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Modelling human behaviours in disasters from interviews: application to Melbourne bushfires


Abstract and Figures

In this paper we are interested in raising the deciders' awareness of the real (irrational and subjective) behaviours of the population in crisis situations. We analyse residents' statements gathered after Vic-toria Black Saturday bushfires in 2009 to deduce a model of human behaviour based on the distinction between objective (capabilities, danger) and subjective (confidence, risk aversion) attributes, and on individual motivations. We evaluate it against observed behaviour archetypes and statistics, and show its explicative value.
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Modelling human behaviours in disasters from
interviews: application to Melbourne bushfires
Carole Adam1?and Benoit Gaudou2
1Grenoble Informatics Laboratory (LIG), University Grenoble-Alpes, France
2Toulouse Computer Science Institute (IRIT), University of Toulouse, France
Abstract. In this paper we are interested in raising the deciders’ aware-
ness of the real (irrational and subjective) behaviours of the population
in crisis situations. We analyse residents’ statements gathered after Vic-
toria Black Saturday bushfires in 2009 to deduce a model of human be-
haviour based on the distinction between objective (capabilities, danger)
and subjective (confidence, risk aversion) attributes, and on individual
motivations. We evaluate it against observed behaviour archetypes and
statistics, and show its explicative value.
Keywords: Human behaviour modelling, agent-based social simulation,
crisis management
1 Introduction
Natural disasters have been getting more and more frequent recently with global
warming, and cause many victims every year. In this context, it is particularly
important, in order to decrease the number of victims, to try to optimise the
population reaction (evacuation, confinement, etc) by sending them appropriate
messages (information, alerts, evacuation orders, etc). The strategies of emer-
gency managers differ depending on the country and type of disaster; here we
are more particularly interested in the Australian bushfires.
The current policy is entitled Prepare, stay and defend, or leave early, so
the population is given a choice between: evacuating early, before fire reaches
their area of residence, because ”many people have died trying to leave at the
last minute” [4]; or stay and defend their house, only if very well physically
and mentally prepared; in both cases, the decision must be made and a fireplan
prepared well in advance. But in the summer 2009, serious bushfires devastated
a part of Victoria, culminating on the Black Saturday 7th February when 173
people died, despite all efforts at raising awareness. Several reports [15, 13] have
tried to explain the reasons for this heavy death toll and have identified incon-
sistencies in behaviour (the population does not react as expected by deciders),
?Thanking note.
2 Carole Adam and Benoit Gaudou
in information (received information is not always considered as relevant by the
population), and in communication means (not efficient, specifically broadcast).
In this paper we focus on the discrepancy between the population behaviour
as expected by the deciders (that determines what information are sent) and
their actual behaviour (that determines what information they would need or
find relevant). We propose to explain this mismatch by a gap between objective
and subjective values of two aspects: risk, and ability to control it. Deciders tend
to predict behaviour based on objective values, while each resident behaves based
on their own, necessarily biased, subjective values. Our approach is agent-based
modelling and simulation. We have analysed witness statements to design a con-
ceptual model of population behaviour consistent with psychology (Section 2);
implemented it on the GAMA simulation platform (Section 3); and evaluated it
against behaviour data (Section 4). We conclude in Section 5 with a discussion of
these results, comparison with related works, and future directions of research.
2 Conceptual model of population behaviour
Methodology There are two main difficulties in building an agent-based model
of human behaviour: finding the right balance between descriptivity of the model
(realistic enough to accurately describe real behaviour) and complexity (yet sim-
ple enough for its results to be easily understandable) [6, 2]; and finding and
exploiting data to inform the model. In this paper we use several types of data
about the 2009 bushfires: witness statements [18], police hearings about circum-
stances of deaths [17], and statistics about the fatalities [16]. In the absence of
any available methodologies and tools to exploit such qualitative data, we read
and analysed them manually. The next section discusses a number of relevant
extracts and how we exploited them to design and inform our model.
2.1 Witness statements and statistics
Under-estimation of danger Reports state that ”among those who died,
some misinterpreted the information they had received, not realising how little
time they had to respond or how soon the fire would reach them”. As a result
they did not have enough time to implement their fire plans. Even people who
did not plan to defend still found themselves forced to when surprised by the
fire. For instance, this father:
When we headed up to [the] property, there was just a little fire at the
bottom of the hills and I didn’t think there were any major dramas. [...]
