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arxiv: 2606.29212 · v1 · pith:HLIKDHKMnew · submitted 2026-06-28 · 💻 cs.AI

A Cognition-Emotion-Personality Framework for Modeling Human-Like Awareness and Behavior in Emergency Evacuations

Pith reviewed 2026-06-30 07:49 UTC · model grok-4.3

classification 💻 cs.AI
keywords agent-based modelingevacuation simulationcognitive modelingemotional dynamicspersonality traitscrowd behavioremergency managementOCEAN personality
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The pith

Integrating cognition, emotion and personality into evacuation models produces delays, confusion and injuries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds an agent-based evacuation model that adds a continuous Event Certainty Level for awareness, memory that can be acquired, forgotten and recalled, a fear model where Neuroticism affects contagion and recovery, OCEAN personality traits, and individual decision thresholds. Simulations vary spatial familiarity, memory strength, emotional dynamics and personality to show how these factors change crowd movement. The core claim is that these mechanisms lower overall evacuation speed and produce realistic effects such as hesitation, group influence and injuries that simpler rational models miss. A reader would care because most existing simulations assume perfect knowledge and calm decisions, yet real evacuations involve uncertainty and personal differences.

Core claim

The framework combines a dynamic Event Certainty Level, memory acquisition/forgetting/recall for exits, a continuous fear model with Neuroticism integrated into generation, escalation, contagion and recovery, OCEAN personality representation, and individualized decision thresholds. When these are implemented as agent rules and tested in simulation experiments, cognitive, emotional and personality processes substantially reduce evacuation efficiency and generate observable crowd phenomena including delays, confusion, injuries and socially influenced behaviors.

What carries the argument

The cognition-emotion-personality framework that links continuous Event Certainty Level awareness, memory mechanisms, a Neuroticism-augmented fear model, OCEAN traits and individualized thresholds to drive heterogeneous agent decisions.

If this is right

  • Simulations that omit these mechanisms will overestimate how quickly and smoothly crowds exit.
  • Higher Neuroticism increases fear spread through social contagion and slows recovery.
  • Weaker memory robustness reduces effective exit knowledge and raises confusion.
  • Varying decision thresholds across agents produces heterogeneous responses to the same perceived risk.
  • Realistic crowd-level effects such as delays and injuries arise directly from the integrated processes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Building codes or safety training could be tested by varying personality distributions in the model.
  • Adding real-time sensor feedback to update Event Certainty Level might improve live evacuation guidance.
  • The same structure could be adapted to model other high-stress decisions such as medical triage or financial panic.
  • Cultural differences in average OCEAN scores would be expected to shift predicted evacuation times.

Load-bearing premise

That the listed mechanisms can be turned into agent rules that produce valid representations of real human behavior under uncertainty.

What would settle it

Direct comparison of the model's predicted delays, injury rates and group behaviors against time-stamped video or sensor data from an actual building evacuation.

Figures

Figures reproduced from arXiv: 2606.29212 by Chairi Kiourt, Dimitris Kalles, Helena G. Theodoropoulou, Michalis Zervas, Vasilis Zafeiropoulos, Zoi Lygizou.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p021_1.png] view at source ↗
read the original abstract

Agent-based evacuation simulations are widely used to study crowd behavior during emergencies, but many models rely on assumptions such as perfect event awareness, complete exit knowledge, and fully rational decision-making. This paper presents an extended evacuation framework that integrates cognitive, emotional, social, and personality-related mechanisms into a unified model of human behavior under uncertainty. The framework incorporates a dynamic event-awareness mechanism based on a continuous Event Certainty Level, a memory-based representation of exit knowledge subject to acquisition, forgetting, and recall, a continuous fear model in which panic emerges as a high-intensity state, and an OCEAN-based personality representation. Neuroticism is explicitly integrated into the emotional model, influencing fear generation, escalation, social contagion, and recovery. Behavioral heterogeneity is further captured through individualized decision thresholds that affect responses to perceived risk. The framework is evaluated through simulation experiments examining the effects of spatial familiarity, memory robustness, decision sensitivity, emotional dynamics, and personality variation. Results show that cognitive, emotional, and personality-driven processes substantially influence evacuation dynamics, reducing evacuation efficiency and generating realistic crowd phenomena such as delays, confusion, injuries, and socially influenced behaviors. The proposed framework provides a more realistic representation of human behavior in emergency evacuations and supports systematic investigation of the interactions between cognition, emotion, personality, and crowd dynamics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes an agent-based evacuation framework that augments standard models with a continuous Event Certainty Level for awareness, memory acquisition/forgetting/recall for exit knowledge, a fear model incorporating Neuroticism for contagion and recovery, OCEAN personality traits, and individualized decision thresholds. Simulation experiments varying spatial familiarity, memory robustness, decision sensitivity, emotional dynamics, and personality are reported to show that these mechanisms substantially reduce evacuation efficiency and produce emergent behaviors such as delays, confusion, injuries, and social influence.

