Social Layers and Population Models Directed by Intelligent Agents
for Estimating the Impact of Operations and Investments
Agostino G. Bruzzone
, Marina Massei
, Christian Bartolucci
and Lorenzo Capponi D’Agostino
Simulation Team, DIME University of Genoa, via Opera Pia 15, 16145 Genova, Italy
MAST srl, via Molinero 1, 17100 Savona, Italy
Keywords: Intelligent Agents, Human Behaviour Modelling, Country Reconstruction, CIMIC, PSYOPS.
Abstract: This research aims to support operations planning and management in complex scenarios where population
and interest groups are critical elements; in particular the paper propose experimental analysis carried out on
a complex South Asia scenario by running an HLA Federation driven by Intelligent Agents; the context is
allows to simulate investments and operations over a an asymmetric mission environment with insurgents,
terrorists, different parties and articulated social frameworks. The proposed scenario is characterized by
various degrees of freedom and it needs to be modelled and simulated in order to evaluate the evolution of
human behaviour and socio-psychological aspects. The authors have developed special models in which
Computer Generated Forces (CGF) are driven by Intelligent Agents (IAs) that represents not only units on
the battlefield, but also people and interest groups (i.e. Middle Class, Nomads, Clans); the study is focused
on Civil Military Co-operations (CIMIC) and Psychological Operations (PSYOPs); while the simulation has
been developed using an architecture that involves various federates in different roles. Along the entire life
cycle of the research processes of Verification, Validation and Accreditation have been applied in order to
determine the correctness and effectiveness of the results and the paper proposes experimental results
obtained during the dynamic test of the federations.
The human factors is a critical element when
investments and operations are planned over a
region; indeed the impact of population point of
view and the interests of related social layers is often
affecting effectiveness and efficiency of operations,
it could introduce risks and/or provide opportunities;
these elements are obviously pretty difficult to be
investigated therefore they strongly affect the overall
success; normally it is fundamental to identify all the
stakeholders and to consider their interests and their
attitude, this require to consider for instance
economic, religious, ethnic groups as well towns,
villages, local leaders as key factor to be consider in
In several geo-political areas it is required to plan
new investments and activities devoted to stabilize
or normalize the situation respect previous critical
conditions (i.e. civil war, insurgency, terrorism, etc);
in these case it is common to develop initiatives
devoted to get support of the local population as well
as to improve the quality of life from several point
of view (i.e. economy, health, civil rights, security,
education, etc.); in order to achieve these results
Civil Military Co-operations (CIMIC), Information
Operations (INFOPS) and Psychological Operations
(PSYOPs) need to be prepared and carried out
So it is evident the interest in being able to model
these activities as well as the dynamic interaction
with the population over a specific framework;
indeed such interactions could be pretty complex
involving many interest groups representing the
different social layers of the population as well as
their distribution over the terrain; obviously these
elements need to be considered even in reference to
the existing situation of the area from many point of
view: infrastructures (i.e. roads, hospitals, schools),
environmental conditions (i.e. weather), specific
actions (i.e. strikes, demonstrations, intimidation
The authors currently have developed models for
these context by creating a new generation of
intelligent agents representing population and
interest groups to drive complex simulation related
G. Bruzzone A., Massei M., Bartolucci C. and Capponi D’Agostino L..
Social Layers and Population Models Directed by Intelligent Agents for Estimating the Impact of Operations and Investments.
DOI: 10.5220/0004635704120419
In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013),
pages 412-419
ISBN: 978-989-8565-69-3
2013 SCITEPRESS (Science and Technology Publications, Lda.)
to these mission environments; therefore it is
important to state that these models and the previous
considerations could be effectively tailored and
applied also in relation to civil scenarios where new
investment (i.e. industries and infrastructures) have
to be plan over a domestic region or a district as
well as during promotional campaign in marketing
Therefore the international context in unstable areas
introduce a good motivation to investigate these
population models ( reconstruction
operations); currently there it is expected that these
models could be very useful for evaluating
alternative planning considering risk, opportunities,
times, resources costs over a complex and stochastic
framework, vice versa the predictive capability of
these simulators is still pretty limited due to the
high degree of uncertainty affecting human elements
and the high influence of specific spot events
(Bruzzone and Massei, 2010). Due to these
considerations, the proposed agent-driven
simulations are devoted to conduct experimental
analysis and decision support by providing reliable
estimations and useful risk analysis, but not the of
the exact time and location of a new riot; indeed
these events are generated usually by an ignition
factor that is highly unpredictable (i.e. a single
phrase or shot in a specific moment).