We could see the smoke coming up over that ridge. I wasn’t too worried
at that stage as the smoke was still two valleys away. [...] We watched
the fire come up. [...] When I saw them knock the fire off the top of the
hill I was quietly confident that we were going to be okay then. [...] As
soon as I saw the smoke I decided it would be too dangerous to drive
the 5km bush track out. I then prepared to face the fire.
Modelling human behaviour in bushfires from interviews 3
Over-estimation of capabilities Many residents had also over-estimated their
ability to face the fires, ignoring the psychological preparation needed:
”Despite saving our lives and our house, I don’t think I would ever stay
and defend again. We were physically very well prepared [...] but I don’t
think anything can prepare you for the psychological impact of facing
the inferno that hit us on Black Saturday.
or not updating their perceived ability to take their current condition (age,
illness) into account. For instance a woman reports how her husband insisted on
defending his house despite now being unable to do so.
[My husband] was in Marysville during the 1939 bushfires and fought the
fire from the roof of our old house and helped to successfully save that
house. [He] also spent many years working for the Forestry Commission
and one of his jobs there was to go to different places to fight fires. During
his younger years, [he] was a very fit and strong man. [...] During the last
few years of his life, [his] health deteriorated, [...] he had a degenerative
spinal disease which affected his mobility. [He] was a proud man and [...]
he found his loss of mobility very difficult to accept.
Others were just plainly unaware and unprepared.
Although I had never really given it much consideration, I suppose that
my fire plan always consisted of staying and defending my property. [...]
Nothing prepared me for this bushfire. Although I never thought about
leaving, I now know that my decision to stay and defend was not a well-
thought-out decision and that I was very underprepared. In particular, I
was psychologically unprepared to fight a bushfire. I simply did not have
any idea what the reality of facing a fire would be.
Reports confirm that ”most of those who died did not, and often could not,
respond appropriately to the risk that the bushfires presented for them on 7
February”; in particular 30% of fatalities occurred in not defendable properties.
Passivity and waiting for triggers Many people stay passive in front of fire
until too late, therefore feeling as if everything went too fast. It was reported
[16] that 69% of the fatalities were ”passively sheltering” when they died.
This was not a conscious decision, but I was standing just outside the
house watching for flames and when they came, they came so suddenly
that I just didn’t have time to do anything or than stand and watch. It
feels almost sacrilegious to say this, but I found the fire fascinating and
strangely beautiful. The contrast of the black and red was stunning.
Individual differences, emotions and relationships. Different individuals
can have different perceptions of risk and different motivations. In average, men
prefer to defend their house, while women want to leave but often end up staying
with their partners because they are reluctant to leave them behind [16]. Witness
statements illustrate the resulting negotiation and the role of emotions.
4 Carole Adam and Benoit Gaudou
I continued to talk to [my husband] and tried several times to persuade
him to leave, but he would not budge. [I] tried several times to persuade
him to leave, but he would not budge. I could smell the fire, I could
hear the fire and I could see it with my own eyes. But I couldn’t get
my husband to accept that it was coming. He just sat and sat and sat.
[...] I decided that I could not stand it any longer. I was feeling very
anxious and angry with [my husband]. I kept saying ”hurry up, come on
we’ve got to go”. The fire seemed to be coming closer. I could hear the
fire crackling. After too much of that I thought ”I’m not staying here
to burn”. So I walked out the gate and I left the house on foot. [...] I
had no idea where I was going the only thing I can remember is that I
wanted to get out. I was not thinking clearly because I was so annoyed
with [him] and I was also feeling terribly guilty about leaving him.
In that first statement, the wife ended up escaping (alone) to safety, but all four
members of another family were reported to have died in similar circumstances.
She was determined to leave, but [her husband] wouldnt leave his par-
ents. [His father] wouldnt go, [his mother] wouldnt leave [his father], [her
husband] wouldnt leave his parents.
External motivations to defend People can have additional motivation to
defend their house, for instance for financial reasons. Some residents report hav-
ing stored expensive equipment or personal belongings.
I also had many belongings stored on [the] property in four sheds. I am a
hoarder by nature [...]. My belongings included the fit out of a cafe that
I used to own, $50,000 - $60,000 worth of tools [...]. I also stored family
photos and personal papers in the sheds.
Others report drawing additional income from cattle or plantations.
[We] are both retired teachers. The olive grove had been planted in 1999
and the trees were at full production. Both the cattle and the olive grove
provided additional income for our retirement.
In both cases they tried to protect their belongings from the fire.
