Ensimag

IRL Report

The psycho-social theories inemergency evacuation agent-based

simulation

Author:

Johan Girod

Supervisor:

Julie Dugdale

May 18, 2012

Abstract

This work is a state of the art of the current work concerning simulation ofcrowd behaviour in emergency evacuation. More particularly, we interest inagent based social simulation in this domain. We see the outcomes of studies inthe social psychology �eld, about the human and social behaviour of individualsin disasters or emergency situation. We discuss the di�erent models explainingthis behaviour. Particularity, we focus on the three main theories in this �eld.Those are known as panic theory, a�liation and normative theory and socialidentity theory. We see how they were incorporated in agent based simulationmodels for evacuation. We �nish by discussing the possible future directions foragent based simulation for evacuation.

Keywords

Mass emergency evacuation, crowd behaviour, disaster, agent based simulation,psycho-social theories.

Contents

1 Motivations 2

2 Overview of social psychology literature 32.1 Historic of the di�erent theories . . . . . . . . . . . . . . . . . . . 4

2.1.1 Mass 'Panic' Theory . . . . . . . . . . . . . . . . . . . . . 42.1.2 A�liation and Normative Approach . . . . . . . . . . . . 52.1.3 Social Identity Approach . . . . . . . . . . . . . . . . . . 6

2.2 Other observations . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 State of the art 93.1 A brief categorization of evacuation simulation tools . . . . . . . 9

3.1.1 Fluid or particle systems . . . . . . . . . . . . . . . . . . . 93.1.2 Matrix based systems . . . . . . . . . . . . . . . . . . . . 103.1.3 Agent based systems . . . . . . . . . . . . . . . . . . . . . 11

3.2 Review of the di�erent agent based models . . . . . . . . . . . . 123.2.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Physical environment . . . . . . . . . . . . . . . . . . . . . . . . . 143.4 Reactive agent simulation . . . . . . . . . . . . . . . . . . . . . . 163.5 Cognitive agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.6 Case study : MASSEgress . . . . . . . . . . . . . . . . . . . . . . 183.7 Point of interest in other simulation tools . . . . . . . . . . . . . 22

4 Recapitulation and perspective 254.1 Recapitulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1

1 Motivations

The amount of time spent in public spaces has risen throughout the year. Wespent the most part of our day in public spaces. In the same time, those centerhave tend to become more and more larger, with a capacity always going up.Thus, the concerns of safety for those place is central, because of the number oflife that is at stake. Besides, the report of disasters during emergency evacuationinvolving losses of human lives has highlighted the need for planning a reliableand e�ective evacuation process. Doing so, we could avoid tragedy like theKing's Cross Fire in 1987 or also the Rhode Island in 2003 where more than onehundred people died.

Consequently, having a good evacuation design is a crucial part in the con-ception of those facility. And for assessing the quality of such a design, severalmeans exist. A �rst approach is to extract data and measurement about evacua-tion time from real life simulation, also known as evacuation drills. But those areexpensive and di�cult to organize. Moreover, they can only be performed oncethe building is achieved. A important drawback, since it prevent any change tobe made from an architectural point of view. A second solution consist in de-sign the building using codes, like the International Building Codes (2001). Forinstance, those provide advices on the location and number of emergency exit.This approach takes place upstream the designing process, reducing drasticallythe costs, in regards from the previous solution. However, the e�ciency of doingso can be discuss. For instance, it has been report that during an evacuationprocedure, a great number of exit door remained unused. Yet the "manuals"doesn't provide answer for those problems.

The last approach is to use simulation tools. The basic idea is to simulatethe behaviour of a virtual crowd in a digital environment similar to the realbuilding. This solution is democratizing, and have encounter a vested interestamong building designers. Indeed, ideally, this approach cumulates the accuracyof a real life simulation with the advantage of assessing the quality of the designbefore the construction. Those simulation tools provide quanti�ed outcomes,that can be compared, and therefore be used to choose between di�erent design.

But the safety concerns are not circumscribed only to the design. It is alsoimportant to have stewards and sta� kept trained, in order for them to reactadequately, and quickly when a disaster occurs. Beside from exercise, whichcan be costly and hard to organized, it could be useful to have them trainedwith simulation tools( such as [SVLS06]), in the same way than plane pilots aretrained before piloting a true plane.

Simulating crowd behaviour during an emergency have many applicationsin every day life. But, like every simulation system, they need to have outcomethat are representative of what happened in the real situation. That means thatthey should be based on theories that have been tested, and validated by thescienti�c community of the subject studied. Ideally, simulation models should

2

be based on sound social psychological theories of crowd behaviour. Hence thefollowing question : What are the social theories explaining crowd behaviourduring emergency evacuation ?

In order to answer that, one must read about the most recent researches insocial psychology on crowd behaviour. Crowd behaviour is a wide and proli�c�eld in social psychology, which has bene�t from great enthusiasm in the lastdecade. The knowledge on crowd response to an emergency or disaster eventhave been increased and speci�ed, thanks to a great number of studies thathave been conducted throughout the years (for instance [AWV98] [CD08] or[ATGLH11]. Well-de�ned and established patterns were emphasized, based inthe analysis of emergency evacuation scenarios that happened in the real life.For instance, it has been found that people tend to evacuate through the sameway as they came in [Maw05]. Such patterns have been explained with di�erenttheories. Having a simulator which do not outcome such patterns would there-fore be considered as a mistake, or even a wrongdoing. Indeed, in this case, theoutput of the simulator would not conform to what has been observed in realsituation, in the past studies. Hence the following interrogations :

In what way evacuation simulation tools shows (or not) such patterns?What are the social-psychological theories underneath those simulation tools?

In section 2, we will do an overview of social psychological theories. Oncethe di�erent theories speci�ed, we will focus on modelling. In section 2, part 1we will see brie�y the principle of two categories of simulation (ie particle/�owapproach and cellular automata), and underline their limitations. In section 2part 2, we will focus on agent based simulation tools. We will study the dif-ferent types of agent based simulation, by analysing in depth di�erent models,corresponding to three di�erent approaches. We will have the constant con-cern of exposing the links they have with the di�erent social theories studiedbefore. Section 3 provides a summary about strengths and weaknesses of thecurrent agent based evacuation simulations. It also gives a set of possible futuredirections for the �eld.

2 Overview of social psychology literature

In this section, we will overview the di�erent social psychological theories thathave been developed to explain crowd behaviour. Three have been selected,because of their importance in the �eld. Those are the "mass panic" theory,the a�liation and normative approach, and the social identity theory. Thelast section contain a set of remarkable observations that have been made incrowd. These informations cannot be explained directly from the three theoriesspeci�ed before, but are related to other known social psychological theories.

3

2.1 Historic of the di�erent theories

2.1.1 Mass 'Panic' Theory

The collective response to threats and disasters is often portrayed as tendingtoward hysteria and social breakdown, with 'mass panic' behaviour. Mass emer-gency response is commonly view as irrational, inappropriate, and excessive.This common belief is shared widely, and tend to part of the popular imag-ination when it is about mass emergency and disaster, as often portrayed inHollywood disaster �lm. The mass panic concept is also widely spread amongscientists who have not researched the phenomena [Qua01].

