On modelling the influence of group formations in a crowd

Köster, Gerta; Seitz, Michael; Treml, Franz; Hartmann, Dirk; Klein, Wolfram

doi:10.1080/21582041.2011.619867

Cited by 90 publications

(80 citation statements)

References 20 publications

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“…They reacted only later, when unambiguous cues of danger, such as smoke, flames, and people running while holding fire extinguishers, occurred. Subsequent studies of survivors' behavior during disasters also suggest that people who were together with familiar others were slow to perceive that they were in danger (Aguirre, Wenger, & Vigo, 1998;Köster, Seitz, Treml, Hartmann, & Klein, 2011). This tendency might result from secure people's sense of safety and optimistic threat appraisals (Ein-Dor et al, 2010).…”

Section: Advantages and Disadvantages Of Secure Individuals' Defensivmentioning

confidence: 91%

Attachment dispositions and human defensive behavior

Ein‐Dor

2015

Personality and Individual Differences

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Section: Advantages and Disadvantages Of Secure Individuals' Defensivmentioning

confidence: 91%

Attachment dispositions and human defensive behavior

Ein‐Dor

2015

Personality and Individual Differences

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“…Over the past decade, there has been increased recognition that the concept of a shared social identity is necessary for more realistic simulation of human collective behaviour (Aguirre et al, 2011;Köster et al, 2011;Langston, Masling, & Asmar, 2006;Smith et al, 2009;Templeton et al, 2015). The ability of self-categorisation theory to explain collective behaviour in numerous contexts indicates that computer simulations could benefit from applying this theory to adequately reproduce a broad variety of collective behaviour scenarios.…”

Section: Towards An Understanding Of Large-scale Collective Behaviourmentioning

confidence: 99%

“…First, one can try to extend existing models with a social layer in addition to the basic interaction and locomotion mechanisms (Köster et al, 2011;Moussaïd et al, 2010;Singh et al, 2009;von Sivers, Templeton, Köster, Drury, & Philippides, 2014;Yang et al, 2005). Second, one could develop a new decision-making framework that provides the necessary structure and flexibility we need.…”

Section: Parsimony Without Reductionism Two Approaches For Simulationmentioning

confidence: 99%

“…A more promising direction of research are proxemics (Baum & Paulus, 1987;Hall, 1966), which describe the social distances individuals keep from one another and has been used for the study of crowd behaviour (Costa, 2010;von Sivers & Köster, 2015;Zanlungo, Ikeda, & Kanda, 2014). Although there have been some attempts to introduce small groups within the larger crowd behaviour to simulation models such as families, friends or other predefined groups (Köster, Seitz, Treml, Hartmann, & Klein, 2011;Moussaïd, Perozo, Garnier, Helbing, & Theraulaz, 2010;Singh et al, 2009;Yang, Zhao, Li, & Fang, 2005), these models do not consider the social structure or dynamic of the whole crowd (for a comprehensive review, see (Templeton et al, 2015)). For example, concepts such as "contagion" between individuals are still referred to in recent literature (e.g., Fridman & Kaminka, 2007;Helbing, Farkas, Molnár, & Vicsek, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Parsimony versus Reductionism: How Can Crowd Psychology be Introduced into Computer Simulation?

Seitz

Templeton

Drury³

et al. 2017

Review of General Psychology

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(2016) Parsimony versus reductionism: how can crowd psychology be introduced into computer simulation? Review of General Psychology, 21 (1). pp. 95-102. ISSN 108995-102. ISSN -2680 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/66196/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. However, the models used are mainly based on video observations, not an understanding of human decision making. Theories of crowd psychology can elucidate the factors underpinning collective behaviour in human crowds. Yet, in contrast to psychology, computer science must rely upon mathematical formulations in order to implement algorithms and keep models manageable. Here we address the problems and possible solutions encountered when incorporating social psychological theories of collective behaviour in computer modelling. We identify that one primary issue is retaining parsimony in a model whilst avoiding reductionism by excluding necessary aspects of crowd psychology, such as the behaviour of groups. We propose cognitive heuristics as a potential avenue to create a parsimonious model that incorporates core concepts of collective behaviour derived from empirical research in crowd psychology.

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“…On the other hand, extensions developed for one model based on scalar fields can easily be adopted for other models within the superposition paradigm. For example, sub-group behaviours [18,51] and navigation fields [40,42] were developed for force-based models and cellular automata, respectively, but were successfully used subsequently in other models afterwards.…”

Section: Implications For Model Developmentmentioning

confidence: 99%

The Superposition Principle: A Conceptual Perspective on Pedestrian Stream Simulations

et al. 2016

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Models using a superposition of scalar fields for navigation are prevalent in microscopic pedestrian stream simulations. However, classifications, differences, and similarities of models are not clear at the conceptual level of navigation mechanisms. In this paper, we describe the superposition of scalar fields as an approach to microscopic crowd modelling and corresponding motion schemes. We use this background discussion to focus on the similarities and differences of models, and find that many models make use of similar mechanisms for the navigation of virtual agents. In some cases, the differences between models can be reduced to differences between discretisation schemes. The interpretation of scalar fields varies across models, but most of the time this variation does not have a large impact on simulation outcomes. The conceptual analysis of different models of pedestrian dynamics allows for a better understanding of their capabilities and limitations and may lead to better model development and validation.

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On modelling the influence of group formations in a crowd

Cited by 90 publications

References 20 publications

Attachment dispositions and human defensive behavior

Attachment dispositions and human defensive behavior

Parsimony versus Reductionism: How Can Crowd Psychology be Introduced into Computer Simulation?

The Superposition Principle: A Conceptual Perspective on Pedestrian Stream Simulations

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