Summary of behaviour factors In conclusion, the manual analysis of these
statements highlighted the following factors that we want to include in our
model: discrepancy between actual danger and perception of risks; discrepancy
between actual abilities and confidence in one’s abilities; inter-individual dif-
ferences in initial motivations for defense (e.g. financial) or escape (e.g. risk
aversion); inter-individual differences in awareness of and knowledge about fires,
and in duration of unawareness, passivity, preparations; frequency of residents
being surprised by the fire while still indecisive (58% of fatalities had made no
preparation at all, neither for leaving nor for staying) or passive.
Modelling human behaviour in bushfires from interviews 5
2.2 Behaviour model: finite state machine
Our goal in this research is to highlight the role of these subjective, irrational
determinants of the population’s decisions and behaviours. We therefore only
need a quite simple model, descriptive enough to capture individual motiva-
tions, subjective risk and abilities, but not so complex that the results will not
be understandable. Our choice is a finite-state-machine architecture with the
following states and transitions (illustrated in Figure 1):
Unaware: initial state where the agent is (rightly or wrongly) not aware of any
danger, and does nothing; agents can become aware by spotting fires in their
perception radius, with a probability based on their objective abilities; they update
their value of subjective danger based on their perceptions and motivations;
Indecisive: the agent is aware of some fires but has not yet made a decision about
how to react; agents stay indecisive for a varying amount of time, until they have
enough motivation to either fight or escape; initial motivations are individual and
then vary based on the evaluation of the situation (subjective danger);
Preparing to escape: the agent has decided to leave and starts preparing, until
ready or surprised by the fire before being ready (transition to Evacuating), or
blocked by the fire and forced to stay (transition to Preparing to defend);
Evacuating: the agent is evacuating towards the closest shelter; travel efficiency
depends on objective abilities; injuries can be received from fires on the way. Unless
it dies during travel, its next state will be Safe when reaching the shelter;
Preparing to defend: the agent has decided to defend, or was forced to stay
because the fire blocks escape; it prepares house and self until the fire is close
enough, which triggers the transition to Defending;
Defending: the agent is actively fighting the fire around its house; when that fire
is extinguished, the agent transitions back to Preparing to defend until another
fire comes; if motivations change (e.g. subjective danger increases when actually
seeing the fire, or subjective abilities decrease after failing to fight) and evacuation
becomes more pregnant, the agent transitions to Evacuating;
Safe: the agent is in a shelter, it stays there until the end of the fire and cannot
be injured anymore;
Dead: the agent’s health dropped to 0 as a result of injuries received from the fire;
Survivor: final state of all agents that did not die during the fires.
Fig. 1. Residents behaviour: states and transitions
This simple model is sufficient to capture the discrepancies highlighted by the
data. Indeed the objective value of danger influences injuries and damage, and
6 Carole Adam and Benoit Gaudou
the objective value of capability influences the success of actions. But these
objective values are inaccessible to the agents, whose decisions are based on
their subjective values of danger and abilities, and on their motivations.
2.3 Evaluation of the conceptual model
Comparison with the psychology of stress. Our model is in agreement
with Lazarus’ theory of stress [11], making a distinction between two simulta-
neous processes: primary (or demands) appraisal consisting in evaluating
the significance of the situation for the individual (good, stressing, or irrelevant),
matches our subjective danger; and secondary (or resources) appraisal con-
sisting in evaluating the individual’s capability (skills, social and material sup-
port, resources) to cope with the stressor, matches our subjective ability.
Comparison with cognitive bias theories. Behaviours reported in the state-
ments are consistent with known cognitive bias [19, 10], in particular the con-
firmation bias, a tendency to give more credit to information consistent with
one’s beliefs or motivation and to discard inconsistent cues (e.g. interpreting the
presence of firemen as a sign that everything is safe, or denying the reality of the
fire when wanting to stay). Our model allows to capture such a bias, because
motivation plays a role in the computation of subjective danger and ability.
3 Implemented model of behaviour in bushfires
3.1 Model of environment
We implemented the model in the GAMA simulation platform [7]. The envi-
ronment is a grid of 50 by 50 cells containing different types of agents: houses,
shelters, and fires, detailed below, and residents, detailed in the next section.
Fire. Very complex and detailed models of fire spreading already exist [5], but
realistic fire behaviour is not the focus here. Still with the goal of not adding
unneeded complexity, we have designed a very simplistic model of fire that is
sufficient to trigger and visualise the reactions of the population that we are
interested in here. The fire is composed of fire agents with a reflex architecture.
Each fire agent has a location and intensity (also representing its radius of action)
attributes, and the following reflexes triggered at each step of the simulation:
Change intensity: increase or decrease with two different probabilities that
can be set as parameters.
