Study of Human Activity Related to Residential Energy Consumption
Using Multi-level Simulations
Thomas Huraux
, Nicolas Sabouret
and Yvon Haradji
EDF Research & Development, Clamart, France
LIMSI-CNRS, UPR 3251, University of Paris-Sud, Orsay, France
LIP6, CNRS UMR 7606, - Pierre and Marie Curie University, Paris, France
Multi-agent Systems, Multi-level Modeling, Simulation, Electrical Consumption.
In this paper, we illustrate how multi-agent multi-level modeling can help energy experts to better understand
and anticipate residential energy consumption. The problem we study is the anticipation of electricity con-
sumption peaks. We explain in this context the benefit of the coexistence of microscopic (human activity)
and macroscopic (social characteristics, overall consumption) levels of representation. We present briefly the
SIMLAB model (Huraux et al., 2014) that extends the SMACH simulator (Amouroux et al., 2013) with coex-
isting levels on different modeling axes. We then present a model of the households activity and its electrical
consumption consistent with energy experts’ observations in the residential sector. We show the impact of dif-
ferent social factors, such as individual sensitivity to price or to personal comfort, on the apparition of peaks on
the consumption. We illustrate the contribution of multi-level modeling in the understanding of macroscopic
As energy consumption increases, one major issue for
electricity suppliers is to adapt in real time to cus-
tomer demand to maintain a stable frequency. In
this context, understanding and analyzing inhabitant
behavior is a major issue for the reduction of en-
ergy consumption. Two solutions are possible to
study and anticipate human behavior. First, experi-
ment in real situations allows psychologists and ex-
perts in ergonomics to study human activity around
the principles of action and situated cognition (Re-
lieu et al., 2004). Second, simulation can, at the cost
of some computer simplifications, reproduce related
consumption phenomena (Kashif et al., 2012; Mura-
tori et al., 2013; Amouroux et al., 2013). We seek to
assemble these two approaches by directly integrat-
ing in situ knowledge into simulations to provide new
tools for experts to connect behaviors and electrical
consumption. To this purpose, we propose a multi-
level simulation model for the study of human activ-
ity both at the microscopic level (individuals) and at
a macroscopic level (household) and we show how it
can be used to study household energy consumption.
In our model, three dimensions are considered:
the temporality of human activity (from actions to
habits), the diversity of populations (from individu-
als to social groups) and the complexity of the en-
vironment (from electrical appliances to residential
area). We extend the SMACH simulator
, a mod-
eling and simulation platform (Haradji et al., 2012;
Amouroux et al., 2013) for the study of human be-
havior which allows energy experts to study the ev-
eryday life of households in relation to their electrical
consumption, and in particular, how organization of
household activities impact on energy consumption.
We add micro-macro dynamics in these three dimen-
sions and we will show in this paper how studying
back and forth between levels of representation can
enable energy experts to better understand the phe-
nomena related to consumption and human activity.
In section 2, we present works that are interested
in the simulation of human activity and multi-level
simulations. Section 3 briefly present SIMLAB, our
multi-level agent-based model based, among others,
on an inter-level influence mechanism and the concept
of modeling axis. Section 4 presents the representa-
tion of households with our model. After presenting
our various action strategies, we present different ex-
periments and the corresponding results in section 5.
Huraux T., Sabouret N. and Haradji Y..
Study of Human Activity Related to Residential Energy Consumption Using Multi-level Simulations.
DOI: 10.5220/0005197401330140
In Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART-2015), pages 133-140
ISBN: 978-989-758-073-4
2015 SCITEPRESS (Science and Technology Publications, Lda.)
2.1 Human Activity
In the residential sector, a Markov process calibrated
using time-use data can be used to simulate an av-
erage household (Muratori et al., 2013). The limita-
tion of this approach applied in the residential sector,
is to be based on the use of human behavior mod-
els as average households. But as pointed out by
(Morley and Hazas, 2011), there is no average house-
hold in our society in the context of energy consump-
tion. To overcome this limitation, (Grandjean, 2013)
uses inhabitant and activity profiles based on stochas-
tic models, which introduces some diversity in the be-
havior. This approach allows to reconstruct a load
curve in the residential context and to observe over-
all phenomena, but it does not allow a micro vision
for studying practices of everyday life.