Considering the proposed context it is evident
that nowadays military mission environments,
especially within countries characterized by different
cultures, society organization and changeable
political situations, require a new approach to
tactical and strategic operations which not only
appreciates military engagements, but also
relationship between civil population, military forces
as well as community evolution and interest groups.
The problem of this analysis is that there are not
universally accepted simulation models and that the
human behaviour modifiers (HBM) are very difficult
to be represented; in addition it is even necessary to
create models of specific operations that are
currently not covered by the existing simulators in
order to take of Civil-Military Cooperation,
INFOPS, PSYOPS as well as of psychological
consequences over population during mission
execution; therefore some existing model/simulator
is currently taking into account these not-kinetic
operations, but usually it is just a qualitative on/off
parameters or a manual script affecting the scenario
evolution; this obviously don’t allow to consider the
complex dynamic of the interaction among different
interest groups that is the basis for situation
The simulator should consider for instance that
digging a well within an area could generate positive
effects on some part of population (i.e. people hired
to carried out the work, owner of the land) as well as
negative effects on other ones (i.e. opposite clan
respect well owner, opposite political party respect
that one involved); these actions generated direct
impact on element of the population living in the
area as well as on the their related interest group and
in addition produce a cascade effect on all the social
networks among people and interest groups. In
addition if due to weather conditions and/or lack of
resource the well constructions result to be affected
by delays this could produce negative impact on the
people that expect the completion to get benefits of
this asset.
All these elements as well as the cascade of
effects could result positive or negative with a strong
influence due to the dynamically evolving
relationships among people and interest groups and
also due to the importance of the specific actions,
the cultural background and the communications
(Seck et al., 2005).
Indeed the diffusion in the region and among the
people and interest groups of the effects of the
actions is modelled based on communications over
different supports (face to face, media, phones) and
considering specific factors; therefore these
communications introduces attenuation factors and
delays; due to the computational workload (i.e. in
our case 300’000 people and 60 interest groups) the
cascade effect could slow down simulation on single
workstations, for this reason it is possible to run the
simulation with correct diffusion models or by
considering that the diffusion happen with fixed
stochastic delays along each single operation phase
(this reduces of drastically the events to be
considered); considering multiple actions on going
concurrently and the main interest to measure final
effects this simplification resulted acceptable,
therefore if computation power is available it is
possible to run the simulator using more correct
In the proposed models it was required to model
these elements and to create a simulation able to
reproduce a scenario where CIMIC and PSYOPS
were conducted; the interest group and populations
were modelled based on a multilayer approach that
consider both population elements deployed over the
terrain and interest groups; in addition these entities
interact with the units of the different parties (i.e.
coalition forces and insurgents).
Figure 1: Multilayer Model including Population, Interest
Groups and Units.
Due to the fact that the intelligent agents reproduce
population behaviour within operations, the model
allows the users to correctly evaluate the reaction of
the civilians not only to military actions, but also
during peace keeping and reconstruction phases.
The research has been developed and tested
through the involvement of Subject Matter Experts
(SME) from different countries; in particular the
proposed scenarios was developed as demonstration
for a R&D (Research and Development) project
named CIMIC and Planning Research in Complex
Operational Realistic Network (CAPRICORN).
These models have been created in order to support
operational planning decisions and to be integrated
with other systems previously developed; it is able
to work as single user or within a federation
architecture (HLA standard); this was motivated not
only by the needs of respecting existing standards,
but even for the opportunity provided by developing
an open architecture to be further integrated with
other simulations in order to cover complex
problems. Due to these reason interoperability
requirements are pretty important and represent a
strategic advantage of the proposed approach
(Bruzzone et al., 2007); (Zacharewicz et al., 2008)
In fact IAs (Intelligent Agents) have a big
potential in addressing these kind of problems (i.e.
Yilmaz and
The most important feature of the models is
represented by the intelligent agents that are able to
simulate human behavior of people modeling their
characteristics; this not only in term of their party
side (neutral forces, friends, enemies and civilians),
but even in reference to their liaisons to different
interest groups and social networks. The capability
to use this approach it is an important support for
applications involving federations of simulation to
address complex scenarios and multiple threats for
training and it is pretty interesting to investigate
their use for supporting operational planning
In the past the authors set up libraries of
innovative models able to simulate different society
attributes represented by riots, agitators and
terrorists (RATS) and IA-CGF modules (Bruzzone
et al., 2008); (Bruzzone, 2008); in some case it was
possible to simulate the whole population of a large
area reacting to a natural disasters (Bruzzone and
Massei, 2006), of a big town respect humanitarian
activities (Bruzzone et al., 2011) or in relation to
health care issues (Bruzzone et al., 2012); therefore
in this case the intelligent agents were extended to
cover, not only entities and units operating on the
field as well as single individuals/families within the
population, but even social objects such as interest
In order to succeed in this process it becomes
necessary to properly design, tailor and experiment
the scenario considering the very large quantity of
elements, variables and parameters; due to these
reasons the M&S (Modeling and Simulation)
process is formulated over three phases: simulation
development, specific mission environment tailoring
and simulation experimentation over the specific
mission environment.