Propagate to a neighbour cell that is not burning yet, which creates a new
fire agent on that cell. Probability of propagating, and starting intensity of
new fires, are both parameters.
Deal damage to buildings in its radius of action (based on its intensity):
the amount of damage is picked randomly between 0 and a maximum value,
function of intensity and a ”damage factor” parameter.
Modelling human behaviour in bushfires from interviews 7
Deal injuries to residents in its radius of action, also random amount be-
tween 0 and the maximum value based on its intensity and an ”injury factor”
parameter. If the person is in their house, the injury is moderated by its re-
sistance weighed by a ”protection factor” parameter.
Disappear when its intensity is null.
The different parameters involved allow the user to make the fire more or less
dangerous (growing and propagating quicker, dealing more damage and injuries,
etc), in order to observe the desired behaviours. Actions are also available for
the user to start any number of new fires (at random locations), or stop all fires
to stop the simulation.
Houses. The environment initially contains 100 houses each inhabited by ex-
actly 1 resident (in future work we plan to consider families and their relation-
ships). Each house is an agent with the following attributes:
– Owner: the resident of that house
– Resistance: random initial value between 100 and 200 to simulate different
solidity, will be increased by preparing, or decreased by fire damage, and
offers some protection from fire injuries to its resident.
– Damage: the damage received from fire
The houses collapse from fire damage when their resistance drops to 0. They
then cease to offer protection, and the resident’s motivation to defend them also
disappears. They stay in the environment as ruins for final visualisation.
Shelters are safe places whose location is known by all residents3. They offer a
total protection from fires (no injuries can be received while in a shelter). Once
a resident has reached a shelter, they stay inside until the end of the simulation.
3.2 Model of residents
Architecture. We used GAMA finite-state-machine (fsm) architecture for the
residents, with the states specified in Section 2: initial state Unaware; states
during the fires: Indecisive,Preparing to defend,Defending,Preparing
to escape,Escaping,Dead,Safe; final state Survivor, only reached by agents
still alive when all fires are stopped.
Attributes. Residents agents are described by the following attributes:
Location on the grid.
House id (each agent is initially in a house).
3In future works we want each resident to have a different (partial, and possibly
wrong) knowledge of the position of existing shelters, to simulate varying degrees of
preparation, but this adds a lot of computational complexity to the model.
8 Carole Adam and Benoit Gaudou
Current state (initially unaware, then following the fsm, see Figure 1).
– Health: random initial value, increased by preparing for fire, decreased when
receiving injuries.
– Injuries: total injuries received from fires, influencing decision to escape.
– Ob jective defense ability: random static value impacting the chance to
perceive fires in perception radius, and the effect of (prepare, defend) actions.
Subjective defense ability: initialised by applying a bias (based on over-
confidence parameter and motivation to fight) on the objective ability, then
updated (rate of update is a parameter) by observing performance (success
or failure of defense actions); influences motivation to defend.
– Objective escape ability: random static value (e.g. driving license, fit-
ness), impacting accuracy and speed of evacuation.
Objective danger: based on intensity and distance of all fires around the agent.
Subjective danger: based on intensity and distance of fires known by the
agent, moderated by individual motivations.
Motivation to escape (risk-aversion): random initial value, then updated
based on subjective danger, health, and resistance of house (offering protec-
tion); determines the decision to escape.
Motivation to defend: random initial value (e.g. financial reasons, previ-
ous experience), then updated based on injuries and damage received, and
on subjective evaluation of danger and of fighting abilities.
Actions. Residents agents can perform three actions depending on their state:
– Prepare: action performed while in Prepare to defend or Prepare to
escape state; consists in raising resistance of the house (watering, weeding,
etc) and health (wearing appropriate clothing, etc), by an increment based
on objective ability; success or failure influences subjective ability.
Fight fire: action performed while in Defense state to decrease the intensity
of nearby fires by a value based on objective ability; agents monitor success
to update subjective ability, thus reconsidering their motivation over time;
– Escape: action performed while in the Evacuating state, to head towards
the closest shelter, with speed and accuracy based on objective escape ability
(could take detours); they might get injured if travelling too close to the fire.