Significance. If the mechanisms were shown to reproduce quantitative features of real evacuations, the framework would offer a more heterogeneous representation of human decision-making under uncertainty than purely rational or uniform models, with potential utility for safety engineering and crowd simulation tools. The explicit integration of personality into emotional contagion is a constructive modeling choice, but the current absence of external anchoring limits the result to an untested conceptual extension.

major comments (2)
  1. [Abstract] Abstract and evaluation description: the headline claim that the simulations 'generate realistic crowd phenomena such as delays, confusion, injuries, and socially influenced behaviors' is unsupported because no section compares any simulated statistic (exit-choice distributions, delay histograms, injury counts, or contagion patterns) against observed evacuation data, video records, or post-incident reports; all reported effects are internal to parameter sweeps.
  2. [Evaluation] Evaluation section: the experiments vary free parameters (Event Certainty Level update rates, individualized thresholds, fear generation/escalation/contagion/recovery coefficients) but supply no equations, numerical values, sensitivity analysis, or reproducibility details, so it is impossible to assess whether the reported qualitative effects are robust or artifactual.
minor comments (1)
  1. Notation for continuous variables (Event Certainty Level, fear intensity) should be defined with explicit update rules and initial conditions to allow replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important issues of empirical grounding and reproducibility. We address each major comment below and will make the necessary revisions.

read point-by-point responses
  1. Referee: [Abstract] Abstract and evaluation description: the headline claim that the simulations 'generate realistic crowd phenomena such as delays, confusion, injuries, and socially influenced behaviors' is unsupported because no section compares any simulated statistic (exit-choice distributions, delay histograms, injury counts, or contagion patterns) against observed evacuation data, video records, or post-incident reports; all reported effects are internal to parameter sweeps.

    Authors: We agree that the abstract's phrasing overstates the results. The reported behaviors emerge from the model's internal dynamics and are described qualitatively, but no quantitative matching to real evacuation data is performed. We will revise the abstract to remove the word 'realistic' and the unsupported claim, instead stating that the mechanisms produce emergent behaviors such as delays, confusion, and social influence within the simulations. revision: yes

  2. Referee: [Evaluation] Evaluation section: the experiments vary free parameters (Event Certainty Level update rates, individualized thresholds, fear generation/escalation/contagion/recovery coefficients) but supply no equations, numerical values, sensitivity analysis, or reproducibility details, so it is impossible to assess whether the reported qualitative effects are robust or artifactual.

    Authors: The current manuscript provides high-level descriptions of the mechanisms but does not include the full set of update equations, exact coefficient values, or sensitivity tests. We will expand the evaluation section in revision to include all governing equations, the specific numerical values and ranges used in the reported runs, and a sensitivity analysis demonstrating that the qualitative effects persist across parameter perturbations. revision: yes

Circularity Check

0 steps flagged

No circularity; modeling framework derives behaviors from explicit rules without reduction to inputs

full rationale

The provided abstract and description show a framework built from defined mechanisms (Event Certainty Level, memory processes, fear model with Neuroticism, OCEAN traits, decision thresholds) whose simulation outputs are generated by applying those rules. No equations, self-citations, or parameter-fitting steps are quoted that would make any reported 'prediction' or 'realistic phenomenon' equivalent to the inputs by construction. The evaluation consists of varying model parameters and observing emergent effects, which is a standard forward simulation rather than a circular renaming or self-referential fit. Absence of external data comparison is a validity concern, not a circularity issue per the analysis rules.

Axiom & Free-Parameter Ledger

3 free parameters · 1 axioms · 0 invented entities

Review limited to abstract; the framework introduces several continuous state variables and personality integrations whose specific update rules and parameterizations are not detailed.

free parameters (3)
  • Event Certainty Level update parameters
    Continuous awareness variable whose dynamics must be parameterized to drive the model.
  • Individualized decision thresholds
    Per-agent thresholds for responding to perceived risk; values are individualized but not specified.
  • Fear generation, escalation, contagion, and recovery parameters
    Parameters governing the continuous fear model and its modulation by Neuroticism.
axioms (1)
  • domain assumption Human evacuation behavior under uncertainty can be usefully approximated by integrating cognitive awareness, memory, fear/panic, OCEAN personality, and social contagion into agent decision rules.
    This premise underpins the entire extension of standard rational-agent models.

pith-pipeline@v0.9.1-grok · 5792 in / 1569 out tokens · 42664 ms · 2026-06-30T07:49:33.299375+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

80 extracted references · 56 canonical work pages

  1. [1]

    ES CAPES: Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social Comparison

    Tsai, J.; Fridman, N.; Bowring, E.; Brown, M.; Epstein, S.; Kaminka, G.A.; Marsella, S.; Ogden, A.; Rika, I.; Sheel, A. ES CAPES: Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social Comparison. In Proceedings of the AAMAS; 2011; Vol. 11, pp. 457–464

  2. [2]

