Impact of Structuring Elements on Agents' Behavior in Social Simulations

Full text

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Abstract—Agent-based social simulations have been widely
used to help social scientists on the understanding of several
social phenomena. Traditional approaches to agents most often
tackle well the behavioral and the temporal aspects of the
carried out simulations. However, a frequent limitation in
social simulations is the lack of simultaneous support for
spatial specifications of social structures. That is, the
incorporation of placement and neighboring of real world
conceptual structuring elements such as houses, hospitals,
roads, and workplaces. Moreover, the incorporation of
mechanisms that affords assessing means on the action
selection of all social agents is deemed also to be seminal. In this
paper we use the Plausible Agents matriX (PAX) framework to
investigate the influence of these social structuring elements on
the intelligent agents’ behaviors, considering some disease
dissemination scenarios. Results obtained show how influential
is spatiality (i.e. consideration of the abovementioned
structuring elements) on the overall epidemics understanding
and sought control. These findings are instrumental for the
development of more effective tools to support decision makers,
namely the ones who work with health care and other public
policies.
I. INTRODUCTION
HERE are frequent limitations for rigorous investigations
in Social Sciences due to difficulties of applying the
Cartesian scientific method to social phenomena. For
instance, controlled real environments for social phenomena
experimentation is obviously limited and hard to obtain.
Some mathematical models were put forward as attempts
of capturing the essence of human social behavior.
However, these analytical models are based on not always
true premises. The works of John von Neumann and Oscar
Morgenstern that resulted in the Game Theory [1], for
example, assume perfect rationality when players adopt
strategies. For real human players this assumption is
evidently false, because human intelligence is something
more complex than the optimization of a gain (or
performance) function.
The search for suitable scientific tools, aiming to carry
rigorous investigations in Social Sciences, directed many

The authors are with Department of Computing and Systems,
Polytechnic School of Pernambuco - University of Pernambuco, Recife
50720-001, Brazil.
Fernando Buarque de Lima Neto (SM'07), the corresponding author, can
be reached by phone: +55 (0)81 3184-7555 extension 7542; fax: +55 (0)81
3184-7555 extension 7536; and e-mail: fbln@dsc.upe.br.

social scientists to experiment the use of computational
modeling and simulation. This differs greatly – but not
essentially – of the traditional deductive inference models
for generating confirmations of hypothesis or answers to
research questions.
Simulations of social phenomena are known as social
simulations [2]. When the simulations are modeled and
instantiated from agent architectures, they are commonly
referred as agent-based social simulations (ABSS) [3]. The
major motivation to use agent-based models is the
possibility of modeling and controlling different granularity
levels, namely, the social global level and also the individual
level. This advantage enables the researcher to produce
highly heterogeneous and sophisticated kinds of virtual
societies.
Regarding the use of computer models instead of
analytical ones, perhaps the major benefit is that the former
are more flexible. A computer model can represent real
phenomena as good as a set of equation, with the advantage
of providing abstractions when necessary, can be easily re-
configured and re-run [4]. As a matter of fact, computer
simulations can even incorporate analytical models. About
ABSS, Epstein and Axtell have shown strong arguments
encouraging the use of agent-based models instead of
analytical ones [5].
ABSS also support the study and prediction of many
human like behaviors such altruism, egoism, perseverance,
as well as social-like phenomena such as reputation
formation, leadership action, group gathering, culture
transmission, spread of various features across population
(e.g. diseases) and many others. This research field evolved
greatly since Neumann’s self-replicating machines and
cellular automata [6]. Most recent works deal with cognitive
social simulations [7] [8].
Along the evolution of the ABSS field, many simulation
environments, toolkits, frameworks and models were
proposed, e.g. Sugarscape [4], SARS [9] and the Vidya
platform [10]. For this paper in particular, previous works
and some learnt lessons using the Vidya platform are of
great value.
In the past the authors managed to use Vidya to simulate
egoistic and altruistic behaviors of agents [11], as well as
disease dissemination on virtual societies [12]. The latter
work revealed interesting and realistic social dynamics, such
as social exclusion of the sick and group re-gathering after
Impact of Structuring Elements on Agents’ Behavior in Social
Simulations