4 Results and evaluation of the model
4.1 Implementation of the simulation
The environment is a 50x50 grid (cf figure 2). Initially we place 2 shelters in the
NE and SW corners (blue-green circles) and 200 houses inhabited by 200 resi-
dents. The attributes are randomly initialised (health and resistance is between
100 and 200; capabilities and motivations are between 0 and 1). The simulation
starts with 20 medium fires of intensity 7 (orange triangles) that grow and prop-
agate in the grid. Burning cells are in red, and surrounded by yellow cells on a
Modelling human behaviour in bushfires from interviews 9
radius equal to the fire intensity (representing its radiant heat). Green cells are
those that are still safe. The user can also start random new fires at any time,
or stop all fires to stop the simulation. The residents have 2 colours to visualise
their current state (outside colour) and their previous state (centre colour): dark
blue (unaware), pink (passive indecisive), orange (preparing to defend), red (de-
fending), yellow (preparing to escape), light blue (escaping when ready), blue
grey (escaping before ready), dark green (safe), black (dead).
Fig. 2. Screenshot of the simulation in GAMA, with parameters and their values
We have defined three categories of parameters concerning: the fires (more
or less serious: probability to grow or to propagate, initial intensity, damage
factor, etc); the buildings (resistance, protection factor, etc); and the residents
(confidence bias, perception radius, action radius, etc). GAMA interface lets the
user specify the values of all parameters when running the simulation. Figure ??
shows the parameters and their values as set in our experiment.
4.2 Evaluation methodology
Our model is aimed at the reproduction of realistic human behaviours (and
not at realistic initial situation or fire spreading), and should therefore not be
evaluated as a whole. This is why we focus here on evaluating the generated
human behaviour. We have implemented a tool to track and log the agents’
states trajectories: what states they went through, at what cycle, and what
were the values of their attributes when making the transition. This allows us
to study and explain what happens in the simulation. Thanks to this tool, we
can evaluate our model on two axis: correctness, by comparing the generated
10 Carole Adam and Benoit Gaudou
trajectories with those observed in the real population (profiles of behaviours),
and in particular comparing the causes of deaths; and explicative value, by
showing the importance of the subjective-objective discrepancy, thus proving the
potential of our model to raise deciders’ awareness of this gap.
4.3 Evaluation of the correctness of model
Consistency with behaviour profiles. A report [1] has established 6 profiles
of behaviours in the residents of fire-affected areas: can-do defenders (most
determined, experienced, self-confident and skilled, determined to defend); con-
sidered defenders (strongly committed to defend, aware of risks, well prepared
and trained); livelihood defenders (committed to defend their livelihood, well
prepared); threat monitors (not intending to stay in front of a serious threat,
nor to leave until necessary, wait and see); threat avoiders (aware of risks an
dvulnerability, plan to leave early before any real threat); unaware reactors
(unaware of risk, feel unconcerned, no knowledge, preparation or training).
We were able to observe the same profiles of behaviours in our simulation,
and to categorise the agents in these profiles based on their logged trajectories.
For instance a typical trajectory for a can-do defender is an early perception of
the fire (transition to Indecisive followed by an immediate decision (transi-
tion to Preparing to defend), an efficient preparation (strong improvement of
health and resistance in that state) and defense (big decrease of fire intensity).
Also they have a good perception of risks and are able to reconsider their inten-
tion (transition to Escaping when health decreases too much), unlike livelihood
defenders who tend to stay on their property no matter what happens. We have
then computed average values of the attributes in each category of agents and
compared them with the global value on all agents. For instance we found that
can-do defenders have a significantly higher self-confidence, which is in agree-
ment with the definition of the profile.
Consistency with death causes statistics. The VBRC report also provides
statistics about the circumstances of the 173 deaths drawn from police hearings
and experts reports. In particular they found that 14% died while fleeing (4%
in cars, 10% on foot); 69% while ”passively sheltering” (as opposed to ”actively
defending”), possibly after having tried to defend; some died while defending,
even when well prepared. In total 30% were taken by surprise by the fire.
Figure 3 shows the percentage of deaths from each state in our simulation.
We can see that 47% of the agents died while still passive (indecisive); 19% die
while escaping; the others died while preparing to defend or defending, taken
by surprise before they could evacuate. This shows that our model generates
behaviour quite similar to the real population.
4.4 Explicative value of our model
The goal of our model is to raise deciders’ awareness of the factors determin-
ing real population behaviour, as opposed to expected behaviour. Indeed, the
Modelling human behaviour in bushfires from interviews 11
Fig. 3. Causes of deaths in our simulation
statements clearly show that deciders expect a ”rational” behaviour based on ob-
jective values, while people behaved based on their subjective (possibly wrong)
values. Our hypothesis was that the discrepancy between these objective and
subjective values of danger and capabilities could explain deaths, and should
therefore be taken into account by deciders. If we are right, we should observe
stronger discrepancy in agents who die than in agents who survive.