    Don’t Panic : The Psychology of Emergency Egress and Ingress; Praeger, 1999

    Chertkoff, J.M.; Kushigian, R.H. Don’t Panic : The Psychology of Emergency Egress and Ingress; Praeger, 1999

  3. [3]

    Patterns of Cognitive Appraisal in Emotion

    Smith, C.A.; Ellsworth, P.C. Patterns of Cognitive Appraisal in Emotion. J. Pers. Soc. Psychol. 1985, 48, 813–838, doi:10.1037/0022-3514.48.4.813

  4. [4]

    Agent-Based Crowd Simulation: An in-Depth Survey of Determining Factors for Heterogeneous Behavior

    Khan, S.; Deng, Z. Agent-Based Crowd Simulation: An in-Depth Survey of Determining Factors for Heterogeneous Behavior. Vis. Comput. 2024, 40, 4993 –5004, doi:10.1007/s00371-024-03503-2

  5. [5]

    Introducing Agent Personality in Crowd Simulation Improves Social Presence and Experienced Realism in Immersive VR

    Pascoli, M.; Buttussi, F.; Schekotihin, K.; Chittaro, L. Introducing Agent Personality in Crowd Simulation Improves Social Presence and Experienced Realism in Immersive VR. IEEE Trans. Vis. Comput. Graph. 2025, 31, 7269 –7283, doi:10.1109/TVCG.2025.3543740. A Cognition-Emotion-Personality Framework for Modeling Human -Like Awareness and Behavior in Emerge...

  6. [6]

    bin Developing Fire Evacuation Simulation Through Emotion -Based BDI Methodology

    Paschal, C.H.; Shiang, C.W.; Wai, S.K.; Khairuddin, M.A. bin Developing Fire Evacuation Simulation Through Emotion -Based BDI Methodology. JOIV Int. J . Inform. Vis. 2022, 6, 45–52, doi:10.30630/joiv.6.1.854

  7. [7]

    A Novel Emergency Evacuation Model of Subway Station Passengers Considering Personality Traits

    Wang, H.; Xu, T.; Li, F. A Novel Emergency Evacuation Model of Subway Station Passengers Considering Personality Traits. Sustainability 2021, 13, doi:10.3390/su131810463

  8. [8]

    An Emergency Evacuation Behavior Simulation Method Combines Personality Traits and Emotion Contagion

    Zhou, R.; Ou, Y.; Tang, W.; Wang, Q.; Yu, B. An Emergency Evacuation Behavior Simulation Method Combines Personality Traits and Emotion Contagion. IEEE Access 2020, 8, 66693–66706, doi:10.1109/ACCESS.2020.2985987

  9. [9]

    The Five-Factor Model of Personality: Theoretical Perspectives; Guilford Press, 1996; ISBN 978-1-57230-068-2

    Wiggins, J.S. The Five-Factor Model of Personality: Theoretical Perspectives; Guilford Press, 1996; ISBN 978-1-57230-068-2

  10. [10]

    Crowd Simulation for Crisis Management: The Outcomes of the Last Decade

    Sidiropoulos, G.; Kiourt, C.; Moussiades, L. Crowd Simulation for Crisis Management: The Outcomes of the Last Decade. Mach. Learn. Appl. 2020, 2, 100009, doi:10.1016/j.mlwa.2020.100009

  11. [11]

    Personality and Individual Differences: Revisiting the Classic Studies ; SAGE, 2018; ISBN 978-1-5264-5520-8

    Corr, P. Personality and Individual Differences: Revisiting the Classic Studies ; SAGE, 2018; ISBN 978-1-5264-5520-8

  12. [12]

    The Cognitive Structure of Emotions ; Cambridge University Press, 2022; ISBN 978-1-108-84424-6

    Ortony, A.; Clore, G.L.; Collins, A. The Cognitive Structure of Emotions ; Cambridge University Press, 2022; ISBN 978-1-108-84424-6

  13. [13]

    Psychological Parameters for Crowd Simulation: From Audiences to Mobs

    Durupınar, F.; Güdükbay, U.; Aman, A.; Badler, N.I. Psychological Parameters for Crowd Simulation: From Audiences to Mobs. IEEE Trans. Vis. Comput. Graph. 2016, 22, 2145–2159, doi:10.1109/TVCG.2015.2501801

  14. [14]

    How the Ocean Personality Model Affects the Perception of Crowds

    Durupinar, F.; Pelechano, N.; Allbeck, J.; Güdükbay, U.; Badler, N.I. How the Ocean Personality Model Affects the Perception of Crowds. IEEE Comput. Graph. Appl. 2011, 31, 22–31, doi:10.1109/MCG.2009.105

  15. [15]

    Pleasure -Arousal-Dominance: A General Framework for Describing and Measuring Individual Differences in Temperament

    Mehrabian, A. Pleasure -Arousal-Dominance: A General Framework for Describing and Measuring Individual Differences in Temperament. Curr. Psychol. 1996, 14, 261 –292, doi:10.1007/BF02686918