For Mawson[Maw05], the term 'panic' refers to inappropriate (or excessive)fear and/or �ight and highly intense fear/or �ight.

The 'mass panic' theory is an explanatory framework based on the commonview. It state that, since the crowd is less intelligent and more driven by sim-ple emotions [LB95] , crowd reaction to an emergency will be disproportionateregarding the danger, and will spread quickly and widely throughout all theindividuals gathered. Besides, since 'instinct' is said to overwhelm socialization,personal survival become the only motivator of the individuals, social bondswill be dissolved, resulting in mindless, instinctive, irrational and self-centredbehaviour. People will experience an intense fear, always directed toward some-thing speci�c, that can be designated [Qua54]. This behaviour will leads tostampede, violence, abandon of family members ...

The conditions of panic have been clearly de�ned by [Qua54]. According towhat has been designated as the "entrapment theory" of mass panic, panic willoccur when the following conditions are satis�ed. There is the perception of amajor physical danger (present or imminent). There is the feeling of possibleentrapment (for instance if escape route are either limited or rapidly closing).Finally, there is a "perception of collective powerlessness, and a feeling of indi-vidual isolation in a crisis situation" [Qua54].

However, this theory has faced sharp criticism. Indeed, studies conductedsince the proposition of traditional panic theories [Maw05] [DC07] [CD08] [ATGLH11][AWV98] have showed, that during a mass emergency situation, panic is actu-ally very rare. Drury and Cocking [CD08] showed that the actual behaviour incrowd reaction to disasters con�icted with the prediction of the panic model inat least three ways:

1. Antisocial or sel�sh behaviours are rare, even when the situation meet allthe prerequisites for panic. If they occur, they stayed localised in spaceand time, and do not spread to the others.

2. Evacuation are often orderly, with people queuing to get out (rather thanstampeding), despite the clear threat of death

4

3. Helping behaviour and co-operation are common

Their conclusion is based on three di�erent approaches: laboratory studies, twointerviews (one of di�erent events, the other limited to the 7th July Londonbombings). Aguirre et al. [ATGLH11], studied the Rhodes Island incident,where more than one hundred people loss their lives. Based on the police in-terview and from the press reports after the disaster, they emphasized the factthat mass panic, even in this extreme condition, was not found. Instead peopleco-operated, and mutual aid was frequent.

In order to explain this gap between the reality and the popular belief, Sime[Sim80] suggests that the concept of panic is often used as a way of blamingthe crowd in the aftermath of a disaster, especially if losses of human lives haveoccurred. Indeed, very often, the losses of lives are the result to a bad or non-existent communication with the crowd, and therefore could have been avoidedwith a good communication [OCC+10]. The judgement of panic is often maderetrospectively, and what may be describe as inappropriate or excessive maynot be judged by the participants themselves [Maw05].

Instead of being antisocial, crowd behaviour is rational and socially struc-tured.

2.1.2 A�liation and Normative Approach

The a�liation model state that, "In fact, the typical response of threats anddisasters is not to �ee but to seek the proximity of familiar persons and places;moreover, separation from attachment �gures is a greater stress factor thanphysical danger"[Maw05]. Indeed, studies of mass evacuation have found thatfamily groups do not break down in an emergency, and moreover, that peopleput themselves at risk of injury and even death in their search for other peoplewho were dear to them [ATGLH11]. Indeed, members of a same group will exittogether and wait for each others, even if it means putting their own life at risk.[Maw05]. This theory also explain why people prefer to leave using a familiarexit (usually the same way as they came in) [Maw05], even if this solution ismore dangerous than taking emergency exit.

The normative approach stresses that behaviour in emergencies is structuredby the same social rules and roles that operate in the every day life. For ex-ample, a study of the Berverly Hills Supper Club �re reported that men helpedwomen more than women helped men, suggesting that traditional gender roleswere not abandoned (Johnson 1988, cited in [DC07]).

The a�liation and normative approach explain the sociality of the crowd asderiving from pre-existing social relation. It means that social structure (theirnorm and roles) are maintained.

5

Despite the fact that these models are more compliant with reality than"mass panic" theories, they have come to be criticized. Indeed, they fail toexplain the altruistic and helping behaviour often observed in large crowds (andsometimes self-sacri�ce), between people that didn't have any connection before[DC07]. Furthermore, the a�liation models suggest that, where in presence ofunfamiliar places and unfamiliar people, there will be mass panic, yet literatureindicates mass panic occurs very rarely; thus the lack of panic observed inemergencies cannot be explained (Aguirre 2005 cited in [OCC+10]).

2.1.3 Social Identity Approach

The social identity approach is intended to �ll this gaps. It is based on theself-categorisation theory (SCT). SCT is a mainstream social psychology theorythat has been used with success to explain a diverse range of group behaviour(such as nationalism, conformity, leadership and follower-ship) [DCR09]. Thebasic premise of self-categorisation theory is that shared social identity deter-mines social behaviour. Social identity is multiple, we each have not only apersonal identity but as many social identities as we have memberships of socialgroups or categories.

Using the SCT to explain crowd behaviour in emergency therefore has theadvantage to reconcile the �eld with the social-psychological domain, especiallythe group behaviour study [DS11].

Cocking and Drury [CD08] [DC07] stress that the aggregate, a term de�ningcollection of non united people (such as crowd of people milling about a shop-ping centre) might become a psychological crowd, with a shared identity, whenfaced to a common experience of an external threat.

In short, the shared fate could create a sens of "we-ness" among those whoare similarly theatened (i.e "all in the same boat"), according to which socialbonds are created and strengthened. And those social-bonds, far from beinglow, will harden over time, and be maintained even when the threat increase[CD08]. In fact, this shared identity provides crowd members with perceptionsof unity and expectations of mutual support [CD08], and helps to reduce thestress level (Drury and Reicher cited in [OCC+10]. Consequently, the crowd, asa whole, act as a source of strength for individual crowd members [DCR09].

Beside, the shared identity that emerge during disaster has a great resilience.That means that the social bonds will stay even after the threat is gone. Impli-cation for authorities are large. For instance, the crowd could help the medicalcrew taking care of injured and shocked people, instead of being directly evac-uated. This shared identity may also be useful for therapeutic care via groupsession in order to cure the eventual psychological trauma [DC07].

In summary, mass emergency behaviour is cognitive. That means that peo-ple's behaviour in emergency evacuation is a�ected by their knowledge of the

6

nature of the emergency and the physical layout of the site. Consequentlythey will seek further information and guidance before but especially during theevent.

As the crowd behaviour is cognitive and meaningful, communication withthe crowd is crucial [DC07], because people will respond co-operatively and inan orderly manner, rather than over-emotionally. Communication (i.e informingpeople of the right thing, at the right time and in the right way) is thereforedecisive in managing disaster situations. And if communication is crucial, thisin turn means prioritizing system of communication over physical features suchas exits width [CD08], when preparing evacuation design.