Figure 4 shows the evolution over time of the average discrepancy between
objective and subjective values of danger and capabilities, for alive agents and
for dead agents. Note that once dead, agents do not update their values, so the
evolution only comes from new agents dying over time.
– Danger discrepancy for dead agents (red) starts at 0 (no death yet)
then jumps to a very high value as the first agents die while unaware of
(yet real) danger. It then tends to decrease as the agents dying later in the
simulation are those who have a lower underestimation of danger.
Danger discrepancy for alive agents (orange) is always much lower than
in dead agents; it also continuously decreases for two reasons: agents with
a higher discrepancy die, and those that survive update their perception of
danger to tend towards the objective value.
Ability discrepancy for dead agents (blue) also starts (and stays) higher
than for alive agents. It decreases quickly at the start (as agents with higher
discrepancy died early), then stays mostly stable.
Ability discrepancy in alive agents (green) keeps going down, until the
last survivors tend to actually underestimate their abilities (this is due to
them updating their subjective abilities based on their performance at fight-
ing the fire, which gets worse as the fire keeps intensifying).
12 Carole Adam and Benoit Gaudou
Fig. 4. Graph of average discrepancy between objective/subjective perception of dan-
ger and abilities in alive/dead agents
5 Discussion and conclusion
Comparison with state of the art. Some existing simulations focus on re-
alistic fire behaviour and spreading (e.g. Phoenix [5]), easier to understand in
physical terms. On the contrary as far as we know no model provide the same
degree of realism for human behaviour. Indeed, many agent-based simulations
focus on crowd evacuation in building fires, with often homogeneous reactive
agents (e.g. social force model [8]). More complex models have then appeared,
integrating emotions [12, 14] or social relationships [3] of agents, but they are
usually based on psychological theories, while we based our model on witness
statements providing a precise description of people actual behaviour.
Existing simulations often focus on the evacuation of public buildings (e.g. air-
port [9], stadium) to inform their design and prevent problems (e.g. crowding at
doors or in stairs) which are not relevant in bushfires in scarcely populated areas.
Besides, most of these simulations tend to focus on evacuation itself, neglecting
the pre-evacuation time which is at least as important [10]. On the contrary we
did model this decision-making phase, which is even more important in bushfires
where people are not evacuating from a public building (where their only moti-
vation is to save their life) but have to abandon their own house to the flames
(with an additional contradictory motivation to also save it).
Future work. Our model relies on simplifying hypotheses and will need to
be extended in the future. In particular, the initial situation has exactly one
Modelling human behaviour in bushfires from interviews 13
person per house, while statements and reports show the importance of family
relationships (several people in the same house making a decision together), and
of tourists or visitors who (in addition to their lack of knowledge) have nowhere
to go when the fire surprises them outside. Also, we need to model how different
sources of information (observation of fire, information on the radio, visits from
authorities, phone calls from relatives, etc) with different levels of trustworthiness
are combined to evaluate risk and make a decision.
Our agent architecture is also very simple, tailored to prove our point about
the discrepancy between objective and subjective values of danger and ability.
In future work we plan to design a BDI (belief, desire, intention) model of be-
haviour, improving its descriptivity but also its explaining power, since mental
attitudes are people’s preferred level of abstraction to explain behaviour [2].
Thanks to this high explicative value, we aim to eventually transform this sim-
ulation into a serious game which will be a valuable tool for deciders to test
response strategies or raise the population’s awareness.
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... 16. Motivationàévacuer /à défendre : les motivationsàévacuer la zone de danger (I4a) età se défendre en luttant contre le danger (I4b) sont deux facteurs qui influencent la réponse plus ou moins active des populations [Adam and Gaudou, 2016]. Ces réactions dépendent de plusieurs autres facteurs, l'évaluation du risque d'une part et la zone géographique mais ...
Cette thèse porte sur l'intégration de connaissances sur les comportements des populations dans la conception et l'évolution des systèmes d'alerte précoce, outils permettant de capter les signaux annonciateurs d'une crise pour les intégrer au cœur des processus de gestion de crise. L'objectif est, d'une part, de fournir un cadre de réflexion aux décideurs sur les questions de la sensibilisation des populations et de la prise en compte de leurs comportements en cas d'alerte, et, d'autre part, d'apporter une aide à la décision en cellule de crise sur les questions relatives à l'alerte et l'information aux populations, en proposant des éléments objectifs et contextualisés. Dans ces travaux, les comportements des populations sont considérés sous l'angle de leurs déterminants environnementaux et individuels, caractérisés par une liste d'indicateurs pouvant être utilisés dans les systèmes d'alerte précoce. Basée sur ces indicateurs, nous proposons une démarche pour la conception d'un outil d'aide à la décision structurée en quatre étapes : identification des connaissances, structuration et recueil des données, génération d'un modèle de décision, utilisation et mise à jour du modèle. La démarche s'inscrit dans une approche systémique fortement intégrée à son contexte de recherche, au carrefour entre positivisme et constructivisme. Elle répond à l'esprit du dispositif ORSEC (Organisation de la réponse de sécurité civile) réformé en 2004 par la loi de modernisation de la sécurité civile, qui fournit un cadre de réflexion et des recommandations générales sur la gestion de crise, transformant ainsi les approches précédentes, davantage orientées processus et scénarios.