The authors decided to develop an innovative
model of a whole country, taking into account the
features that involve agents able to correctly interact
in the agent based environment; obviously
considering the very broad spectrum of applications
and elements affecting these operations it is critical
to restrict the range of validity and the components
to model based on a detailed analysis to be carried
out among trans-disciplinary teams involving
scientists and operative people (Bruzzone, 2012).
Indeed considering the possibility to use these
agents in order to support decision makers on the
field for planning operations in overseas scenarios it
could be very important to develop a simulator able
to be used by people with no strong scientific
background and using limited computing capability.
Therefore it is necessary to develop models and
simulators able to run correctly based on an
installation and configuration that should be
operated and maintained on field with the kind of
resources that are expected to be available on that
context; users should be able to configure and create
a mission environment; therefore considering the
complexity of these applications it is expected that a
team of experts and analysts will proceed to create a
configuration of the simulator for a specific region
and timeframe (i.e. Kapisa District in Afghanistan
2010) to be used as reference by final users
eventually deployed on site.
More in detail, the first step approached by the
scientists consist of Modelling phenomena, actions
and elements that are specific for a socio-political-
cultural framework. During the conceptual model
creation, simulation expert contributions are
essential to building a proper and effective set of
models and to properly approach the problem thanks
to their knowledge of specific operations and
scenarios; for instance for a region could emerge the
necessity to include nomad behaviours among the
possible alternative occupation of the population.
The second phase regards precisely the
knowledge management: info sources are used in
order to achieve the knowledge basis in order to
tailor parameters and entities of a specific region or
context; indeed to the necessity to determine a
reference scenario a specific mission environment
has to be defined; for instance it is necessary to
collect information about the different political and
economic groups as well as to tailor the importance
of religion and clan factors in term of their influence
in creating a family respect the specific cultural area.
Obviously along the entire M&S process the
VV&A has been performed to ascertain their formal
correctness and their usability effectiveness
according to the imagined use; therefore during the
simulation experimentation is the critical moment to
dynamically test the validity of the models as well as
the functionality of the simulator
A set of mission environment hypothesis are
defined by planners and/or analysts in order to
choose alternative friend course of action (F-COA)
and opposite course of action (O-COA); by this
approach it is possible to plan investments and
operations; a COA could involve CIMIC or
PSYOPS targeting different interest groups over a
specific zone, affecting people in the area as well as
social layers; in addition the decision maker could
define the operation time plan of the investment, the
assigned resource in term of money as well as
equipment and people; obviously during the
simulation multiple operations could be planned and
carried out concurrently or sequentially and the
simulator allow to consider availability of resources,
influence of opposite force actions as well as
weather condition influence (i.e. weather working
days for external constructions).
Each CIMIC or PSYOPS evolves based on
different phases (i.e. for a CIMIC planning,
engineering, acquisition of resources, erection,
commissioning) each one affected by specific needs
in term of money, resources, boundary conditions
(i.e. weather).
The agents are currently driving the behaviour of
population and interest groups respect their
perception of the general situation and their
“feelings” respect on-going activities; the models
use fuzzy rules to estimate the effect of the different
operations respect their nature and their attitude
respect the actors.
Figure 2: Fuzzy Membership for evaluating Mutual
relationships between two Groups of Interest.
Relationships among entities are usually defined
usually by two functions (impact and influence) that
could be defined in term of ownership to
membership functions respect the following classes
(low, medium, high and negative, indifferent and
positive) as proposed in the following graph.
By this approach it is possible to express
quantitative estimations about the effectiveness of
the actions conducted; for instance it is possible to
apply defuzzification in order to transform the
relationships among different critical interest groups
or people over an area in order to estimate their
trustiness respect specific players; for instance the
Overall Trustiness of the Population respect the
Coalition could be estimated or that one of a specific
religious group, village and/or district.
For each mission environment the simulation
Negative Indifferent Positive
Low Medium High
parameters have to be set according to the initial
conditions and hypothesis; then predefined settings
represent the base for the execution of the simulator,
which outputs has to be analysed.