Besides, trust between the crowd and the authority a�ects the nature ofevacuation behaviour. The crowd has to be con�dent in the source of infor-mation. Therefore, giving the crowd contradictory information, or withholdinginformation from it will break this con�dence. Consequently, the authoritiesmay not be trusted when giving directions afterwards [DC07] .

In summary, mass panic approach emphasizes the dissolution normative anda�liation approaches stress their maintenance, and social identity approachstate a creation of such bonds. The �rst theory has been disavowed by the so-cial psychology community, whereas the two last theories can be complementary.Indeed, they both relied on the assumption that crowd behaviour is rational andsocially structured.

The main lesson provided by this analysis is that 'panic' is actually veryrare. Instead of being antisocial, irrational, crowd behaviour during an emer-gency is at the contrary structured, organized, self-conscious and socially linked.Consequently, behaviour such as queuing, routine courtesy and mutual aid arewidespread [CD08] [ATGLH11] [DCR09]. This can be explained by normativeand a�liation approach (the people will continue to take care of their relatives,and the social link between people will be maintain). But also by the socialidentity approach (people will tends to help others, as they have the feeling tobelong in a same group, and share the same identity).

2.2 Other observations

Some other phenomenon have been observed during an evacuation. Those arenot explained by the precedent theories. But they provide useful informationsabout people reactions in emergency.

• It has been reported that they may be some leader in�uence in emergen-cies (as noticed by Drury & Cocking [DC07]). When faced to uncertain orconfusing situations, people will tend to look to those they think shouldknow what to do. And as social structure is preserved, this role is oftenassumed by stewards (police o�cer, �remen, �oor responsible). That im-

7

plies the need of a well-trained and prepared crew, in communication withcrowd. The leader / follower behaviour have been explained with successusing Self-Categorisation Theory (stated in [DC07]), from which derivedthe social identity explained before.

• Herding behaviour, (i.e. people that make a choice based on the obser-vation of what others do) can also be observed [DC07]. This behaviourcan be explained by the concept of social proof. Social proof, also knownas informational social in�uence, is a psychological phenomenon wherepeople assume the actions of others re�ect correct behaviour for a givensituation. This e�ect is prominent in ambiguous social situations wherepeople are unable to determine the appropriate mode of behaviour, and isdriven by the assumption that surrounding people possess more knowledgeabout the situation. Consequently, herding behaviour will be favoured inuncertain context, when people are not given enough information.

• "When people die in �re, it's not because of panic, it's more likely tobe the lack of panic" (Townsend, 2003 cited in [CD08]). It has been no-ticed that people often under-reacted to the danger [AWV98], and failedto take appropriate evasive action when needed [Maw05]. Individuals arepredisposed to believe a situation is normal for as long as possible. Clearinformations and speci�c instructions are a good way to override theirscheme [OCC+10].

• People will react di�erently when faced to a same threat, depending ontheir personality, their social role (head of the family, security agent, �re-man, teacher ...), or their physical ability (age, disability). Consequently,a crowd has to be seen as mainly heterogeneous [OCC+10].

2.3 Summary

It has been showed from previous studies [Maw05], [AWV98], [ATGLH11] thatthe crowd behaviour when facing an emergency or a threatening situation isnot antisocial and irrational, with an immediate unconscious and re�exive �ightreaction of people. In contrary, people will communicate, share informations,seek the proximity of familiar people, discuss the di�erent options, and makechoice based on those decisions. Consequently, when designing the evacuationprocedure of building, one might have in mind that people involved in suchevent are capable of processing and take into account the information provided.Such approach, based on the recent outcome of social-psychological scienti�ccommunity, began to be recommended in recent report destined to architectand organizer of crowded event (such as [OCC+10]).

8

3 State of the art

Now that we have detailed the di�erent theories explaining crowd behaviour, wehave the possibility to confront the di�erent simulation tools with them. In thissection, we will do an overview of the di�erent methods existing for simulatingcrowd movement during evacuation. In a �rst time, we will discuss brie�y the�rst approaches (i.e particle/�uid approach and cellular automata), and em-phasize their limitations. We will then focus on Agent Based Social Simulation(ABSS), because they exhibit strengths in simulating complex mechanisms asthe crowd �ow. We will attempt to discuss in depth some of the models used.

3.1 A brief categorization of evacuation simulation tools

In the past, a certain number of evacuation simulation tools have been devel-oped. Generally speaking, most existing models can be categorized into �uid orparticle systems, matrix-based system, and agent-based system.

3.1.1 Fluid or particle systems

The �rst emergency simulation tools used an analogy between crowd movementand �uid and particle motions. In the �ow-based approach (for instance EVAC-NET4 cited in [SA04]), the physical environment is represented as a networkof nodes. Nodes represent physical structure, such as rooms, hallway or stairs.The user de�ne the content of each node, specifying how many people a nodemay contain at the maximum, and how many people it contains at the beginningof the simulation. The destination nodes are speci�ed, i.e the exit where peoplecan evacuate. Each arc is decorated with the average travel time between thetwo nodes it links, and with the maximum �ow capacity (ie how many peoplecan use the link simultaneously).

The particle model see each people as a particle. Developed by [HFV00],it is also known as "Social force model". The basic idea is that the humanbody react not only to physical forces (like wall, or other agent body), but alsoundergoes and create social forces, representing the observed will for humanpeople to maintain their personal space. In this model, each people is seen assubject of social forces and physical forces. Physical forces are relative to theenvironment and to the physical collisions with other people. Social forces re-sult from the need of each individual to maintain his personal space around him.The future position of people is known by computing the Newton equation foreach people. This models have been successful modelling local phenomena dueto crowd moving. Such as "the faster is slower" e�ect, ie the fact that whenpeople want to increase their speed, the general e�ect is the deceleration of the�ow. The models exhibit the behaviour of a �ow of people, when passing ona corridor which enlarged and narrowed (cf �gure 1). When passing by such acon�guration, the �ux of people is slowed down. This slowdown is proportional

9

Figure 1: The narrowing e�ect (slowing of the �ow) with the social force model

to the angle of the corridor enlargement.

However, some criticisms have been made against those models. As Stillnoted [Sti00] "the laws of crowd dynamics have to include the fact that peopledo not follow the laws of physics; they have a choice in their direction, haveno conservation of momentum and can stop and start at will". Indeed, particleand �uid models are not able to represent such common and evacuation timein�uential pattern in crowd like herding, queuing, or bidirectional �ow (peoplemoving in opposite direction).

A second limitation that applied to the social force model, is the fact thatit relies on the assumption of mass panic ("panicking individuals tend to showmaladaptive and relentless mass behaviour like jamming and life-threateningovercrowding" [HFV00]). Yet, we saw that panic was extremely rare in evac-uation procedure. Thus, this model, when focusing on a rare event in crowd,may produce wrong results when applied to the general situation, i.e. orderlyevacuation of buildings.