... Social simulations and computational models not only allow for discovery of the consequences of theories in artificial societies, but by enforcing formalization in terms of coherent programs, they play a similar role in social sciences as mathematics does in physical sciences [127]. Examples of this technique applied to disasters include [128][129][130][131]. Simulation is an alternative to common static modeling approaches in social sciences. ...
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... The outcome of the research addresses a drawback that was found in the previous model, which was unable to capture the emergence of reluctant people [15]. It also improves on other similar models of evacuation, which give less consideration to this phenomenon (e.g., [13,14,67,127,136]). Additionally, the model has been evaluated through a spatial and temporal validation approach to evaluate its plausibility. ...
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As the size of human populations increases, so does the severity of the impacts of natural disasters. This is partly because more people are now occupying areas which are susceptible to hazardous natural events, hence, evacuation is needed when such events occur. Evacuation can be the most important action to minimise the impact of any disaster, but in many cases there are always people who are reluctant to leave. This paper describes an agent-based model (ABM) of evacuation decisions, focusing on the emergence of reluctant people in times of crisis and using Merapi, Indonesia as a case study. The individual evacuation decision model is influenced by several factors formulated from a literature review and survey. We categorised the factors influencing evacuation decisions into two opposing forces, namely, the driving factors to leave (evacuate) versus those to stay, to formulate the model. The evacuation decision (to stay/leave) of an agent is based on an evaluation of the strength of these driving factors using threshold-based rules. This ABM was utilised with a synthetic population from census microdata, in which everyone is characterised by the decision rule. Three scenarios with varying parameters are examined to calibrate the model. Validations were conducted using a retrodictive approach by performing spatial and temporal comparisons between the outputs of simulation and the real data. We present the results of the simulations and discuss the outcomes to conclude with the most plausible scenario.
... Consequently, they have been used to extract different stereotypical behaviour profiles [15]: can-do defenders, livelihood defenders, considered defenders, threat monitors, threat avoiders, and unaware reactors. They have also been used to develop a model of the population's behaviour in the bushfires [16], [17]. This model uses a finite-state-machine architecture that provides a rather simplistic and rigid description of behaviour. ...
Conference Paper
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Efficient communication is essential in disasters in order to coordinate a response and assure effective evacuation. This paper focuses on the case study of the Melbourne bushfires in 2009. We first analysed some interviews of the population to know who the population communicates with (neighbours, family, authorities, etc), and using what channel (radio, phone, internet, etc). We then developed and implemented communicative actions in a Belief-Desire-Intention model of the population's behaviour. Finally, we ran experiments in order to compare the speed at which the population becomes aware of the fires in different scenarios with different types of communication (more or less organised). Our first results show that more organised modes of communication would provide significant benefits in terms of propagation of awareness in the population.
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Emergency managers receive communication training about the importance of being ’first, right and credible’, and taking into account the psychology of their audience and their particular reasoning under stress and risk. But we believe that citizens should be similarly trained about how to deal with risk communication. In particular, such messages necessarily carry a part of uncertainty since most natural risks are difficult to accurately forecast ahead of time. Yet, citizens should keep trusting the emergency communicators even after they made forecasting errors in the past. We have designed and implemented a serious game called Vigiflood, based on a real case study of flash floods hitting the South West of France in October 2018. In this game, the user changes perspective by taking the role of an emergency communicator, having to set the level of vigilance to alert the population, based on uncertain clues. Our hypothesis is that this change of perspective can improve the player’s awareness of flood risk, and response to future flood vigilance announcements. We evaluated this game through an online survey where people were asked to answer a questionnaire about flood risk awareness and behavioural intentions before and after playing the game, in order to assess its impact. The results are encouraging, showing improved risk awareness, protective intentions, vigilance, and trust after playing. However, it also suggests that the current ’’game design” is still poor and unable to engage the general public, in particular school students. Future research will therefore address this issue.