  16. [16]

    A Computational Model of Emotions for Agent -Based Crowds in Serious Games

    Aydt, H.; Lees, M.; Luo, L.; Cai, W.; Low, M.Y.H.; Kadirvelen, S.K. A Computational Model of Emotions for Agent -Based Crowds in Serious Games. In Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology; December 2011; Vol. 2, pp. 72–80

  17. [17]

    Interactive Simulation of Dynamic C rowd Behaviors Using General Adaptation Syndrome Theory

    Kim, S.; Guy, S.J.; Manocha, D.; Lin, M.C. Interactive Simulation of Dynamic C rowd Behaviors Using General Adaptation Syndrome Theory. In Proceedings of the Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games; Association for Computing Machinery: New York, NY, USA, November 9 2012; pp. 55–62

  18. [18]

    Simulating Crowd Evacuation: From Comfort to Panic Situations

    Rockenbach, G.; Boeira, C.; Schaffer, D.; Antonitsch, A.; Musse, S.R. Simulating Crowd Evacuation: From Comfort to Panic Situations. In Proceedings of the Proceedings of the 18th International Conference on Intelligent Virtual Agents; Association for Computing Machinery: New York, NY, USA, August 5 2018; pp. 295–300

  19. [19]

    Simulation of Emotional Contagion Using Modified SIR Model: A Cellular Automaton Approach

    Fu, L.; Song, W.; Lv, W.; Lo, S. Simulation of Emotional Contagion Using Modified SIR Model: A Cellular Automaton Approach. Phys. Stat. Mech. Its Appl. 2014, 405, 380–391, doi:10.1016/j.physa.2014.03.043

  20. [20]

    A Domain-Independent Framework for Modeling Emotion

    Gratch, J.; Marsella, S. A Domain-Independent Framework for Modeling Emotion. Cogn. Syst. Res. 2004, 5, 269–306, doi:10.1016/j.cogsys.2004.02.002

  21. [21]

    Simulation of Crowd Evacuation under Toxic Gas Incident Considering Emot ion Contagion and Information Transmission

    Zou, Q.; Chen, S. Simulation of Crowd Evacuation under Toxic Gas Incident Considering Emot ion Contagion and Information Transmission. J. Comput. Civ. Eng. 2020, 34, 04020007, doi:10.1061/(ASCE)CP.1943-5487.0000889

  22. [22]

    A Multi -Agent Model For Mutual Absorption Of Emotions

    Bosse, T.; Duell, R.; Memon, Z.A.; Treur, J.; Van Der Wal, C.N. A Multi -Agent Model For Mutual Absorption Of Emotions. In Proc eedings of the ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera; ECMS, June 9 2009; pp. 212–218

  23. [23]

    Simulation of the Emotion Dynamics in a Group of Agents in an Evacuation Situation

    Van Minh, L.; Adam, C.; Canal, R.; Gaudou, B.; Tuong Vinh, H.; Taillandier, P. Simulation of the Emotion Dynamics in a Group of Agents in an Evacuation Situation. In Principles and Practice of Multi -Agent Systems; Desai, N., Liu, A., Winikoff, M., Eds.; A Cognition-Emotion-Personality Framework for Modeling Human -Like Awareness and Behavior in Emergency...

  24. [24]

    Modelling Collective Decision Making in Groups and Crowds: Integrating Social Contagion and Interacting Emotions, Beliefs and Intentions

    Bosse, T.; Hoogendoorn, M.; Klein, M.C.A.; Treur, J.; van der Wal, C.N.; van Wissen, A. Modelling Collective Decision Making in Groups and Crowds: Integrating Social Contagion and Interacting Emotions, Beliefs and Intentions. Auton. Agents Multi -Agent Syst. 2013, 27, 52–84, doi:10.1007/s10458-012-9201-1

  25. [25]

    BDI Model - Based Crowd Simulation

    Cho, K.; Iketani, N.; Kikuchi, M.; Nishimura, K.; Hayashi, H.; Hattori, M. BDI Model - Based Crowd Simulation. In Proceedings of the Intelligent Virtual Agents; Prendinger, H., Lester, J., Ishizuka, M., Eds.; Springer: Berlin, Heidelberg, 2008; pp. 364–371

  26. [26]

    Microscopic Dynamics of Pedestrian Evacuation

    Parisi, D.R.; Dorso, C.O. Microscopic Dynamics of Pedestrian Evacuation. Phys. Stat. Mech. Its Appl. 2005, 354, 606–618, doi:10.1016/j.physa.2005.02.040

  27. [27]

    Agent -Based Modeling of a Multi -Room Multi -Floor Building Emergency Evacuation

    Ha, V.; Lykotrafitis, G. Agent -Based Modeling of a Multi -Room Multi -Floor Building Emergency Evacuation. Phys. Stat. Mech. Its Appl. 2012, 391, 2740 –2751, doi:10.1016/j.physa.2011.12.034