The work of (Lee et al., 2011) relies on a micro
modeling of activities in the professional context and
use simulation to optimize the building performance.
They use simulation to make prediction on real sys-
tems, combining individual activities (e.g. opening
windows) with more macro information (e.g. holiday
periods, meeting habits, ...). In the SMACH model,
we want to go further in the modeling of activities
and interactions between individuals, which naturally
leads us to turn to multi-agent simulations.
2.2 Multi-agent Systems
A large part of human activity results from coopera-
tion, knowledge sharing, negotiation and adjustments
within interactions (Haradji et al., 2012). This is
the kind of dynamic between intrinsic properties, ac-
tions and interactions that can be found with multi-
agent systems (MAS). For instance, this paradigm is
used by (Yang and Wang, 2013) to controls the build-
ing environment through installed actuators with data
from sensors and occupants as inputs. The MAS pre-
dicts occupant preferences through learning their be-
haviors Nevertheless in simulation models, although
some agent based models have studied the electricity
market, such as (Zhou et al., 2007), little work has
been done on the simulation of human activity. Or the
models are focused on one aspect of the activity such
as comfort (Alfakara and Croxford, 2014).
Kashif (Kashif et al., 2012) works in the residen-
tial context to design a detailed model of human be-
havior for energy management. The authors also de-
part from the premise that human behavior is an im-
portant element of energy consumption of buildings.
Based on the analysis of activity logs, they propose an
inhabitant behavior model based on BDI agents (Rao
et al., 1995). The model requires precise frameworks,
which is difficult to implement with domain experts
(not computer scientists) and that does not always
seem necessary (Haradji et al., 2012). That is why
we propose in this paper an approach that combines
a microscopic description of human activity with the
use of domain expert macroscopic knowledge, as the
phenomenon of overall consumption peaks. We pro-
pose in our work to use a multi-level approach.
2.3 Multi-level Simulations
Representation of macro phenomena in MAS can be
done using multi-level models (Morvan, 2012). How-
ever, most models do not consider the coexistence of
different levels within the same model during simu-
lation. Some models combine different levels, as in
hierarchical models like SWARM (Minar et al., 1996)
where macro levels take control of micro levels. Sim-
ilarly, in the crowd simulations (Navarro et al., 2011)
macro entities are seen as aggregations to speed up
simulations. The system automatically selects, for
each agent, the appropriate level of representation to
allow a significant computation gain and one level is
enabled at a time. In all these approaches, the micro
and macro levels do not co-exist within a single sim-
Among more generic approaches, AA4MM (Ca-
mus et al., 2013) proposes to combine levels with a
multi-modeling approach: coordination is done us-
ing information sharing. The interaction between the
different levels is limited as the levels correspond to
heterogeneous models. PADAWAN (Picault et al.,
2011) allows the co-existence of agents and environ-
ments corresponding to different spacial and temporal
scales in the IODA model (Kubera et al., 2011). All
concepts are represented by agents that can change
level (i.e. environment) based on their activity. The
IRM4MLS approach (Morvan et al., 2011) offers
a meta-model based on the principle of influence-
reaction (IR) where the influence represents the “de-
sire” of the agent to modify its environment and the
reaction represents the consequence according to the
state of the world. The links between levels are repre-
sented by directed graphs and allow to have a partic-
ular temporality for each level.
In our work, we share the view that a more sys-
temic approach (i.e. coexisting levels) can enhance
our model by extending it with macro entities which
is not allowed by approaches based on aggregation
with average behaviors. We retain the Picault’s idea
that all concept must be represented by an agent and
Morvan’s idea of representing the influences between
levels. However, we are not interested by different
time scales or level changes (i.e. conceptualization by
energy experts), but we want to take into account the
different levels in the system modeling itself and to
combine models from different domains. We seek to
show that the direct introduction into the model of lev-
els that coexist during the simulation, not as different
visualizations of a same phenomenon but to connect
expert knowledge within a model to improve it. It will
help us to study large-scale phenomena. So we pro-
posed the SIMLAB model that we will briefly present
in the next section.
The SIMLAB model
(Huraux et al., 2014) is based
on two major proposals for modeling and studying
multi-level phenomena. First, agents that share com-
mon features for the study are grouped within mod-
eling axes that capture the representation of cross do-
mains properties at different levels. Second, we dis-
tinguish between interactions of agents (i.e. the ex-
change of information or queries) and intra-axis in-
fluences on properties, which represent the inter-level
dynamics of these cross domains components.