3.1.2 Matrix based systems

Also referred as cellular automata models, the notion behind them is the dis-cretisation of space. Floor is represented by a certain number of cells, which canbe free, or occupied (people or obstacle). People transit from cell to cell basedon the occupancy rule de�ned for the cell. The simulator EGRESS [Aut02] isbased on this principle.

It was suggested that they su�er from the di�culties of simulating crowdcross �ows [Sti00]. Crowd cross �ows are phenomena that happen when thereare two or more �ows of people going in di�erent directions. For instance in acorridor, there might be two distinct �ows of people, going in each direction.Furthermore, the people simulated have no memory, and their movements de-pend entirely from their present environment, disregarding fundamental human

10

motivators such as emotion, experience or communication.

The third and last way of modelling crowd behaviour is to use a multi agentsimulation paradigm. We shall now see what is behind that term, and why itpresents a great opportunity for the modelisation of crowd evacuation.

3.1.3 Agent based systems

De�nition According to a de�nition of Wooldridge and Jennings (cited in[Woo09]):

An agent is a computer system that is situated in some environment,and that is capable of autonomous action in this environment inorder to meet its design objectives".

This de�nition is quite large, as it includes as well a thermostat and a realisticsimulated human being. Indeed, the thermostat can be considered as an agent,since it is an entity situated in a room. It's capable of autonomous action i.eincreasing and decreasing the power of heating; in order to meet its design ob-jective, in this case, keeping the temperature of the room at a constant value.We need to have further classi�cation, for better understanding. Di�erent prop-erties have been propose by Franklin in [FG97], for a better categorization ofagents. Those are resumed in table 1.

Table 1: Taxonomy for autonomous agent (Franklyn [FG97])Property Description

ReactiveResponds in a timely fashion to changes in theenvironment

Autonomous Exercises control over its own actions

Goal OrientedDoes not simply act in response to the envi-ronment

Communicative Communicates with other agents

LearningChanges its behaviour based on its previousexperience

Character Believable "personality" and emotional state

A system including several agents is called a multi-agent system.

Multi-agent have been applied with success in the �eld of simulation, andmore especially the �eld of social simulation. They have been useful to simulatevarious phenomena such as the racial segregation in American cities (ThomasShelling, cited in [Gil08]), opinion dynamics, electricity market or consumerbehaviour (all cited in [Gil08]). We will now see what is the principle of thissimulation paradigm.

11

Agent Based Social Simulation (ABSS) In an agent-based social simula-tion, agents represent social actors, like �rms, nation states or people. The mainconcept behind a multi agent simulation is to simulate an arti�cial world whichis made of computational interactive entities (agent). The idea is to transposesocial entities and interactions among them, and the environment in which theyevolve from the real world into the arti�cial one. Social entities are representedas agents. In our case, agents will be human beings and the environment willbe the place where the disaster (or emergency) takes place.

An important phenomenon associated with the agent-based social simulation(and from multi-agent systems as a matter of fact) is the notion of emergence,ie the apparition of macro or societal patterns or structures from the interactionbetween the agents at a micro level. The phenomenon of emergence stems fromthe complex system theories. Complex systems are systems of interconnectedentities that, as a whole, exhibit properties that are not evident when regardingthe properties of the individual parts. Those properties are called emergent be-cause they did not arise from the description of individual parts. The crowd maybe considered as a complex system which exhibit behaviour that are not obviouswhen interesting in the psychology of one particular person; including queuingat intersection[ref], herding behaviour, leadership in�uence, group formation.Finally, the average evacuation time of a building can itself be considered as anemergent phenomenon. Indeed, it arise from the interactions of the individualwithin the crowd, and cannot be asserted from one individual.

Another advantage of ABSS, is that it allows the simulation of heterogeneoussystems, with heterogeneous agents. The two previous approaches (�ow/particleand cellular automata) failed to this point, making the incorrect assumption ofthe homogeneity of the crowd. Indeed the crowd is by nature heterogeneous,including various psychological and physical pro�le such as men, women or chil-dren. In ABSS, each agent can be assigned individual attributes such as gender,age, mobility, body size and walking speed. Agent are also able to adapt theirbehaviour according to environmental circumstances.

Agent based models therefore appear with a great potential in the simulationof evacuating crowd. Their ability of modelling complex and realistic individualbehaviour at the micro level[SA04], to produce outcomes at the general level issomething that is quite interesting. We will see now how this ability is used inthe di�erent existing agent based simulations.

3.2 Review of the di�erent agent based models

3.2.1 Architecture

Agent based simulation tools are organized according an architecture that isquite common. Five components are usually used when designing an agentbased program. Those are the simulation engine, the visualizer, the geometric

12

Figure 2: A common architecture (found in MASSEgress [Pan06])

engine, the population generator and the events recorder. Such a framework canbe seen in Figure 2. The main component is naturally the simulation engine.This unit is responsible for the simulation of the crowd. In order to do this,it simulates the behaviour of each agent individually. One way of doing so isto compute the next state of agent, one after the other, but in a random order[Pan06].

Technically, agent based simulations provide as outcome the most completeinformation about the crowd behaviour. Indeed, they simulate the behaviour ofeach people at every step of the simulation. The outcome consists therefore inraw data of the position of each individual. One good and usual way to visual-ize this information is by a visualizer. This visualizer is generally showing theagents position at each time step. It can provide a 3D view of the building (likein MASSEgress[Pan06]) or just a 2D view of the �oor, from above. Although itdoes not permit a deep statistical analysis of the crowd behaviour, it allows toperceive intuitively the general outcomes of the simulation in real time (exampleFigure 3).

The simulation tools may also have an events recorder, in order to do furtheranalysis of the simulated crowd. In this way, events captured may be comparedwith known and archived scenario, from real life, as done in [Pan06].

The third element commonly found in simulation tool is the population gen-erator. This unit aim to initialize the simulation unit with agents of di�erentnatures. As specifying every agent behaviour would be long and respondent, agood way of proceeding is to use an automatic tool. Based on the distribution ofparameters (for instance age, mobility, physical size as in MASSEgress [Pan06]), this tool will generate an heterogeneous crowd. Agents will have di�erentcharacteristics.

Finally, the last element is the geometry engine. This unit is responsible forrepresenting the physical environment where the simulation takes place. Typi-cally, it contains the architecture of the building, with the di�erent informations

13

Figure 3: The visualizer of MASSEgress[Pan06]

(obstacle, exits, walls etc.). We will begin to describe the di�erent types of en-vironment.

3.3 Physical environment

The �rst aspect of a crowd simulation is the environment. In real life, people arephysically placed in a persistent and consistent physical environment, in whichthey evolve. In an agent-based simulation, agent should be capable of doing thesame. In practice, there are two main approaches for the implementation of theenvironment in an agent based simulation.