The simulation execution could run in different
operative modes according to the context and user
requirements: stand alone, federated with other
simulators, multiple replicated runs etc.; at the end
the outputs are collected for each single run and
statistically analysed; these results are evaluated
during the simulation experimentation (Spiegel and
Schiller, 1999); a classic and simple approach for
analysing the results it is based on what if analysis
consists of the simulation of different hypothesis
previously formulated (Hill, 1996). Another
possibility that is allowed by simulator is to compare
the Desired Final Effect (DFE) of a scenario with the
Simulated Course of Action (COA).
The user can also first define Key Performance
Indexes (KPIs) and then compare final results of
different planning alternatives basing evaluation on
them; an effective approach to perform a ranking of
different alternatives consists of creating a target
function which has to be able to appreciate and
involve all these Key Performance Indexes. The
latters also represent the reference in order to
develop the cost-benefits analysis, which, together
with risk analysis, gives to the user all the elements
for choosing the best planning alternative.
Considering the complexity of the mission
environment usually Design of Experiments (DOE)
is used in order to complete analysis and produce
synthetic reports (Montgomery, 2000).
Through the feedback from military users with
operational experiences and subject matter experts
on the specific disciplines, it was possible to develop
the models as well as to define the specific user
needs; by this approach to develop and validate the
conceptual models, to perform the definition of the
specific mission environment created for
CAPRICORN Demonstrator and to validate the
functions; during the last phase related to use the
Demonstrator for testing and analysing simulation
results it was possible to complete the dynamic
VV&A of the proposed approach over a specific
case study.
In particular the involvement of the users for
VV&A was based on different phases; during the
first one the focus was to review of key concepts and
operational planning requirements; this allowed to
generate a common synthesis about CIMIC and
PSYOPS, decision making processes, scenario
analysis methodologies, training and risk analysis
requirements; most of the activities carried out in
this phase was organized mostly by desk-top review
and face validation performed through organization
of meetings and workshops; therefore in this phase
some preliminary simplified model was presented
and even executed to share concepts and to validate
and verify model assumptions and IA basic
In the following phases the work was based on
running of the simulation in front of the users and on
analysing experimental results: during this phase
operational planning was carried out through the
cooperation of users, analysts, subject matter
experts, development team and operational planners.
Figure 3: Simulation Interface.
For a tactic scenario, such as the real recently
warfare where Northern Atlantic countries are
involved, is necessary to model civil status and
characteristics like:
Ethnic and Clan
Psychology status;
Cultural / educational level;
Social and Economic Status
Geographic location of object;
Health care status;
Political party;
And especially are considered particular modifiers
of person features, such as:
People relationships, friendships and social
relationships are also considered in agents
algorithms and distributed in a stochastic manner
using Monte Carlo techniques based on consistency
algorithms able to aggregate people respect the
socio-cultural-economic background.
The layers used to model this context includes
the following classes:
Terrain Infrastructure Layers (i.e. Roads),
Terrain Elements, Weather Conditions
People The Population Entities on the
(i.e. Mrs.Baran and/or Sakhi Family)
Groups Interest Groups (i.e. Sunnis, Honey
Producers, Hotaki Clan, Tajik Ethnic
Group, Hamnazar Political Party)
Entities Units on the Terrain (i.e. Coalition
Platoon, Insurgent Group, Riot)
The People have their social networks
interconnecting the population elements based on
familiar and friendship relationships; while each
people object is connected to multiple interest
groups based on his nature with dynamic links
representing his affiliation and the related strength;
in addition groups and interconnected by mutual
hostility and friendship over the social layer,
obviously also these connections evolve dynamically
during the simulation due the actions carried out; in
addition the terrain and Entities affect the behaviour
of people and social layers, while the Intelligent
Agents are in charge of directing the objects during
the simulation for completing tasks and for reacting
to stimuli and to their own situation awareness.
The Capricorn Demonstrator consists of a Simulator
including the Mission Environment Generator based
on Monte Carlo technique applied on statistical
database of the population; by this approach the
whole people objects representing population and all
related interest groups are created and
interconnected by the reference relationships over
the different layers.
It is proposed an example specific to a
CIMIC/PSYOPS mission environment in the Kapisa
Afghan region, considering the related COA and
parameters concerning population, social networks
and groups. The simulation paradigm is based on
stochastic discrete-events simulation and it is
federated within an HLA Federation (High Level
Architecture) both referring to original and
IEEE1516 standards; models were implemented in
Java with different RTI (Run Time Infrastructures)
were tested including Portico, Pitch, VT Mäk.