3.1 Modeling Axis
Our model is positioned in the context of multi-expert
modeling and simulation. In each domain of exper-
tise, shared properties can be identified. But also, in
each domain of expertise, these notions appear with
different levels of abstraction. For example, the en-
ergy consumption is a shared property of the axis
grouping appliances, rooms, housings. This is why
we define modeling axes. These particular properties
make the modeling process easier by encouraging re-
flection on what is common to all levels.
Let be the set of agents. An axis χ
associated with the concept c of the studied system
and a set of shared properties P
specific to the axis,
which represents the domain. And in each axis, we
then define the agents representing the different lev-
els. For example the heater, the room, the housing, all
calculate their energy consumption in the consuming
environment axis. We denote @ an inter-level rela-
tionship between two agents. An axis χ
form a con-
nected subset of for the relationship @. We assume
that the inter-level relationship is non-transitive and
acyclic, but an agent can have several super-agents.
SIMLAB Is Multi-Level Agent Based
3.2 Agent Description
Each agent ω has certain level of abstraction
in a modeling axis. It is defined by the tuple
, P , A, I , F i where ω
and ω
the access of the agent to his direct super and sub-
agents. P is the set of internal variables manipulated
by the agents, called properties. These concepts cap-
ture the characteristics of the modeled entity. A prop-
erty can be atomic (real, boolean, ...) or more com-
plex (list, set, function, or even a reference to another
agent). A is the set of internal actions to enable the
agent to modify its properties. A precondition is as-
sociated to each action, and at each step of execution,
an agent performs all actions whose preconditions are
satisfied. Finally, I and F are the sets of interactions
and influences of the agent (see 3.3).
3.3 Inter-agent Relationships
Interactions are at the heart of agent based modeling.
However, these are not sufficient when entities of sev-
eral levels are added into the model. Therefore, we
also introduce inter-level influences to describe the re-
lations between levels within a modeling axis.
3.3.1 Interactions
Our mechanism of interaction between agents is a
simplified version of the intentional communication
model proposed by FIPA (FIPA consortium, 2003).
Performatives used by agents are modeled specific
concepts and may involve several axes (for example,
agents-individuals use performative action to inform
each other of their desire and their availability for the
realization of common tasks, as we will see in the
next section). Each agent is provided with a set of
reactions R (ω), i.e. actions triggered only by interac-
tions. Like actions, reactions act on the internal prop-
erties and are associated with pre-conditions. Every
agent ω has a set of interactions I (ω), where each
i I (ω) is a tuple htarget, reactionsi with target
the recipient and reactions R (target) a set of reac-
tions by the recipient.
Example: Let n and h be two agents corre-
sponding to an individual and a heater, h.temp P (h)
the temperature property of h and increase I (n) the
possible interaction of n on h define as :
increase = hh, {h.temp := h.temp 1.1}i
The operator “:= describe the effect of the action,
given a value ω. p := v means that v is allocated to
the property p of the agent ω. Here, if the individual
performs the increase interaction on the heater, it will
increase the temperature by 10%.
3.3.2 Influences
Levels are linked by an influence function that change
the value of a super or sub-agent property, based on
some properties of the influencing agent. These in-
fluences are defined in the agents properties and not
on instances of these properties. For example, if a
property of an individual influences a group, all the
individuals in this group benefit from this influence.
An influence f F is characterized by a tuple
, ω
, P
, p, in f li. It allows an agent ω
to influ-
ence the value of a property of one of its super or
sub-agents called ω
, based on some of its properties.
P (ω
) is the set of properties of ω
used to
compute the influence on ω
, p P (ω
) the changed
property and in f l the influence function which change
the property. We denote f (P
) the value that ω
integrate in p (with the relation :=).
Example: Let n be a sub-agent of f . They cor-
respond to an individual and its family. t [0, 1] is an
individual’s property which represents its tendency to
increase the heater. prio {co, sp} describes if the
family gives priority to its comfort or its spending.
We define f F the following influence:
h f , n, prio, t,
t 0.5 prio = sp
t prio = co
Here, the individual’s tendency to increase the heater
is halved when its family give priority to spending.
