Speed • Randomness

15. Future Research Needs • Variables affecting actual crowds: • One collective vs. companion clusters vs. assembly of individuals? • Information propagation through crowds • Methods to extract information from actual crowds (e.g., videos) • Optic flow • Pixel tracking

16. Future Research Needs (cont.) • Center Line 0.5 • Lower Control • Limit = Out of Control Region ith Observation Number (Observation Vector) • Computational approaches that allow the inclusion of individual difference variables in crowd models • Social Force Models • Randomness • Methods for the verification and validation of the resulting models • e.g., principal components analysis • see Malone et al., Thursday AM

17. A Final Quote… • “Crowds are the elephant man of the social sciences. They are viewed as something strange, something pathological, something monstrous. At the same time they are viewed with awe and with fascination. However, above all, they are considered to be something apart. We may choose to go and view them occasionally as a distraction from the business of everyday life, but they are separate from that business and tell us little or nothing about normal social and psychological realities . Such an attitude is reflected in the remarkable paucity of psychological research on crowd processes and the fact that it is all but ignored by the dominant paradigms in social psychology.” (Reicher, 2001, p. 182) • From: Reicher, S. D. (2001). "The psychology of crowd dynamics." Blackwell handbook of social psychology: Group processes: 182–208.

18. Contact Information SimMBioS at the University of Central Florida 3100 Technology Parkway Orlando, Florida 32826 U.S.A. Dr. D. J. Kaup, 407-883-1484, dkaup@ist.ucf.edu Dr. Linda C. Malone, 407-823-2833, lmalone@mail.ucf.edu Dr. T. L. Clarke, 407-882-1327, tclarke@ist.ucf.edu Dr. Florian Jentsch, 407-882-0304, fjentsch@mail.ucf.edu Rex Oleson, 407-882-1300, quazerin@juno.com