During the test federation integrated CAPRICORN
Simulator and IA-CGF E&U (Intelligent Agent
Computer Generated Forces Entities and Units)
developed by Simulation Team for modelling units
on the battlefield; the simulation were carried out
over Kapisa Region in South Asia considering
presence of several companies of Coalition Forces,
several units of Insurgents able to carried out O-
COA (i.e. Intimidation), Demonstrations and Riots
generated based on the population behaviours and
simulated within IA-CGF E&U; the operations (i.e.
CIMIC and PSYOPS) as well as the Interest Groups
and Population were simulated by CAPRICORN
Demonstrator over a timeframe corresponding to 1
year. ANOVA (Analysis of Variance) was applied in
order to measure the confidence band on the
controlled variables and the optimal duration time
(Mosca et al., 1993).
As anticipated the population and social
networks, within the simulation, are generated by
CAPRICORN Demonstrator basing on Monte-Carlo
techniques; Groups and people so generated relate
with entities and units as well as with
PSYOPS/CIMIC operations; the actions and events
are affected by stochastic factors considering time,
cost and effectiveness elements as well as all human
behaviour factors.
The outputs of CAPRICORN are related to
performances in term of times, costs and involved
resources during the planned operations as well as a
KPI concerning the evolution of the mutual
behaviour between the critical groups represented in
the chosen scenario (i.e. trustiness of the target
groups of the operation respect coalition forces, or
overall trustiness of the population versus coalition
Figure 4: Estimation of Trustiness among the two critical
Groups of Interest (Coalition and Target Group).
A sensitivity analysis based on DOE was carried out
respect different independent variables such as:
A: Budget Allocated to the main Operation
B: Staff and Resources Assigned to the main
In the figure 4 the effect represents the influence
expressed as ratio between contrast and the square
pure error of trustiness scalar of the target function
“overall trustiness”; the analysis propose the effect
of single independent variables and of their
combination respect this output.
At the end of the simulation process the user
knows the effective schedule of the different
operation phases (i.e. planning, preparation,
supplying, erection/execution,
commissioning/follow-up) and the overall duration
as well as costs, cash flow, impact on the population.
The figure below shows the evolution of
trustiness during the simulation of the CIMIC action
well digging COA; it is evident that the deliverables
of the different phases introduce major changes; in
fact the simulation in this case was executed with the
simplified algorithm for diffusing of
positive/negative reinforcement due to the action
among the population the cascade.
Figure 5: Trustiness evolution with simplified model.
The model allow simulating multiple CIMIC and
PSYOPs actions within different zones and affecting
different groups; these could be planned and studied
over a single simulation run: in this case report
concerns information about their changing along the
time and their effect on the population. So for
example the solution which determines the best
impact on civilians could be identified and
quantified as well as risk estimation could be used to
support decision making.
The figure below represents the mean square
pure error diagram for a CIMIC action Digging Well
from COA in term of variance of the trustiness
respect the replicated runs carried out by changing
the random seeds of the statistical distributions; it is
evident with 25 replications it is possible to obtain
results stable with acceptable confidence in term of
trustiness (~15%).
Figure 6: Digging well action error analysis.
The model is available to be used for several
different kind of investments and infrastructures
such as school construction, police station
installation and digging wells and it could be
extended easily to irrigation infrastructures,
buildings and apartment constructions, roads; in
addition in term of INFOPS and PSYOPS it is
already possible to simulate use of Radio and TV
Media as well as leaflet campaign over a region.
In the figure below the contrast represent the
influence of factor expressed respect the target
function “Trustiness among Coalition and Sunni
Interest Group”.
This paper propose an approach to model operations
devoted to create infrastructures and actions on a
area to improve the social economic situation; the
authors developed innovative models for the
population and the interest groups devoted to
reproduce their behaviour and to estimate the impact
of the new actions the context is referring to the case
of CIMIC, INFOPS and PSYOPS conducted in
South Asia and consider both economic and
operational aspects as well as weather conditions
and possible hostile actions by insurgents.
The research proposed by the authors represents
a modelling approach for reproducing complex
behaviour among population and interest groups
during specific operations.
The experimental analysis provided interesting
results and confirmed the potential of this approach;
currently the authors are working for further extend
the current models for different applications
including industrial and civil cases over domestic
Trustiness COA pro Sunni
Digging Well Action
0 5 10 15 20 25 30 35
Figure 7: Sensitivity Analysis over Specific Trustiness.
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