The introduction of influences allow us to define
specific properties called recursive. They are proper-
ties defined for all the agents of an axis and the in-
fluence function is directed from sub-agents to super-
agents (n.b. recursive properties are also shared prop-
erties). A change of value on a recursive property
spreads as a recursive function whose computation is
from the agent to the sub-agents. This mechanism al-
lows the modeler to establish automatic consistency
in behavior between different levels (e.g. establishing
a link between the preferences of an individual and
those of a group he belongs).
In the next section, we present a practical example
of modeling that combines a micro representation of
individuals and human activity with a macro represen-
tation of household and housing. We show how the
dynamics between levels can help business experts to
better understand the complex system studied.
We now illustrate the potential of multi-level model-
ing to facilitate energy studies, we simulate everyday
life of households in relation with the electrical con-
sumption and their thermal comfort. In the follow-
ing, we present our multi-level modeling realized with
SIMLAB. It is based on three axes with several levels.
We will show in section 5 some simulation results and
why they are interesting for the experts.
4.1 Three Axes Representation
The problem is based on three concepts : population,
human activity and consuming environment. To ap-
ply the previously presented model, we define respec-
tively the following three modeling axes (as shown on
figure 1):
The populations axis assemble individuals or
groups of individuals. Each agent in this axis can per-
form activities and have preferences for these. To se-
lect the current activity, agents use a priority function.
The human activity axis, in this example, is re-
duce to a set of tasks characterized by preconditions
(e.g. do the laundry is precondition of ironing) and
links with possible objects in the consuming environ-
ment (e.g. ironing necessitate an iron). When an ac-
tivity is triggered, associated elements of the environ-
ment are activated. The task model in SMACH has
been presented in details in (Amouroux et al., 2013).
The consuming environment axis represents en-
tities related to energy consumption (from the appli-
ance to the housing). An agent in this axis is charac-
terized by an electrical power, a consumption function
(as a recursive properties, see section 3.3) and an ac-
tivation state (off, standby or on).
4.2 Micro Level
The micro level can be summarized as follows, each
individual will perform tasks which may activate ap-
pliances within rooms that will produce electricity
consumption. For each of these agents, we will
present the various characteristics.
4.2.1 Individual
Individuals represent household’s members and are
characterized by an age, a gender, a thermal comfort,
a cold-sensitivity level and a clothing level. Individ-
uals interact with the tasks to achieve them based on
their internal states. An individual associates prior-
ities for each tasks and performs the task with the
highest priority. Household’s members must commu-
nicate to exchange information or request the partici-
pation of others in tasks. In addition to the individuals
internal state, the priority may be influenced by exter-
nal factors such as energy price level or other individ-
uals invitations. Our model also includes inhabitants
Figure 1: A simplified view of our three axes representation.
adaptation capabilities to the energy price level. The
cold-sensitivity changes the thermal comfort level felt
by individuals which is computed using the Fanger’s
equation (van Hoof, 2008) depending on their prop-
erties, mainly the temperature and the clothing level.
To communicate, the individuals have a set of speech
acts (e.g. to ask another inhabitant what it does, to
encourage someone to perform an activity, ...).
4.2.2 Task
A task represents a generic activity in the house. It
can be done individually or collectively and can have
a rhythm corresponding to a certain regularity in its
realization which modifies the probability that an in-
dividual achieves it (e.g. dinner occurs every day be-
tween 7 and 9 pm and lasts approximately 1 hour).
A task can have pre-conditional tasks (e.g. the din-
ner must be prepared). An individual can perform a
given task if and only it has the information that all
pre-conditional tasks. Finally, a task can interact with
an appliance to change its activation state (e.g. doing
the laundry activates the washing machine).
4.2.3 Appliance
An appliance updates its electrical consumption
and can perceive the room temperature. Electrical
consumption profiles of appliances come from the
database REMODECE
measured in real situations.
The appliances can have a standby power (such as
television, computer, ...) and a thermostat (heater).
Heaters can interact with their room to modify the
REMODECE : European database on residential con-
sumption -
temperature. All appliances influence the room’s tem-
perature according to their radiation property, charac-
teristic of running electrical appliances.
4.2.4 Room
We define a room as a super-agent of appliances char-
acterized by a number of present individuals, a cur-
rent and a target temperature for heaters. A room
update its temperature using a thermodynamic model
and perceive the number of present individuals.