The �rst one is to discretized it in cells of equal size. The world can betherefore describe as a matrix, where each cell has a value for what it represent(it could be �oor, obstacle, agent...). This discretized representation of �ooris of the same kind that in cellular automata, however, similitude end there.Indeed, in this case agents evolve on those cells, moving according to their ownrules, whereas in cellular automata people are cells. Agent based simulationusing discretized �oors can be seen in [Sch], [CS08], [ZM05], [dSFMdGSO12].In Figure 4, we can see the discretized environment of SimPan [Sch]. Agentscan move on neutral cells, and are blocked by other agents and wall cells. Thissolution is commonly used because less costly, and easier to develop. However,it lacks of physical realism, as the shape of the agent is a square of de�ned size,whereas human body has more complex dimensions.

The other way of modelling an environment is to have a continuous repre-sentation of it, with agent being able to move and orienting themselves in a twoor three dimensional world. This is the case for [Pan06], [HKP+06], [HFV00].The physical representation is in this case more precise, and closer to the real-

14

Figure 4: An Agent Based Simulation using discretized �oor [Sch]

ity. The human body can therefore be modelled more precisely. In MASSEgress[Pan06], it is modelled by a three circle shape : two for shoulders and one forthe torso.

A great work have been done to model the mobility of agents (ie physicalinteractions and collisions avoidance with obstacles, walls, and other agents).One of the approach is the social force one, developed by Helbing [HFMV02][HFMV02], and that have been used in [HKP+06]. This solution is limitedbecause the speed, the motion and the placement relative to the crowd of theagent are not cognitive, but reactive, as it depends only from the environment.This means that the position of the agent is computed from the variables ofthe environment (i.e the size, the number and the distance to other agents).In this model, we will not see agent suddenly stop and turn back. Indeed, themovement is not a cognitive process, that means it is not based on the internalstate of the agent (which can include a short term memory, experience, emotionetc...). The solution implemented in [HKP+06] is to let the agent modify thevariable of its movement equation, depending on its goal, or state of mind.

Another approach, used in MASSEgress[Pan06] is the ray-tracing method,a method picked from work on agent vision. Agent have a vision cone in whichthey can see obstacles, and other agents, and consequently move in order toavoid them. This vision is more realistic because it corresponds the behaviourof real human being. Obviously, it has to coupled with a collision detector (be-cause the vision cone is not 360 degree wide). Unlike the previous one, in thismethod, the movement of the agent is cognitive. Agents have a perception oftheir environment, associated with a short term memory, and their next positiondepends on their internal state.

15

Even if the method di�ers, the great majority of models studied takes intoaccount the heterogeneous physical features of people. The age, the mobility andthe gender have an in�uence on body size and speed in [MBMV06], [HKP+06],[Pan06], for instance.

3.4 Reactive agent simulation

Once the environment is well de�ned and implemented, simulation can be devel-oped. A �rst kind of agent based simulation of crowd behaviour in evacuationprocedure are those who are based on reactive agent. This is the case, for in-stance of the Zaboutis and Marmaras simulation. They intend to simulate thebehaviour of a crowd in a metro train on �re. [ZM05]. In this simulation,Zaboutis & Marmaras postponed that

The behaviour of the individual in a crowd can be considered asre�exive, event-driven and thus context-dependent [...]. Forward-thinking is considered having no signi�cance in the formation ofhers/his activity while the behaviour of each individual can be con-sidered as if it lack meta-cognition.

It concludes :

Therefore, the interactions between the individuals and with theirenvironment can be approximated by simple rules.

. The environment is discrete, and each cells have the capacity of representinga special space of infrastructure (sidewalk, train, power line etc.), the amountof smoke it contains, and whether it is �ammable or not. In this simulation,the physical capability of a passenger depends on the environment (the natureof the cells he is on, the amount of smoke he inhaled) and on his basic physicalskills. The crowd is therefore heterogeneous.

The agent psycho-motor behaviour (i.e the cells he heads at for the next sim-ulation step) depends on the environment (physical constraint) and by the "�eldof in�uence". The �eld of in�uence models the attraction between individuals ina crowd. Each individuals, or group of individuals have a �eld of in�uence. This�eld, implemented as value on cells (measurement of the intensity of the �eld),move with the individual as they evolve in the environment. Each individualalso have a �eld of comfort, representing its ability to perceive others. Indeed,when the �eld of comfort of an individual intercepts with the �eld of in�uenceof another entity (individual or group), an interaction takes place, and the in-dividual with the lowest �eld of in�uence intensity moves towards the other one.

This simulation permits to exhibit leadership / follower-ship behaviour,based on the "charisma" of individuals (individuals with a low charisma willtend to follow those with a high one). It also models group formation, as peo-ple will gather around leaders. However, the concept of �eld of in�uence lack a

16

ground justi�cation. In this model, people may get attracted by people they cannot see (if behind a corner for instance). This model is quite close to the masspanic theory, as it postpone that individuals in crowd evacuation lack cognitivecapability, and are just subject to instinct (i.e only react to the environment).However, it is di�erent in two points. Firstly, this model does not take into ac-count the stated pre-condition for mass panic (i.e the feeling of entrapment andthe perception of a threat), individuals exhibit reactive behaviour independentlyfrom the threat. Secondly, individuals, far from showing "antisocial" behaviour,as expected by the panic theory, search instead for the presence of others andgathers in groups.

3.5 Cognitive agent

Human being is not an entity which only react to his environment. Peoplemakes decisions, that means that they choose between several options at onemoment. In agent based system, the decision process implies a cognitive process.Cognitive agent are agent capable of reasoning. That implies they have a sym-bolic representation of their world (data), from which they can process decisions.

The generic process of decision making for cognitive agent is the following.First agent sensors provide information about the environment. This informa-tion is partial. This information is then processed to be transform in knowledge,i.e it is transformed in symbolic representation. Then the knowledge of the agentis updated with this information. The knowledge is the set of all the informa-tions that have been processed or inferred by the agent. The usual way ofsimulating decision making, is by rules. Agent have several rules that permitthem to process a decision based on their knowledge. Once the decision is taken,the agent elaborate a plan, i.e a list of actions, that he will follow in order tomeet his goal (his decision).

For instance, in the case of an agent simulating human behaviour in emer-gency exit, the cognitive process could be the following. Via his perception, theagent spot an exit door. The information is added to the knowledge. The agenthave a decision rule which state "if there is an exit door AND I am with myfamily THEN exit the building". The agent then elaborate a plan to meet hisobjective. That could be "Walk toward the exit".

One way of implementing decision process is to use a decision tree (see Figure5).

We will study the case of the MASSEgress simulation tool, and more partic-ularly, the earlier version developed by X.Pan [Pan06], during his Phd Thesis.Indeed, this simulation tool exhibits the features of cognitive agents, withoutincorporating advanced mechanism like groups or emotions. It will provide usa good understanding of the concept behind cognitive agent, when applied tosimulating crowd reaction to emergency. Besides, several concepts implementedin this model are found in other simulation tools.