Marcelo Pita, and Fernando B. de Lima Neto, Senior Member, IEEE
T

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the simulated epidemic is over. Besides the exciting results
obtained then, some limitations were also encountered.
These are to be addressed here, that is the absence of
structuring social elements.
In many ABSS tools (including Vidya), we have
identified the lack of flexibility to include structuring
elements such as houses, roads, hospitals and workplaces.
This means that across the simulation (search) space, of
those tools, events are equi-probable to happen. However,
this is ratter unrealistic, thus we strongly advocate that this
flexibility must be considered in order to the production of
plausible high level social simulations that include
organizations, individuals and symbols.
ABSS environments may be classified according to three
requirements for which they shall provide support, regarding
relationships among entities of the simulation: (i)
behavioral, (ii) temporal and (iii) spatial relations [13].
Traditional approaches to ABSS most often tackles well the
behavioral and the temporal aspects of carried out
simulations. However, a frequent limitation in artificial
environments is the lack of simultaneous support for spatial
specifications of social structures.
Considering (i) and (ii), in this paper, we are interested in
analyzing the impacts of spatial relations (including
placement and neighborhood of simulation elements) on
simulated agents’ behaviors and, consequently, on the
overall simulation results. This not so easy task is suitably
addressed by the new PAX (Plausible Agents matriX)
framework, which is still under development, but
sufficiently mature to afford full specifications of social
structuring elements and basic simulation infrastructure.
Hence, this paper can also be seen as a proof of concept of
using the PAX framework, which is a more comprehensive
tool for ABSS.
As case study, we investigated the influence of using a
connecting road (i.e. a social structuring element) that links
communities to far away hospitals. This feature minimized
transportation costs for its users when compared with
inhabitants that do not use it. Simulated scenarios included
epidemic spread (and control) in the presence and absence
of the road connecting communities to a hospital. Several
other conditions were also simulated and influenced directly
on the intelligent agents’ behaviors.
Results show that the social structuring element
investigated, here the simple concept of a road (but could be
any other cocept, e.g. vaccination or radio broadcast), had a
seminal importance in he epidemic dynamics and control.
This paper is organized as follows: section 2 gives an
overview on the main concepts related to agent-based social
simulations; section 3 explains the PAX environment;
section 4 discusses the experimental setup and the carried
out simulations; and section 5 concludes the paper and gives
some prospective views.
II. AGENT-BASED SOCIAL SIMULATIONS
Computer simulations in social sciences besides to be a
young field, have produced very interesting tools that aims
to approximate computational models to real social
phenomena, complying also with some scientific demands.
Among them, we highlight the following [14]: (i) prediction
of future social outcomes; (ii) test-bed of social hypothesis;
(iii) discoveries of new relationships and principles.
The principles of social simulations came from the studies
of John von Neumann and Oscar Morgenstern on Game
Theory [1]. Later, the Neumann’s model of self-replicating
machines motivated him to work with Stanislaw Ulam in the
construction of the first cellular automata model [6], giving
the first steps in the direction of ABSS. Each cell in a
cellular automaton can be seen as a very simple agent,
because it “perceives” its neighborhood, perform very
simple computations on this “perception” considering a
small set of rules and, finally, “act” (i.e. change its state and
maybe the environment).
Based on the model of self-replicating cellular automata,
John Conway builds the Game of Life, demonstrating
graphically that very simple local rules can generate
complex global patterns. This way to view complex systems
has influenced recent studies, including “the new kind of
science” of Stephen Wolfram [4].
The Conway’s Game of Life influenced a new generation
of social simulations, like the Boids algorithm developed by
Craig Reynolds to simulate flocking birds [14]. The Boids is
widely used in high definition computer graphics animations
to simulate swarm behavior. The agent-based approach,
following Reynolds’s works, became more evident with the
creation of the research field of Artificial Life (ALife).
Joshua Epstein and Robert Axtell pioneered in ABSS
through their works in simulating social, economic and
biologic phenomena, using explicit agent-based models [1].
Later, Epstein and Axtell created the first general purpose
ABSS model, the Sugarscape [4]. Recently, Ron Sun has
proposed the adoption of more realistic cognitive agents’
architectures [8], influencing a new generation of highly
plausible cognitive social simulations.
This work is strongly motivated by previous instigating
results (of the same authors) obtained using the Vidya multi-
agent systems platform. Actually, Vidya was initially
introduced as a god-game based on intelligent agents whose
actions were devised through evolutionary computation
[10]. Later, the Vidya platform was used to simulate human-
like behavior [11], as well as simulate the spread of diseases
over populations in unstructured virtual worlds [12].
The authors believe that the possibility of incorporating
structures, like roads, houses, hospitals, and workplaces, as
well as structural levels such as health, transportation and
communication systems is a natural step on the construction
of more realistic bread of ABSS. No need to stress that all
that helps increasing the realism of produced social
simulations of highly organized societies.