4.3 Macro Level
We extend our model by adding two macroscopic en-
tities: the household and the housing. These new
agents are represented with a gray background in the
figure 1.
4.3.1 Household
A household is defined by its type (couple, single-
parent, ...), its comfort sensitivity (eco-oriented,
medium, comfort-oriented) and its income. It has a
thermal comfort and acts on the energy manager in
the housing to regulate the target temperatures in the
rooms depending on its properties. As it is defined
as a recursive property (see 3.3.2), the houshold up-
dates its comfort depending on the influences of the
sub-agents (individuals). Household properties influ-
ence individuals properties and also modify their ac-
tivity. We associate an influence on tasks priorities
of individuals. Depending on the household sensitiv-
ity, this influence reduces the priority when an energy-
consuming appliance (e.g. an oven or a washing ma-
chine) is needed to perform the corresponding task.
Figure 2: An example of cold discomfort feels by an inhab-
4.3.2 Housing
A housing is composed of rooms. It is characterized
by a presence indicator which perceives if at least one
individual is present in the housing. The housing can,
depending on its energy manager, interact with the
rooms to change the target temperatures. The energy
manager also controls the presences of individuals to
not uselessly heat the housing. The electrical con-
sumption, defined as a recursive property, depends on
the influences of the sub-agents (rooms).
This section describes two experiments we have con-
ducted. The first one illustrates the micro level as an
important aspect for energy experts studies. The sec-
ond one studies the influence of household types on
individuals and its effect on the consumption.
5.1 Experimental Setup
For our experiments, we generate 50 households us-
ing a populations generator with socio-statistical data
based on a modified version of the Gargiulo’s algo-
rithm (Gargiulo et al., 2010). Also, generated house-
holds properties such as the income, the surface of the
housing or the number of children are statistically rel-
evant. As it is sufficient for our experimental needs,
we consider that all individuals have the same set of
tasks they can perform (working, eating, sleeping, ...).
The housings have a set a appliances (e.g. TV, heater,
computer, ...) spread in the rooms. The tasks prefer-
ences are randomly set following a linear distribution.
5.2 Observation of Micro Behavior
During the simulation experts will want to observe
situations that can impact the comfort or the energy
consumption and to connect it to the activity that was
performed at that time, so as to detect typical every-
day life situations. For example, we can focus on
the periods when inhabitants are cold and to compare
this with the clothing level. The figure 2 presents
on the right the thermal comfort (red line) and the
clothing level (blue line) of an inhabitant and, on the
left, an extract of its activity diagram corresponding
to this period. This graphical representation of activ-
ity, where each color corresponds to a task, allow the
experts to visualize inhabitant’s behavior during the
day. So, this diagram allows to precisely retrieve the
activity of an inhabitant on a given simulation step.
The example in figure 2 shows a decrease in the
clothing level (the agent removes his clothes) causing
an important degradation oh his thermal comfort level
around 10h45. By observing the extract of the corre-
sponding activity diagram, we observe that the com-
fort gap is caused by the realization of the task ”taking
a shower” (in sky blue, boxed in the diagram).
This simple example illustrate how a micro rep-
resentation of activity between several axes identify
phenomena very localized in time that cannot be tak-
ing into account by more macro models. Indeed, a
study of just an average comfort is not pertinent for
a household because, as showed by (Z
elem, 2013),
two individuals in a same household can have differ-
ent perceptions (or even opposite perceptions) of there
comfort, and also different reactions: one can increase
the heater temperature, the other put on a pullover or
change his activity. The interest of a fine grain de-
scription is to reproduce the diversity and the com-
plexity of human behaviors.
This simple example also illustrates how our
model can be used to simplify the modeling process.
Once the modeling axes (population, activity and en-
vironment) have been identified and the correspond-
ing entities are reified in the model, the expert only
needs to determine the key observable parameters and
to relate them. Using agents for every entities gives a
more direct access to experts knowledge specific to
each axis: experts are familiar with the individual-
centered modeling. For example, ergonomists can de-
fine the notions that intervene in the definition of the
activity, while energeticians will define the consump-
tion based on the thermal characteristics, etc. We
show in the next section how the intra-axes common
structure of agents, based on influences between prop-
erties, allows us to easily define macro-level agents
that interact with each other. This addition of such
agents is limited to the definition of the new proper-
ties, specific to these new agents, and the inter-level
influences on these properties.