17

Figure 5: A decision tree in MASSEgress[Pan06]

3.6 Case study : MASSEgress

The virtual environment in MASSEgress is continuous. It is composed of �vetypes of component : obstacles (such as walls or furnitures), spaces (the nav-igable space where agent can evolve), exit (such as doors, that allow agent totransit from one space to another), exit signs (that gives the direction of thenearest exit) and assembly point (the destination of the evacuation).

Each agent is composed of three unit. A perception unit, that aims tocompute sensory data from the perception of the agent. A behaviour unit,which contain the decision-making process; it plays the role of the brain. Thelast part is the motor unit, which takes care of the motion of the agent in thecrowd.

The behaviour system implements psychological and sociological factors thatare involved in decision making process. The �rst one is familiarity, that is rep-resented as a list of exits in the agent's memory space. The agent will tend toevacuate thought these exits over other. Secondly, each agent have a decisionmaking type, modelling his psychological pro�le. This allows agent to reactdi�erently to a similar event, in order to simulate the heterogeneity of agents in

18

Figure 6: Emerging crowd patterns at an exit when agents exhibit queuingbehaviour(b) or competitive behaviour(a)

term of personality and experience.

The last three factors are the urge to exit, the stress threshold type, andthe herding factor. The urge to exit represents the stress level of the agent.The more high it is, the more the agent may exhibit competitive behaviour.Competitive behaviour is the fact that the agents behave in self-centred way,with the only objective to exit from the building the more quickly possible.When generalize to all the people in a crowd, that lead to clogging and archingat exits (see �gure 6.a).

The stress threshold type re�ects how di�erent stress levels can in�uencean agent's decision making, which in turn would lead to di�erent behaviours.For instance, when the stress level of an agent is below a certain threshold, theagent exhibits social behaviour, like queuing at intersection. Otherwise, it mayexhibit competitive behaviour. (see �gure 6).

Finally the herding factors represent how the agents is subject to the socialproof, i.e the propensity it has to follow others when not knowing what to do.When several agents are doing so, the general pattern that emerges is a herdingone, with people favouring an exit instead of an other (see �gure 7).

Concretely, each agent are associated with a set of decision tree (see �gure5), depending on their "decision making type". Each decision tree of the agentwill be associated with an interval of stress threshold value. When the urge toexit, i.e. the stress value, is included in this interval of threshold value, thedecision tree is used to make decision. In this way, the decision making processof an agent is in�uenced by his personal psychological pro�le (via the "decisionmaking type" factors), and by the current stress level of the agent. Typically,when on high stress, the agent will tend to :

19

Figure 7: Herding crowd behaviour in MASSEgress[Pan06]

• Stop following its leader (if it has one), because high stress leads to moreindividualistic actions

• If the agent sees multiple exits, then it goes to the one with the mostpeople (the process that potentially leads to herding behaviour)

• The agent will exit in a competitive manner

Except urge to exit, which represent the current stress level of the agent, allthose psychological factors are chosen at the creation of the agent and are notchanged during the simulation. Thanks to them, agents in MASSEgress havedi�erent psychological pro�les. This lead to a heterogeneous crowd, not onlyfrom the physical point of view, but also from the psychological one. Indeed, ina real situation, people will react di�erently when confronted as the same situa-tion. Therefore, a simulation engine should be able to model this phenomenon.This is another advantages of agent-based system, as agent are able to havedi�erent physical characteristics, but also di�erent cognitive pro�les.

The motor system executes the behaviour routine which corresponds to abehaviour decision selected by the behaviour system. The behaviour routine iscomposed of one or more steering behaviour, which in turn consists of a sequenceof basic locomotion. Steering behaviour are high level routine that are used tocomplete higher level goals. Several steering behaviours exist in MASSEgress,such as "random walk" (the agent walks randomly in the virtual environment);

20

"seek"(the agent walks toward a goal point) or "collision avoidance" (the agenthas the ability to maneuver in the virtual environment, avoiding collision withobstacle and agents that are foreseen using the agent's sensory output). Thesteering behaviour computes and executes at each step the "legitimate basicmove" for the agent. Those basic moves are move forward, turn left, turn right,reverse direction, shift left, shift right, move backward and stop.

In MASSEgress, the possible behaviour decisions are the following: "ex-plore", "go to goal point", "compete at exit", "queue at exit", "herding", "fol-low leader", "follow an agent". Each decision is associated with one or moresteering behaviour. For instance, the decision "go to goal point" use the associ-ation of two steering behaviours : seek and collision avoidance. In this way, theagent steers toward a point without colliding any obstacle.

MASSEgress simulation tool implements several interesting points in agentbased crowd evacuation simulation. We will know discuss the strengths andweaknesses of the MASSEgress simulation tool.

Firstly, agents show a realistic motion behaviour. Like VTT simulation tools[HKP+06], agents move in a believable way together. However, when VTT usedthe social force algorithm, MASSEgress implements an agent motion that is di-rectly dependant of the perception of the agent. That corresponds to reality,in which humans have a partial vision of their environment, with a restricted�eld of view. In this way, MASSEgress agent may move backward and "bump"into another agent that they didn't see. Then, their motion ability depends ontheir physical features. This is commonly implemented in the agent based crowdsimulations [MBMV06], where age, gender and ability generally have in�uenceon walking speed.

Secondly, contrary to VTT simulation, agents have a partial knowledge oftheir environment. They have to search for the exit, and do not know all the�oor architecture beforehand. This is more realistic, as people in buildings rarelyknows the exact escaping path. However, the memory is not implemented. Thismeans that people will not keep track of the place they were previously. This isserious brake on the believability of the agent as human being. Moreover, thiscould lead to arti�cially extends the measured evacuation time, as people maywander in places they have already see, instead of exploring other parts of thebuilding.

One interrogation that remains concerns the urge to exit value. If it is saidthat it may "change over time", it is never explicitly said how and when. How-ever, this value has a great importance, as we saw, in the agent decision makingprocess. Thus, the simulation may be considered incomplete, as the mecha-nisms incrementing and lowering the stress level don't seem to be implemented.We may conclude that the simulation is able to exhibit queuing behaviour andcompetitive behaviour, but do not models the interactions that may lead to one

21

be preferred over the other in a given situation. However, this has great impor-tance. Indeed, according to the mass panic theory, if competitive behaviour mayoccur, it is under well de�ned condition (among them, the feeling of entrapmentand the perception of a great threat).

Agents in MASSEgress simulation prefer to escape thought familiar exit.The underlying social psychological theory behind this behaviour is the a�li-ation theory. However, the simulation doesn't model other behaviour that areexpected with this theory. For instance, groups aren't represented in MASSEg-ress. Agent will not wait/look for relatives.

More generally, this simulation lack a realistic social modelling of agent. In-deed, in MASSEgress, agents are not communicative agents. There is no socialinteraction between the individuals. However, whether in the a�liation andnormative approach or in the social identity approach, individuals are tied withsocial bonds between them. They communicate with others, share informations.Besides, roles are not taken into account. In the normative approach, individu-als behave accordingly to their role, and social situation. There is no such thingin MASSEgress.