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The next section explains the PAX framework, which
among other functionalities allows a easy specification of
structuring elements and structural levels.
III. THE PAX FRAMEWORK
PAX is an ABSS framework, developed with the Java
programming language (JDK
architecture was conceived to give support to context
specific agent-based social simulations in the following
aspects:
1. Specification of environments – environments are
specified their comprising structures and
structural levels;
2. Specification of entities or objects – objects are
specified by spatial characteristics (positional
coordinates) and the specific structure where
they are located;
3. Specification of objects’ interaction interfaces –
an object interaction interface supply the set of
actions that others objects can produce on it,
considering restrictions based on its state;
4. Specification of agents – agents follow the BDI
(Beliefs-Desires-Intentions) model [15] [16], and
abstract the perception and planning phases,
needing to be implemented together with specific
context intelligent components.

The PAX framework also contains classes (regarding to
the object-oriented paradigm) that help on the instantiation
of specific contexts of simulations, as well as classes for
simulation parameterizations and statistics.
The experiments presented in this paper were completely
implemented with the PAX (Plausible Agents matriX)
framework. Although it is still in development, its main
functionalities are already operational. The following sub-
sections detail PAX further.
6). The framework
A. Environments
PAX environments are made of structures and structural
levels. Figure 1 illustrates a simple environment with the
following structures: 27 houses, 3 factories, 2 roads and 1
hospital. They are all part of four structural levels: housing,
occupation, transportation and health care.


Fig. 1. Simple environment with its structures (houses, hospital, factories
and roads) and structural levels (housing, occupation, transportation and
health care).
The PAX framework supplies classes for instantiation of
context specific structures and structural specific levels, at
the same time maintenance effort is low and as transparent
as possible. Each structure is an object with spatial
coordinates (placement), dimensions (i.e. width and length)
and may contain others substructures. This is an important
feature, as a hospital may incorporate wards with different
levels of sanitation, for example. Each structure also
contains a set of adjacent structures, to which it is linked;
agents can migrate across structures through these links.
In PAX, structures abstract high level cultural symbols
that can be incorporated into the agents’ intelligent
processing. Hence, the presence or absence of social
structures can influence the adoption of different agents’
behaviors. For this reason, structures may function as
instruments to promoting global order in open societies.
The idea of using (abstract) structural levels1 came with
the need of simultaneous specification of parameters to a
group of structures that perform similar functions in the
environment or collaborate among themselves, That is,
structures that have to reach common objectives; e.g.
hospitals and drugstores, although different, are both in the
health care structural level. This concept makes possible, for
example, the experimenter to enable or disable the operation
of all structures of the same structural level at the same time.
The user of the PAX framework (e.g. a social scientist)
may build simulation structures by only implementing some
abstract methods that are context-specific, particularizing the
environment to a target simulation purpose.
The PAX framework can be used not only to support
agent-based social simulations, but also new AI algorithms
that aims to solve computational problems through agent-
based modeling. Structures can be seen, in this perspective,
as a priori knowledge that guides agents to find the solution
in the specific problem. This aspect affords PAX with great
extensibility, regarding learning abilities.