  28. [28]

    Modeling the Effect of Leadership on Crowd Flow Dy namics

    Aubé, F.; Shield, R. Modeling the Effect of Leadership on Crowd Flow Dy namics. In Cellular Automata; Sloot, P.M.A., Chopard, B., Hoekstra, A.G., Eds.; Lecture Notes in Computer Science; Springer Berlin Heidelberg: Berlin, Heidelberg, 2004; Vol. 3305, pp. 601–611 ISBN 978-3-540-23596-5

  29. [29]

    ACUMEN: Activity -Centric Crowd Authoring Using Influence Maps

    Krontiris, A.; Bekris, K.E.; Kapadi a, M. ACUMEN: Activity -Centric Crowd Authoring Using Influence Maps. In Proceedings of the Proceedings of the 29th International Conference on Computer Animation and Social Agents; Association for Computing Machinery: New York, NY, USA, February 23 2016; pp. 61–69

  30. [30]

    von Sivers, I.; Templeton, A.; Künzner, F.; Köster, G.; Drury, J.; Philippides, A.; Neckel, T.; Bungartz, H. -J. Modelling Social Identification and Helping in Evacuation Simulation. Saf. Sci. 2016, 89, 288–300, doi:10.1016/j.ssci.2016.07.001

  31. [31]

    Modeling Dynamic Groups for Agent -Based Pedestrian Crowd Simulations

    Qiu, F.; Hu, X. Modeling Dynamic Groups for Agent -Based Pedestrian Crowd Simulations. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology; December 2010; Vol. 2, pp. 461 – 464

  32. [32]

    Prediction of COVID -19 Infection Spread through Agent-Based Simulation

    An, T.; Kim, H.; Joo, C. Prediction of COVID -19 Infection Spread through Agent-Based Simulation. In Proceedings of the Proceedings of the Twenty -Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobi le Computing; Association for Computing Machinery: New York, NY, USA, July 3 2022; pp. 247–252

  33. [33]

    How COVID -19 Is Affecting Pedestrian Modeling and Simulation: The Case of Venice

    Espitia, E.; Gorrini, A.; Vacca, A.; Deponte, D.; Sarvi, M. How COVID -19 Is Affecting Pedestrian Modeling and Simulation: The Case of Venice. Transp. Res. Rec. 2024, 2678, 59–71, doi:10.1177/03611981221088224

  34. [34]

    Modeling Crowd and Trained Leader Behavior during Building Evacuation

    Pelechano, N.; Badler, N.I. Modeling Crowd and Trained Leader Behavior during Building Evacuation. IEEE Comput. Graph. Appl. 2006, 26, 80 –86, doi:10.1109/MCG.2006.133

  35. [35]

    Emotion -Based Crowd Simulation Model Based on Physical Strength Consumption for Emergency Scenarios

    Xu, M.; Li, C.; Lv, P.; Chen, W.; Deng, Z.; Zhou, B.; Manocha, D. Emotion -Based Crowd Simulation Model Based on Physical Strength Consumption for Emergency Scenarios. IEEE Trans. Intell. Transp. Syst. 2021, 22, 6977 –6991, doi:10.1109/TITS.2020.3000607

  36. [36]

    Simulating Heterogeneous Crowds from a Physiological Perspective

    Zheng, L.; Qin, D.; Cheng, Y.; Wang, L.; Li, L. Simulating Heterogeneous Crowds from a Physiological Perspective. Neurocomputing 2016, 172, 180 –188, doi:10.1016/j.neucom.2014.12.103

  37. [37]

    How Simple Rules Determin e Pedestrian Behavior and Crowd Disasters

    Moussaïd, M.; Helbing, D.; Theraulaz, G. How Simple Rules Determin e Pedestrian Behavior and Crowd Disasters. Proc. Natl. Acad. Sci. 2011, 108, 6884 –6888, doi:10.1073/pnas.1016507108

  38. [38]

    A Model of Human Crowd Behavior : Group Inter - Relationship and Collision Detection Analysis

    Musse, S.R.; Thalmann, D. A Model of Human Crowd Behavior : Group Inter - Relationship and Collision Detection Analysis. In Proceedings of the Computer Animation and Simulation ‟97; Thalmann, D., van de Panne, M., Eds.; Springer: Vienna, 1997; pp. 39–51. A Cognition-Emotion-Personality Framework for Modeling Human -Like Awareness and Behavior in Emergency...

