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Symmetry Breaking in Evacuation Exit Choice: Impacts of Cognitive Bias and Physical Factor on Evacuation Decision

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Agents and Artificial Intelligence (ICAART 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11978))

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Abstract

When people evacuate from a room with two identical exits, it is known that these exits are often unequally used, with evacuees gathering at one of them. This inappropriate and irrational behavior sometimes results in serious loss of life. In this paper, this symmetry breaking in exit choice is discussed from the viewpoint of herding, a cognitive bias in humans during disaster evacuations. The aim of this paper is to show that simple herd behavior is sufficient to reproduce symmetry breaking in exit choice, whereas many models in the literature adopt predefined rules, scenarios, or some models representing rational decision making processes such as utility functions or payoff matrices. The evacuation decision model, based on the response threshold model in biology, is presented to reproduce human herd behavior. Simulation with the evacuation decision model shows that almost all agents gather at one exit at some frequency, despite individual agents choosing the exit randomly. Moreover, the social force model is employed in conjunction with the evacuation decision model to take physical factors such as clogging and collisions into account. The effects of physical factors on both evacuation decisions and evacuation times are analyzed.

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Notes

  1. 1.

    NetLogo 6.0.2 [41] is used to implement the models presented in this paper.

  2. 2.

    https://www.ted.com/talks/derek_sivers_how_to_start_a_movement.

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Acknowledgements

The author is grateful to Robert Ramirez, Yoshikazu Shinoda, and Kei Marukawa for their helpful comments and suggestions.

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Correspondence to Akira Tsurushima .

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Tsurushima, A. (2019). Symmetry Breaking in Evacuation Exit Choice: Impacts of Cognitive Bias and Physical Factor on Evacuation Decision. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-37494-5_15

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