1 They are abstract because they do not represent any real world entity.

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B. Objects
PAX objects are anything conceivable to be included in
one environment. Then, an object has also spatial
coordinates (2-dimensional), a meta-location (i.e. the
structures it is located, or no structure) and an object
interaction interface.
This is the basic class of all simulations. It is used when
the developer want to create context specific objects. These
objects need to have some of its abstract methods
implemented to become operational and effectively
influence the simulation results.
Objects are also automatically perceived by agents, given
some implementer defined restrictions such as distance for
example, and may be considered or not by their intelligent
engine. Of course, the experimenter should only create new
objects that aggregate value to the sought simulation.
C. Objects Interaction Interfaces
As objects interact with each other, the set of rules that
guide their interactions are implemented by objects
interaction interfaces. The PAX object interaction interface
may also include restrictions over actions that an object can
perform onto another.
For example, considers the object syringe-of-vaccine; it
has two possible states: (i) full and (ii) empty. Suppose that
agents can interact with it by performing the following
possible actions: (a) buy vaccine, (b) take vaccine, (c)
dispose the syringe-of-vaccine and (d) re-use the syringe-of-
vaccine. Thus, the interaction interface for the syringe-of-
vaccine object captures its interaction protocol and only
allows, for example, the agent to perform the following
actions: a and b if syringe-of-vaccine is in state i, and action
c if syringe-of-vaccine is in state ii. The action d can be set
to never be allowed regardless of state. Interesting enough,
PAX makes it possible the unthinkable re-use of syringes in
given environments. Notice that this flexibility may
completely invert the semantic value of a syringe-of-
vaccine, but make it highly plausible for simulation of social
problems.
When the PAX experimenter is designing an object and
needs to include restrictions on its behavior during
interactions with others objects, he also has to implement an
interaction interface for the other object. Of course, the
interaction interfaces only allow or restrict behaviors, so
they do not implement any intelligent processes that work on
the selection of plans; this is a private task of agents.
D. Agents
PAX agents are special types of objects designed to be
intelligent. Notice that the framework does not supplies any
intelligent component for agents’ behaviors. The surround
architecture is based on the BDI model, thus the framework
supplies routines that abstract the perception phase (i.e.
perception generate facts, that are part of the agents’ beliefs)
and represent plans of actions mapped out from intentions.
The experimenter, when implementing a specific kind of
intelligent agent, have to build the intelligent component that
will map perceptions to plans of actions (i.e. implement the
mechanisms that generate agents’ desires). The BDI model
was used because of its ability to incorporate high level of
cognition to PAX agents [16].
IV. CASE STUDY, EXPERIMENTAL SETUP AND RESULTS
This section explains the case studied, which was
implemented using the PAX framework. Results of
simulations for several social scenarios are presented and
commented.
A. Case Study: Spread of Disease over Population
The case study was the simulation of an environment
made of 4 agents’ communities that are suffering with a
known epidemic. Each community contains 200 habitants
(i.e. a total of 800 agents), of which 10% of them (i.e. 20
inhabitants in each community) are initialized as sick. Here
we are not simulating a real world disease. We are solely
analyzing the transmission dynamics and if PAX is able to
provide the necessary features for the simulation.
Each agent has a sickness label, indicating if it is sick or
healthy, and a contamination level, indicating how sick the
agent is. The contamination levels of agents initialized as
sick are randomly determined with a uniform distribution in
the interval [0.0; 1.0]. The higher the contamination level of
an agent is, the higher the risk of contamination of others
agents is in the same location. Thus, the transmission is
probabilistic.
In the simulated environment there are 2 hospitals shared
by all communities. The first of these hospitals (Hospital-1)
is the nearest, as show Figure 2. We arbitrarily attributed
costs for an agent to transit from its community to a hospital
and vice-versa. Ad hoc we have attributed the cost 1.0 to
“Community→Hospital-1”,
“Hospital-1→Hospital-2” and “Hospital-2→Hospital-1”.
Also ad hoc we have attributed the cost 2.0 to
“Community→ Hospital-2” and “Hospital-2→Community”.
Another kind of structure that represented a road was built
to link all communities to the Hospital-2. This alternative
route could be made available or not at the experimenter
will. So agents could also follow the alternative route:
“Community→ Road→Hospital-2” and “Hospital-2→Road
→Community”. Actually this was one of the features that
we investigated, which is the impact of attributing different
cost values to alternative transportation ways.
Figure 2 illustrates the communities and hospitals with
graphical indications of their crowdness.

“Hospital-1→Community”,

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Fig. 2. Screenshot of a simple graphical interface developed using the PAX
framework to visualize the simulation progress, showing the 4 agent
communities (top) and 2 hospitals at different distances. The dots are agents
(colors not seen here indicate levels of contamination).
The developed structuring elements, that is, the
“community”, “hospital” and “road” structures, belong to
the “housing”, “health care” and “transportation” structural
levels, respectively. This conception allows global
parameterization of structures, as explained in section III.
Regarding to the disease dynamics, sick agents can
disseminate the disease to others that are inside the same
structure. In the same community, the presence sick agents
enhances the probability of healthy agents be infected.
Concerning to hospitals, we investigated the possibility of
them contributing to be disease dissemination areas as well,
instead of simply disease treatment locations. Thus, we
simulated scenarios where agents can disseminate disease
even in hospitals (i.e. scenarios with nosocomial infection)
and others where disease cannot be disseminated in
hospitals.
Notice that in the beginning of all simulations agents do
not know the best options of actions to perform. As the
simulation progresses, they learn through reinforcement
learning to tune-up their behavior to their particular needs
and according to their current state. Agents may be in any of
the possible states:
1. At a Community and healthy;
2. At a Community and sick;
3. At Hospital-1 and healthy;
4. At Hospital-1 and sick;
5. At Hospital-2 and healthy;
6. At Hospital-2 and sick.