  39. [39]

    Autonomous Pedestrians

    Shao, W.; Terzopoulos, D. Autonomous Pedestrians. In Proceedings of the Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation; Association for Computing Machinery: New York, NY, USA, April 29 2005; pp. 19–28

  40. [40]

    Dynamically Altering Agent Behaviors Using Natural Language I nstructions

    Bindiganavale, R.; Schuler, W.; Allbeck, J.M.; Badler, N.I.; Joshi, A.K.; Palmer, M. Dynamically Altering Agent Behaviors Using Natural Language I nstructions. In Proceedings of the Proceedings of the fourth international conference on Autonomous agents; Association for Computing Machinery: New York, NY, USA, March 1 2000; pp. 293–300

  41. [41]

    Multiagent Colla boration in Directed Improvisation

    Hayes-Roth, B.; Brownston, L.; van Gent, R. Multiagent Colla boration in Directed Improvisation. In Proceedings of the ICMAS; 1995; pp. 148–154

  42. [42]

    Populations with Purpose

    Li, W.; Allbeck, J.M. Populations with Purpose. In Proceedings of the Motion in Games; Allbeck, J.M., Faloutsos, P., Eds.; Springer: Berlin, Heidelberg, 2011; pp. 132–143

  43. [43]

    Evacuation Behavior in a Subway Train Emergency: A Video - Based Analysis

    Philpot, R.; Levine, M. Evacuation Behavior in a Subway Train Emergency: A Video - Based Analysis. Environ. Behav. 2022, 54, 383–411, doi:10.1177/00139165211031193

  44. [44]

    Panic, Irra tionality, and Herding: Three Ambiguous Terms in Crowd Dynamics Research

    Haghani, M.; Cristiani, E.; Bode, N.W.F.; Boltes, M.; Corbetta, A. Panic, Irra tionality, and Herding: Three Ambiguous Terms in Crowd Dynamics Research. J. Adv. Transp. 2019, 2019, 9267643, doi:10.1155/2019/9267643

  45. [45]

    How Fire Risk Perception Impacts Evacuation Behavior: A Review of the Literature

    Qin, H.; Gao, X. How Fire Risk Perception Impacts Evacuation Behavior: A Review of the Literature. In Proceedings of the Engineering Psychology and Cognitive Ergonomics; Harris, D., Ed.; Springer International Publishing: Cham, 2019; pp. 396–409

  46. [46]

    Why People „Freeze‟in an Emergency: Temporal and Cognitive Constraints on Survival Responses

    Leach, J. Why People „Freeze‟in an Emergency: Temporal and Cognitive Constraints on Survival Responses. Aviat. Space Environ. Med. 2004, 75, 539–542

  47. [47]

    Intentionality and Fatality during the King‟s Cross Underground Fire

    Donald, I.; Canter, D. Intentionality and Fatality during the King‟s Cross Underground Fire. Eur. J. Soc. Psychol. 1992, 22, 203–218, doi:10.1002/ejsp.2420220302

  48. [48]

    Qualitative Overview of Some Important Factors Affecting the Egress of People in Hotel Fires

    Graham, T.L.; Roberts, D.J. Qualitative Overview of Some Important Factors Affecting the Egress of People in Hotel Fires. Int. J. Hosp. Manag. 2000, 19, 79 –87, doi:10.1016/S0278-4319(99)00049-3

  49. [49]

    Maladaptive Behavior in Survivors: Dysexecutive Survivor Syndrome

    Leach, J. Maladaptive Behavior in Survivors: Dysexecutive Survivor Syndrome. Aviat. Space Environ. Med. 2012, 83, 1152–1161, doi:10.3357/ASEM.3199.2012

  50. [50]

    Understanding Mass Panic and Other Collective Responses to Threat and Disaster

    Mawson, A.R. Understanding Mass Panic and Other Collective Responses to Threat and Disaster. Psychiatry 2005, 68, 95–113, doi:10.1521/psyc.2005.68.2.95

  51. [51]

    Human Behaviour during an Evacuation Scenario in the Sydney Harbour Tunnel

    Burns, P.; Stevens, G.; Sandy, K.; Dix, A.; Rap hael, B.; Allen, B. Human Behaviour during an Evacuation Scenario in the Sydney Harbour Tunnel. Aust. J. Emerg. Manag. 2013, 28, 20–27, doi:10.3316/informit.136060512142252

  52. [52]

    Evacuation Experiment in a Road Tun nel: A Study of Human Behaviour and Technical Installations

    Nilsson, D.; Johansson, M.; Frantzich, H. Evacuation Experiment in a Road Tun nel: A Study of Human Behaviour and Technical Installations. Fire Saf. J. 2009, 44, 458–468, doi:10.1016/j.firesaf.2008.09.009

  53. [53]

    Analysis of the Evacuation of the World Trade Center Towers on September 11, 2001

    Averill, J.D.; Peacock, R.D.; Kuligowski, E.D. Analysis of the Evacuation of the World Trade Center Towers on September 11, 2001. Fire Technol. 2013, 49, 37 –63, doi:10.1007/s10694-012-0260-2

  54. [54]

    Risk Communication and the Willingness to Follow Evacuation Instructions in a Natural Disaster

    Rød, S.K.; Botan, C.; Holen, A. Risk Communication and the Willingness to Follow Evacuation Instructions in a Natural Disaster. Health Risk Soc. 2012, 14, 87 –99, doi:10.1080/13698575.2011.641522