The question of the conformity of MASSEgress with psychosocial theoryis therefore asked, as several of the outcome expected in those theory are notexhibited. MASSEgress doesn't seem to rely on any of the previous studiedpsychosocial theory. However, this harsh conclusion must be relativized, asMASSEgress is an ongoing work. Several ameliorations are programmed, amongthem the implementation of sound social behaviours [CPL11].

MASSEgress, despite its imperfection, permitted us to have a better idea onhow cognitive agent where implemented, and what they allow to do. We willknow study more brie�y some simulation tools, focusing on their particularityand their strengths, when compared to MASSEgress.

3.7 Point of interest in other simulation tools

MASSEgress lacks a clear mechanisms for changing the "stress value" of theagent. SimPan, a simulation tool developed by Schneider [Sch], particularlyfocused on this point. In SimPan, the environment is discretized in cells. Themain idea of SimPan is to implement a realistic mechanism for the fear emotionin agent. The fear is represented by a value that evolves during the simulation.The fear value is changed accordingly to several factors, such as the previousvalue of the fear, the sensitivity of the agent (whether or not it is easily fright-ened), the perception of the threat, or the density of the crowd. The fearvalue will directly set the agent reaction, as showed in �gure 8. When low, theagent have a re�ective behaviour, characterized by ability to consciously calm-ing down, leading, and guiding other agents. When the value is upper a certainthreshold, the agent will have a deliberative behaviour, i.e. planning for a pathto an exit, using personal memory and given information (via loudspeakers). A

22

Figure 8: Behaviour of the agent depending on the fear value in SimPan [Sch]

re�ective behaviour will be characterized by participation in a mass �ight, wan-dering around, or approaching an exit within eyespot and pushing. The theoryunderneath is clearly the mass panic theory. SimPan models e�ectively all therange of possible behaviour described by this theory, with a realistic modellingof the fear value.

However, two criticism can be made, regarding psycho-social theories. The�rst is that there is no evidence of a direct relation between the fear value andthe �ight reaction [Qua01]. Yet, in this model, the behaviour of an agent (in-cluding the �ight behaviour) depends directly from his fear value. The secondlimitation is the one of the social-psychological model used, i.e. "mass panic"theory. As seen, this theory has been invalidated [CD08], [Maw05] by the scien-ti�c community. Consequently, models based on this theory may be consideredas invalid too.

The simulation developed by Massaguer et al. [MBMV06] implements rolesin a simple and intelligent way. Roles are considered as psychological pro�les.That means that when confronted to the emergency, the agent will have di�erentbehaviour depending on their role. In that simulation, the decision makingprocess is implemented as a recurrent arti�cial neural network, where the coreof the neural net describes the importance if each input (information variables,previously taken decision) to the decision making module, and results in theprobability of taking each decision. A role is therefore de�ned by specifyingthe weights of the decision-making neural network. Each agent can thereforehave a di�erent role, and react di�erently. For instance, when hearing the alarmbell, a security o�cer will probably decide to put a uniform and look into eachroom, telling people to evacuate; whereas a evacuee will start to look aroundhim to see what others do. This incorporation of the role aspect permits to

23

Figure 9: Inpact of the Floor Wardens in Massaguer et al. simulation [MBMV06]

obtain outputs about the impact of trained people on the general outcome ofthe evacuation. For instance, Massaguer et al. ran a simulation, with the de�nedrole of �oor wardens. Floor wardens had several possible decisions, includingwearing his/her vest and safety helmet, going into rooms and ask people toevacuate, calling to report people that are not evacuating and exiting the �oor.From those simulation, they implied the impact of the number of Floor Wardenson the evacuation time, as showed in Figure 9.

Massaguer et al. simulation tools also model social ties between occupants.The simulation models two social networks. One models how much an agentwill wait for another one (during the evacuation). The seconds models howmuch an agent trusts another agent. Indeed, in this simulation, agents shareinformations about the situation they perceive. They can share informationswith any of their relatives, even if they are not in the same place. Indeed, thisis coherent regarding the actual communication devices such as mobile phones.When receiving the information, the agent will take into account the trust ithad for the transmitter. The underneath theory behind this is the a�liationapproach, as agent will wait for their relatives before exiting the building. Thegroup behaviour will also be implemented in the next version of MASSEgress[CPL11].

Finally, some simulations focused on the modelling of human emotion andpersonality. This is the case, for instance of [POSB05]. However, we lack thepsychological knowledge to evaluate them.

24

4 Recapitulation and perspective

4.1 Recapitulation

Simulating crowd behaviour in emergency evacuation has great implicationsin term of conception of facility, or training o�cer to behave adequately whenfaced to such situation. However, in order for the simulation to be coherent withthe real situation, they have to be based on sound social-psychological theorydescribing and explaining the behaviour of the crowd during emergency. Westudied three main theories developed in this �eld : the mass panic theory, thea�liation and normative approach and the social identity theory. We concludedthat mass panic theory had been discredited to explain crowd behaviour. Wesaw that a�liation and normative approach su�ers from gap, that the socialidentity theory �lled.

After a brief evocation of the simulation methods used in simulation tools,we saw that agent based crowd simulation has a great potential in modellingcrowd behaviour in response to an emergency. The main idea behind an agentbased simulation is to model adequately each individual in the crowd, and hisinteractions with the environment in order to outcome a realistic simulation ofthe crowd. The crowd behaviour will emerge from the interaction of the dif-ferent agents between them. This model has several strengths. Firstly, theyare able to model emergent patterns that arise from interactions between in-dividuals. This is the case for instance of the queuing behaviour (as saw inMASSEgress). Secondly, they are able to model an heterogeneous crowd. Thatmean having di�erent individuals, not only from the physical point of view (forinstance with the age in�uencing the walk speed [FG97]) , but also from a psy-chological point of view (for instance with the role in�uencing decision makingprocess [MBMV06]).

However, the studied agent-based simulation tools didn't seem to take intoaccount the conclusion of last social-psychological theory.

4.2 Perspectives

The �eld of agent based simulation tools seems to lack a complete and validsimulation tools which would implements a realistic crowd simulation. Instead,several simulation tools exist. Generally, each one of them has a strength andseveral weaknesses, as they focus on one aspect of the individual behaviour. It isunderstandable, from a research point of view. However, this leads to di�cultiesabout the evaluation of the models. Indeed, as it is partial, it lacks mechanismsthat are important. A good simulation tools would uni�ed all the strengths ofthese studied simulation tools. In other words, it would implement the physi-cal environment and the motion of agents described in MASSEgress. It wouldimplement roles, as modelled in Massaguer et al.[MBMV06]. It would take intoconsideration the group approach found in Chu et al. [CPL11]. It would modelthe threat, and the individual physical response to it as in the VTT model

25

[HKP+06]. It would models adequately the time needed for people to begin toevacuate. And �nally, it would properly take into account the importance ofpersonality and emotion in the decision making process, as proposed in [ZAV10].