5.3 Adding Macro Agents
We now study the consuming activity profile of a set
of households. We aim to investigate the addition
within our model of macro agents at the household-
housing level. As describe in 4.3.1, the household
agent is defined as a super-agent of individuals (the
household members), it will influence the priority of
the individuals’ tasks and modify the energy manager
of housing based on its comfort (calculated from in-
dividuals’ influence) and its sensitivity. Thus an eco-
oriented household will have a lower target temper-
ature in the housing that individuals should be com-
pensate, to the extent possible by their clothing levels
(to each type of sensitivity is also associated a level
of minimum and maximum clothing, for example in
some households more focused on comfort, members
wear rarely more than a T-shirt). Moreover, the mem-
bers of such a household (eco-oriented) are incitated
by an influence to avoid performing consuming tasks.
We perform one-day simulations of our 50 house-
holds (see 5.1) whose number of consuming tasks per
hour is shown in the figure 3. Contrary to the first
experiment (see 5.2), we now take the similar house-
holds but we add the two macro agents (the household
and the housing) with their associated interactions and
influences. The households we choose to study in this
particular experiment are eco-oriented as describe in
the previous paragraph. The consuming tasks profile
is shown graphically in figure 3 with the green curve.
The red curve represents the reference profile without
the macro agents and their influences.
We first notice that adding macro agents reduce
significantly the consuming activity due to the influ-
ence of the eco-oriented households. As illustrated by
the red curve, we note that meals are structuring tasks
in our modeling as they produce two gaps at 12am and
8pm. They correspond to a certain synchronization of
the meal tasks which are specified as non-consuming.
Also, meal preparations produce consumption peaks
as shown with the green curve. As the need of elec-
tricity remain important for these tasks, the number of
consuming tasks tend to decrease only slightly with
the influence of the household.
This choice of measure (number of consuming
tasks) allows us to focus on human activity without
being concerned about avoiding eventual bias in the
consumption model of electrical appliances. It must
be emphasized that some other models are able to
reproduce a residential load curve, however the in-
terest of multi-level modeling is the ability to relate
macro knowledge, e.g. here households typologies
and energetics housing management with more mi-
cro elements (as the example presented in the previ-
Figure 3: Number of consuming tasks per hour.
ous section). Experts can easily test different work-
ing hypotheses such as the impact of different pric-
ing policies on consumption practices, to test the ef-
fectiveness of energy strategies (i.e. heating manage-
ment) or indeed the relationship between human ac-
tivity and consumption peaks. The previous experi-
ment fall within an approach to study how different
types of household with the same housing can pro-
duce different consumption profiles.
As mentioned previously, macro knowledge is
available in many areas. Their explicit representation
helps the experts to study the connection with micro
concepts to make them co-evolve. More precisely, if
a household gives more importance to its comfort or
expenses, there is a theoretical link (i.e. an influence)
on how people will manage their heating energy con-
sumption. However, this relation does not prevent the
diversity of behavior in so far as household members
(individuals) preserve their autonomy.
In this paper, we briefly introduced SIMLAB, a multi-
agent model based on the use of agents of different
levels and influences to capture the inter-level dynam-
ics. The main interest of our approach is the definition
of axes for the analysis of complex systems, high-
lighting shared properties. We have shown how multi-
level modeling can enable energy experts to better
understand and anticipate the residential energy con-
sumption. We believe that our model is able to repro-
duce micro and macro phenomena, both interesting
for energy experts, allowing energeticians to consider
possible incentives to reduce consumption peaks.
We consider several perspectives in the context of
our work. We are currently working on extending our
model with more macro concepts such as lifestyle and
social groups to perform large-scale simulations with
several households and housings. We believe that
the addition of these new levels from different fields
such as sociology, allow us to validate the model as
generic, in terms of modeling axes and influences. An
large-scale study will be soon performed by EDF us-
ing the SMACH platform. Interviews of households
will be used to create more realistic simulation. Re-
sults will be compared to real data from a several year
in situ experiment with sensors in the clients’ hous-
ings. We are also exploring the possibility of our
agents to dynamically change the MAS organization
proposing to add observations and transformations to
detect and reify potentially useful macro-entities to
help the modeler. To go further in this direction, it
would be interesting to study in more detail the char-
acterization of emergent phenomena.
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