  55. [55]

    Commuter Characteristics in Mass Rapid Transit Stations in Singapore

    Yeo, S.K.; He, Y. Commuter Characteristics in Mass Rapid Transit Stations in Singapore. Fire Saf. J. 2009, 44, 183–191, doi:10.1016/j.firesaf.2008.05.008

  56. [56]

    The Nature of Collective Resilience: Survivor Reactions to the 2005 London Bombings

    Drury, J.; Cocking, C.; Reicher, S. The Nature of Collective Resilience: Survivor Reactions to the 2005 London Bombings. Int. J. Mass Emergencies Disasters 2009, 27, 66–95, doi:10.1177/028072700902700104

  57. [57]

    Everyone f or Themselves? A Comparative Study of Crowd Solidarity among Emergency Survivors

    Drury, J.; Cocking, C.; Reicher, S. Everyone f or Themselves? A Comparative Study of Crowd Solidarity among Emergency Survivors. Br. J. Soc. Psychol. 2009, 48, 487–506, doi:10.1348/014466608X357893

  58. [58]

    The Role of Social Identity Processes in Mass Emergency Behaviour: An Integrative Review

    Drury, J. The Role of Social Identity Processes in Mass Emergency Behaviour: An Integrative Review. Eur. Rev. Soc. Psychol. 2018, 29, 38 –81, doi:10.1080/10463283.2018.1471948. A Cognition-Emotion-Personality Framework for Modeling Human -Like Awareness and Behavior in Emergency Evacuations 40

  59. [59]

    Fire Alarm in a Public Building: How Do People Evaluate Information and Choose an Evacuation Exit? Fire Mater

    Benthorn, L.; Frantzich, H. Fire Alarm in a Public Building: How Do People Evaluate Information and Choose an Evacuation Exit? Fire Mater. 1999, 23, 311 –315, doi:10.1002/(SICI)1099-1018(199911/12)23:6<311::AID-FAM704>3.0.CO;2-J

  60. [60]

    Experimental and Modeling Study on Evacuation under Good and Limited Visibility in a Supermarket

    Cao, S.; Fu, L.; Wang, P.; Zeng, G.; Song, W. Experimental and Modeling Study on Evacuation under Good and Limited Visibility in a Supermarket. Fire Saf. J. 2018, 102, 27–36, doi:10.1016/j.firesaf.2018.10.003

  61. [61]

    Dynamics of Crowd Disasters: An Empirical Study

    Helbing, D.; Johansson, A.; Al -Abideen, H.Z. Dynamics of Crowd Disasters: An Empirical Study. Phys. Rev. E 2007, 75, 046109, doi:10.1103/PhysRevE.75.046109

  62. [62]

    From Crowd Dynam ics to Crowd Safety: A Video -Based Analysis

    Johansson, A.; Helbing, D.; Al -Abideen, H.Z.; Al -Bosta, S. From Crowd Dynam ics to Crowd Safety: A Video -Based Analysis. Adv. Complex Syst. 2008, 11, 497 –527, doi:10.1142/S0219525908001854

  63. [63]

    Yang, X.; Wu, Z.; Li, Y. Difference between Real -Life Escape Panic and Mimic Exercises in Simulated Situation with Implications to the St atistical Physics Models of Emergency Evacuation: The 2008 Wenchuan Earthquake. Phys. Stat. Mech. Its Appl. 2011, 390, 2375–2380, doi:10.1016/j.physa.2010.10.019

  64. [64]

    Uncertainty Promotes Information -Seeking Actions, but What Information? Cogn

    Keller, A.M.; Taylor, H.A.; Brunyé, T.T. Uncertainty Promotes Information -Seeking Actions, but What Information? Cogn. Res. Princ. Implic. 2020, 5, 42, doi:10.1186/s41235-020-00245-2

  65. [65]

    The Variation of Pre-Movement Time in Building Evacuation

    Forssberg, M.; Kjellström, J.; Frantzich, H.; Mossberg, A.; Nilsson, D. The Variation of Pre-Movement Time in Building Evacuation. Fire Technol. 2019, 55, 2491–2513, doi:10.1007/s10694-019-00881-1

  66. [66]

    An Approach for Modeling Human Cognitive Behavior in Evacuation Models

    Pires, T.T. An Approach for Modeling Human Cognitive Behavior in Evacuation Models. Fire Saf. J. 2005, 40, 177–189, doi:10.1016/j.firesaf.2004.10.004

  67. [67]

    Human Factors in Evacuation Simulation, Planning, and Guidance

    Hofinger, G.; Zinke, R.; Künzer, L. Human Factors in Evacuation Simulation, Planning, and Guidance. Transp. Res. Procedia 2014, 2, 603–611, doi:10.1016/j.trpro.2014.09.101

  68. [68]