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Computer simulation is a powerful tool for planning real evacuation scenarios during a crisis. In such context, emotion is a major factor that influences human decision making process and behavior. In this paper, we present our multi-agent simulation through the mathematical formalization of its main components: emotion and its dynamics, an heuristics for evasive actions of agents, the scenarios for tests and the results of theses tests. We show that on one hand, emotions increase the chaos of simulation which leads to an increase of collisions between agents, and on the other hand the evacuation time decreases because agents are more hurry to leave the place of the crisis.
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Risk perception (RP) is studied in many research disciplines (e.g., safety engineering, psychology, and sociology), and the context in which RP is studied varies greatly. Definitions of RP can be broadly divided into expectancy-value and risk-as-feeling approaches. RP is seen as the personalization of the risk related to a current event, such as an ongoing fire emergency, and is influenced by emotions and prone to cognitive biases. The present article is a literature review that differentiates RP from other related concepts (e.g., situation awareness) and introduces theoretical frameworks (e.g., Protective Action Decision Model and Heuristic-Systematic approaches) relevant to RP in fire evacuation as distinct from other related fields of research Furthermore, this paper reviews studies on RP during evacuation, especially on the World Trade Center evacuation on September 11, 2001. It discusses factors modulating RP, as well as the relation between RP and protective actions. This paper concludes with a summary of the factors that influence risk perception and the direction of these relationships (i.e., positive or negative influence, or inconsequential), the limitations of this review, and an outlook on future research.
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Agent-based models tend to be more and more complex. In order to cope with this increase of complexity, powerful modeling and simulation tools are required. These last years have seen the development of several platforms dedicated to the development of agent-based mod- els. While some of them are still limited to the development of simple models, others allow to develop rich and complex models. Among them, the GAMA modeling and simulation platform is aimed at supporting the design of spatialized, multiple-paradigms and multiple-scales mod- els. Several papers have already introduced GAMA, notably in earlier PRIMA conferences, and we would like, in this paper, to introduce the new features provided by GAMA 1.6, the latest revision to date of the platform. In particular, we present its capabilities concerning the tight combination of 3D visualization, GIS data management, and multi-level modeling. In addition, we present some examples of real projects that rely on GAMA to develop complex models.
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Nowadays, more and more emergency evacuation simulations are used to evaluate the safety level of a building during an emergency evacuation after an accident. The heart of this kind of simulations is the simulation of human behavior because simulation results depend for a big part on how this behavior is simulated. However, human behaviors in a real emergency situation are determined by a lot of cognitive mechanisms. In order to make the simulation more realistic, plenty of factors (e.g. innate characteristics, perception of the environment, internal rules, personality and even emotions) that affect human behaviors must be taken into account. This paper focuses on the influence of emotions, and more precisely on the influence of their dynamics and propagation from an agent to another. The main contribution of this work is the development of a model of emotions taking into account their dynamics and their propagation and its integration in an evacuation simulation. The first results of the simulation show the benefits of considering emotion propagation.
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In creating an evacuation simulation for training and planning, real-istic agents that reproduce known phenomenon are required. Evac-uation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of first-time visitors who have incomplete knowledge of the area and fam-ilies that do not necessarily adhere to often-assumed pedestrian behaviors. Evacuation simulations not customized for the airport domain do not incorporate the factors important to it, leading to inaccuracies when applied to it. In this paper, we describe ESCAPES, a multiagent evacuation simulation tool that incorporates four key features: (i) different agent types; (ii) emotional interactions; (iii) informational interac-tions; (iv) behavioral interactions. Our simulator reproduces phe-nomena observed in existing studies on evacuation scenarios and the features we incorporate substantially impact escape time. We use ESCAPES to model the International Terminal at Los Angeles International Airport (LAX) and receive high praise from security officials.
Studies of past emergency events have revealed that occupant behavior, local geometry, and environmental constraints affect crowd movement and govern evacuation. Occupants' social characteristics and the unique layout of buildings should be considered to ensure that egress systems can handle evacuee behavior. This paper describes an agent-based egress simulation tool, SAFEgress, which is designed to incorporate human and social behaviors during evacuations. Simulation results on two scenarios are presented. The first scenario illustrates the effects of the exiting strategies adopted by occupants on evacuation. The second scenario shows the influence of social group behavior on evacuations. By assuming different behaviors using this prototype, engineers, designers, and facility managers can study the important human factors during an emergency situation and thereby improve the design of safe egress systems and procedures.