The other issue of agent based simulation is to take into consideration thesocial-identity theory. Indeed, if the mass panic theory and the normative anda�liation approach have been implemented, often partially, in several model,none have proposed a way of implementing the social identity approach. Thatis a lack that has to be repair, as the social identity theory tend to be majorityin the social-psychological �eld. Finally, the social identity approach aims toreconcile the study of crowd behaviour during an emergency and the study ofcrowd behaviour in other situations. Based on this assumption, unifying crowdsimulation in disaster situation and crowd simulation under normal could be agood approach. Indeed, the crowd will be contextualized, and the outcome of theevacuation will depend on the con�guration of the crowd before the evacuation.And as the individuals during evacuation stay socially-linked and cognitive, thisfusion does make a sense.

Finally, it has been proved that during a disaster, communication with thecrowd is crucial, and has often more consequences than the physical con�g-uration of the place. Indeed, in the previous disaster, the losses of life wereoften due to a bad or non-existent communication with the crowd [OCC+10].Consequently, having a simulation tool that could permit to test di�erent com-munication methods and see the e�ect on the crowd is crucial. This is perfectlypossible to model with agent based simulation, and would, therefore, be a goodfuture direction to the �eld.

26

References

[ATGLH11] BE Aguirre, M.R. Torres, K.B. Gill, andH. Lawrence Hotchkiss. Normative collective behavior inthe station building �re*. Social science quarterly, 92(1):100�118, 2011.

[Aut02] U.K.A.E. Authority. A technical summary of theaea egress code. Technical report, technical reportAET/NOIL/27812001/002 (2), 2002.

[AWV98] B.E. Aguirre, D. Wenger, and G. Vigo. A test of the emergentnorm theory of collective behavior. In Sociological Forum, vol-ume 13, pages 301�320. Springer, 1998.

[CD08] C. Cocking and J. Drury. The mass psychology of disastersand emergency evacuations: A research report and implica-tions for the �re and rescue service. Fire Safety, Technologyand Management, 10:13�19, 2008.

[CPL11] M.L. Chu, X. Pan, and K. Law. Incorporating social behaviorsin egress simulation. In Computing in Civil Engineering (2011),pages 544�551. ASCE, 2011.

[CS08] K. Christensen and Y. Sasaki. Agent-based emergency evacu-ation simulation with individuals with disabilities in the pop-ulation. Journal of Arti�cial Societies and Social Simulation,11(3):9, 2008.

[DC07] J. Drury and C. Cocking. The mass psychology of disasters andemergency evacuations: A research report and implications forpractice. University of Sussex Falmer, Brighton, 2007.

[DCR09] J. Drury, C. Cocking, and S. Reicher. Everyone for themselves?a comparative study of crowd solidarity among emergency sur-vivors. British Journal of Social Psychology, 48(3):487�506,2009.

[DS11] J. Drury and C. Stott. Contextualising the crowd in contempo-rary social science. Contemporary Social Science, 6(3):275�288,2011.

[dSFMdGSO12] R. dos Santos França, B. Maria das Graças, M.B. Steinberger,and N. Omar. Simulating collective behavior in natural disastersituations: A multi-agent approach. 2012.

[FG97] S. Franklin and A. Graesser. Is it an agent, or just a pro-gram?: A taxonomy for autonomous agents. Intelligent AgentsIII Agent Theories, Architectures, and Languages, pages 21�35,1997.

27

[Gil08] G.N. Gilbert. Agent-Based Models. Number nÂ◦ 153 in Quan-titative Applications in the Social Sciences. Sage Publications,2008.

[HFMV02] D. Helbing, I.J. Farkas, P. Molnar, and T. Vicsek. Simula-tion of pedestrian crowds in normal and evacuation situations.Pedestrian and evacuation dynamics, 21, 2002.

[HFV00] D. Helbing, I. Farkas, and T. Vicsek. Simulating dynamicalfeatures of escape panic. Nature, Vol. 407, pp. 487-490, 2000,2000.

[HKP+06] S. Hostikka, T. Korhonen, T. Paloposki, T. Rinne,K. Matikainen, and S. Heliövaara. Development and valida-tion of fds+ evac for evacuation simulations. Project summaryreport, Helsinki University of Technology, 2006.

[LB95] G. Le Bon. Psychologie des foules (1895). Paris, Presses Uni-versitaires de France, réédition de, 1995.

[Maw05] A.R. Mawson. Understanding mass panic and other collectiveresponses to threat and disaster. Psychiatry: Interpersonal andBiological Processes, 68(2):95�113, 2005.

[MBMV06] D. Massaguer, V. Balasubramanian, S. Mehrotra, andN. Venkatasubramanian. Multi-agent simulation of disasterresponse. ATDM AAMAS, 2006.

[OCC+10] Great Britain: Cabinet O�ce, R. Challenger, C.W. Clegg,M. Robinson, Grande-Bretagne. Cabinet O�ce, and Grande-Bretagne. Her Majesty's Stationery O�ce. UnderstandingCrowd Behaviours: Supporting Theory and Evidence. Under-standing Crowd Behaviours. Stationery O�ce, 2010.

[Pan06] X. Pan. Computational modeling of human and social behaviorsfor emergency egress analysis. PhD thesis, Stanford University,2006.

[POSB05] N. Pelechano, K. O'Brien, B. Silverman, and N. Badler. Crowdsimulation incorporating agent psychological models, roles andcommunication. Technical report, DTIC Document, 2005.

[Qua54] EL Quarantelli. The nature and conditions of panic1. CrisisManagement, 60:309, 1954.

[Qua01] E.L. Quarantelli. The sociology of panic. 2001.

[SA04] G. Santos and B.E. Aguirre. A critical review of emergencyevacuation simulation models. 2004.

28

[Sch] B. Schneider. E�orts in agent-based simulation of human panicbehaviour: Reference model, potential, prospects.

[Sim80] J.D. Sime. The concept of panic. Fires and human behaviour,1:5, 1980.

[Sti00] G. Still. Crowd dynamics. 2000.

[SVLS06] A. Shendarkar, K. Vasudevan, S. Lee, and Y.J. Son. Crowdsimulation for emergency response using bdi agent based onvirtual reality. In Simulation Conference, 2006. WSC 06. Pro-ceedings of the Winter, pages 545�553. IEEE, 2006.

[Woo09] M.J. Wooldridge. An introduction to multiagent systems. Wi-ley, 2009.

[ZAV10] A. Zoumpoulaki, N. Avradinis, and S. Vosinakis. A multi-agentsimulation framework for emergency evacuations incorporat-ing personality and emotions. Arti�cial Intelligence: Theories,Models and Applications, pages 423�428, 2010.

[ZM05] N. Zarboutis and N. Marmaras. Investigating crowd behaviourduring emergency evacuations using agent-based modelling.Proceedings of EAM, pages 17�19, 2005.

29