    -T.; Lin, B.S

    Chang, B.-L.; Chang, H. -T.; Lin, B.S. -M.; Hsiao, G.L. -K.; Lin, Y. -J. Factors Affecting Emergency Evacuation: Floor Plan Cognition and Distance. Sustainability 2023, 15, doi:10.3390/su15108028

  69. [69]

    Visitor Responses to Emergency Evacuation: A Human Behavior Approach

    Akbarzadeh, O.; Moshashaei, P.; Golzad, H.; Liu, H.; Alizadeh, S.S. Visitor Responses to Emergency Evacuation: A Human Behavior Approach. Health Promot. Perspect. 2025, 15, 370–383, doi:10.34172/hpp.025.44366

  70. [70]

    Memory Retention of Spatial Knowledge in Fire Evacuation-and Safety Training

    Menzemer, L.W.; Gwynne, S.M.; Ronchi, E. Memory Retention of Spatial Knowledge in Fire Evacuation-and Safety Training. Fire Saf. J. 2026, 104799

  71. [71]

    Studying the Dynamics of Crowd Panic Propagation dur ing Emergency Evacuation

    Li, Y.; Liu, C.; Yang, Y. Studying the Dynamics of Crowd Panic Propagation dur ing Emergency Evacuation. J. Saf. Sci. Resil. 2025, 6, 100207, doi:10.1016/j.jnlssr.2025.03.001

  72. [72]

    Integrating Panic Behavior into Agent -Based Subway Evacuation Simulations

    Kim, H.; Choi, J.; Ahn, B.; Lee, J. Integrating Panic Behavior into Agent -Based Subway Evacuation Simulations. Buildings 2025, 15, doi:10.3390/buildings15213990

  73. [73]

    Development of an Evacuation Model Considering the Impact of Stress Variation on Evacuees under Fire Emergency

    Cao, R.F.; Lee, E.W.M.; Yuen, A.C.Y.; Chan, Q.N.; Et., A. Development of an Evacuation Model Considering the Impact of Stress Variation on Evacuees under Fire Emergency. Saf. Sci. 2021, doi:10.1016/j.ssci.2021.105232

  74. [74]

    A Research Roadmap for Evacuation Models Used in Fire Safety Engineering

    Ronchi, E. A Research Roadmap for Evacuation Models Used in Fire Safety Engineering. In Proceedings of the Fire and Evacuation Modelling Technical Conference 2016; Torremolinos, Spain, November 2016

  75. [75]

    Patient and Impatient Pedestrians in a Spatial Game for Egress Congestion

    Heliövaara, S.; Ehtamo, H.; Helbing, D.; Korho nen, T. Patient and Impatient Pedestrians in a Spatial Game for Egress Congestion. Phys. Rev. E 2013, 87, 012802, doi:10.1103/PhysRevE.87.012802

  76. [76]

    3D Visual Simulation of Individual and Crowd Behavi or in Earthquake Evacuation

    Liu, T.; Liu, Z.; Ma, M.; Chen, T.; Liu, C.; Chai, Y. 3D Visual Simulation of Individual and Crowd Behavi or in Earthquake Evacuation. SIMULATION 2019, 95, 65 –81, doi:10.1177/0037549717753294

  77. [77]

    Emotion Contagion in Agent -Based Simulations of Crowds: A Systematic Review

    van Haeringen, E.S.; Gerritsen, C.; Hindriks, K.V. Emotion Contagion in Agent -Based Simulations of Crowds: A Systematic Review. Auton. Agents Multi-Agent Syst. 2022, 37, 6, doi:10.1007/s10458-022-09589-z. A Cognition-Emotion-Personality Framework for Modeling Human -Like Awareness and Behavior in Emergency Evacuations 41

  78. [78]

    Impacts of Stress on Workers‟ Risk -Taking Behaviors: Cognitive Tunneling and Impaired Selective Attention

    Pooladvand, S.; Hasanzadeh, S. Impacts of Stress on Workers‟ Risk -Taking Behaviors: Cognitive Tunneling and Impaired Selective Attention. J. Constr. Eng. Manag. 2023, 149, 04023060, doi:10.1061/JCEMD4.COENG-13339

  79. [79]

    Representing Crowd Behaviour in Emergency Planning Guidance: „Mass Panic‟ or Collective Resilience? Resilience 2013, 1, 18 –37, doi:10.1080/21693293.2013.765740

    Drury, J.; Novelli, D.; Stott, C. Representing Crowd Behaviour in Emergency Planning Guidance: „Mass Panic‟ or Collective Resilience? Resilience 2013, 1, 18 –37, doi:10.1080/21693293.2013.765740

  80. [80]

    Viewpoint: Terrorism and Dispelling the Myth of a Panic Prone Public

    Sheppard, B.; Rubin, G.J.; Wardman, J.K.; Wesse ly, S. Viewpoint: Terrorism and Dispelling the Myth of a Panic Prone Public. J. Public Health Policy 2006, 27, 219–245, doi:10.1057/palgrave.jphp.3200083