For each state above there are a set of possible actions
that can be performed by an agent. The possible actions for
the agent are the following:
I. No action;
II. Go to Hospital-1;
III. Go to Hospital-2;
IV. Go to Hospital-2 via Road (if available);
V. Go to Community;
VI. Go to Community via Road (if available);

The combination of states and actions result in a transition
graph, illustrated in Figure 3. Notice that inhabitants may
not migrate from one community to another in some
particular simulations.


Fig. 3. Agent’s transitions graph. Learning here can be understood as
weighting each transition by reinforcement through agent’s experience.
The agent’s learning mechanism consists of weighting
each transition by reinforcement originated from agent’s
experience (interaction) with the environment. The weights
of transitions represent probabilities of selecting the actions
they codify, so the action selection is not deterministic, but
probabilistic.
B. Experimental Setup
The main objective of the carried out experiments was to
verify the impacts of inserting a new social structuring
element (the road) on the agents’ behaviors (i.e. the actions
they select), considering different associated costs. The
experiments also investigated the impacts of nosocomial
infections (i.e. disease transmission inside hospitals).
Therefore, the simulation has 3 parameters: (1) the presence
or absence of the road; (2) the cost to use the road; (3)
presence or absence of nosocomial infection.
The road, when “on”, links communities to Hospital-2,
reducing the cost when compared to the main route (i.e. no
road). Notice that the main route to Hospital-2 costs 2.0. We
used 4 possible road costs: 1.0, 0.5, 0.25 and 0.1. Notice that
all these costs are less than the main route cost.
We combined all possible configurations of the above
parameters. Each configuration was executed 5 times, and
final results are average over the 5 executions (in all cases
standard deviation was so low, so there are no outliers).
All simulations last 2000 iterations (meaning 2000 agents’
actions) and approximately 3 minutes running in a regular
desktop (Pentium IV 2.8 GHz, 512 MB of RAM). This
number of iterations was deemed to be sufficient to
distinguish different tendencies for each configuration.
Simulations were inspected every 200 iterations (i.e. 10
times).

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C. Simulation Results
The first experiments aimed to verify the agents’ behavior
without a road that links all communities to Hospital-2.
Figure 4 shows the percentage of sick individuals over
time in the scenario without road, and, with and without the
possibility of nosocomial infection.

Fig. 4. Percentage of sick individuals, without road. Notice that curves for
the two scenarios (with and without nosocomial infection) are similar,
indicating that in both cases sick agents give preference to stay at their
communities, instead of going to hospitals.
The similar curves may suggest that hospital attendance
was drastically reduced even in the first iterations of the
simulation. That is sick agents give preference to stay at
their communities without treatment. This hypothesis is
confirmed in Figure 5 and Figure 6 (i.e. number of
appointments) and is justified by the high cost of
transportation to either hospital.


Fig. 5. Attendance figures of Hospital-1 and Hospital-2 over time, without
road and without nosocomial infection.


Fig. 6. Attendance figures of Hospital-1 and Hospital-2 over time, without
road and with nosocomial infection.
Notice the clear difference between curves of Figure 5
and Figure 6, revealing that in scenarios of nosocomial
infection, agents relinquish more rapidly hospital help. In
both cases, the majority of the sick agents give preference to
go to Hospital-1, instead of going to Hospital-2, again this is
explained by cheaper transportation cost.
When we analyze the average contamination level of
population over time (Figure 7), it is possible to identify a
small difference in the curves that represent the scenarios
with and without nosocomial infection. The curve for
nosocomial infection has a faster growth, although it reaches
a little higher plateau when the simulation stabilizes
(approximately after 800 iterations).
When we include in the environment a road that links all
communities to Hospital-2, we observed a totally different
dynamics for different road costs. This could be seen as a
metaphor for government positive action towards public
health. Figure 8 and Figure 9 show the percentage of sick
individuals when the road is present, with and without
nosocomial infection, respectively.


Fig. 7. Average contamination level of population without road, for
scenarios without and with nosocomial infection.

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Fig. 8 Percentage of sick individuals, with road and without nosocomial
infection.


Fig. 9 Percentage of sick individuals, with road and with nosocomial
infection.
Results show that the possibility of nosocomial infection
is less important to reduce the number of sick individuals
than the transportation costs, although it accelerates all
curves growth. It can also be noticed a small non-linearity
on the relation between the road cost and the percentage of
sick individuals of the population.
Figure 10 and Figure 11 show the average contamination
level of the population with road, without and with
nosocomial infection, respectively. Here we see that the
cheaper is the road, the lesser the average contamination
level of the population. For the road costs 1.0 and 0.5, the
possibility of nosocomial infection accelerates the growth of
the average contamination level. When transportation costs
of the road to Hospital-2 are very low (0.25 and 0.1), the
impacts of nosocomial infection are much reduced. Similarly
to Figures 8 and 9, it can be noticed a non-linearity on the
relation between the road cost and the average
contamination level of population.


Fig. 10 Average contamination level of population over time, with road and
without nosocomial infection.


Fig. 11 Average contamination level of population over time, with road and
with nosocomial infection.

Figure 12 and Figure 13 show the percentage of
attendances of Hospital-2 in the presence of road, with and
without nosocomial infection, respectively. Notice that the
agents rapidly learn to choose the Hospital-2 as the best
option (i.e. lower cost) and that the percentage of
attendances of Hospital-2 is linearly related to the road cost.
According to the similarities of Figures 12 and 13, the
possibility of nosocomial infection has little influence over
the percentage of appointments in Hospital-1 and Hospital-
2. This was the case because in our simulations there was no
exchange of experiences between communities and agents.

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Fig. 12 Percentage of appointments in Hospital-2, with road (costs 1.0, 0.5,
0.25 and 0.1) and without nosocomial.


Fig. 13 Percentage of appointments in Hospital-2, with road (costs 1.0, 0.5,
0.25 and 0.1) and with nosocomial.

Figure 14 and Figure 15 show the some of the already
presented information relative to average contamination
level of population, this time allowing agents to share their
findings and migrate to others communities. What one can
observe is that knowledge spreads very quickly to the whole
population. Notice the maximum value of the graphs scales
(0.1), indicating a quick convergence to low values of
contamination level when communication is enabled.


Fig. 14 Average contamination level of population. Scenario with road,
without nosocomial infection and communication among agents enabled.

Fig. 15 Average contamination level of population. Scenario with road,
with nosocomial infection and communication among agents enabled.
We observed that the results shown in Figures 14 and 15
are homogeneous in the population level (this is not evident
in figures), because it was not incorporated any segregation
mechanism in the agent level (e.g. reputation). Most
certainly, the next step is the incorporation of such a
segregation mechanism based on ascribed reputation among
agents
V. CONCLUSIONS AND FUTURE WORK
In this paper the authors have investigated the impacts of
structuring elements on agents’ behaviors for social
simulations. The case study selected was disease
dissemination in an artificial population split into four
communities with and without the existence of a structuring
element (i.e. road) in the agent’s world and some specific
contextual variations (i.e. presence and absence of
nosocomial infections). Learning is carried out be
reinforcement drawn from interaction between agent and
others world entities. The paper also introduces the PAX
framework, which was used to carry out all simulations.
In our simulation model, which adopts structuring
elements and structural levels, the meaning of the structures
is intrinsically related to the simulation contexts. However
this can be easily parameterized by an interested
experimenter due to flexibility of PAX. In line with this,
structuring elements and structural levels can be set to
produce highly detailed simulations. They can also abstract
a priori knowledge that can be used by agents to accelerate
global and local learning.
Results demonstrated the high importance of structuring
elements – in the simulated scenarios a road – in the whole
agents’ population dynamics. We see clearly that the
introduction of such structuring element not only caused
impacts on immediate behavior of agents, but in the overall
performance, which in our case study is measured in terms
of disease spreading and contamination level of agents.
Regarding to specific aspects of the simulated context,
besides the simplicity of the modeled environment, it can be
observed a very coherent emergent social behavior of
agents, indicating that the model can be explored in
simulating highly complex social scenarios in health
contexts, benefited with the possibility of incorporating also
highly complex structuring elements and other structural
levels (e.g. broadcast of health information over a
population through communication mechanisms).
Overall results revealed that PAX framework is a flexible
means to perform social simulations and also indicated the
importance (i.e. impact) produced by structuring element as
a means to overcome undesired effects on stochastic
phenomena on a population of agents. We argue that social
scientists may profit greatly of using the platform just
presented, as it is able of incorporating behavioral, temporal
as well as spatial relations of among entities of the world.

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The authors finally argue that the PAX framework and
assumptions simulated here can be useful on public policies
planning in health care and other governmental fields. For
this, we are investigating new and plausible intelligent
agents’ models with elaborated communication skills,
aiming to enhance their findings in reducing the error of
simulations and real social phenomena.
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