A Multi-Dimensional Approach
Bogart Yail Márquez, Manuel Castanon-Puga, Juan Ramón Castro
Baja California Autonomous University, Chemistry and Engineering Faculty
Calzada Universidad 14418, Tijuana, 22390, Baja California, México
Eugenio D. Suarez
Trinity University, Department of Business Administration, One Trinity Place, San Antonio, 7821, TX, U.S.A.
Keywords: Social simulation, Complex systems, Distributed agency, Sustainable systems theory.
Abstract: The methodology refers to the forms in which reality and knowledge can be studied; it does not question
knowledge that has been accepted as true by the scientific community but instead concentrates on strategies
to expand the knowledge. This work is motivated by need to establish a methodology for the study of
complex social systems in situations where conventional analysis is insufficient in describing the intricacies
of realistic social phenomena and social actors. The proposed general methodology we describe requires the
use of all available computational techniques and interdisciplinary theories. This growing consensus must
be able to describe all aspects of social life as well as serve as a common language in which different
theories can be contrasted.
The objective of our study is to develop a
methodology and corresponding computational
platform that incorporates available mathematical
and computational theories that have not been
appropriately considered in models of complex
social phenomena. Even though applications of
Multi-Agent Systems (MAS) have been developed
for the social sciences have been widely considered
in some areas such as Artificial Intelligence (AI)
(Gilbert, 2007). The modelling of a realistic social
system cannot be achieved by resorting to only one
particular type of architecture or methodology. The
growing methodology of Distributed Agency (DA)
represents a promising research avenue with
promising generalized attributes, with potentially
ground breaking applications in engineering and in
the social sciences—areas in which it minimizes the
natural distances between physical and sociological
nonlinear systems. In this work we thus lay the
foundations for a DA description of socioeconomic
realities, in a process that weaves different available
computational techniques in the context of DA to
represent social and individual behaviour in a
contextualized fashion, accommodating agents with
limited rationality and complex interactions.
We consider a disentangled agent that is formed
by multiple and relatively independent components.
Part of the resulting agent’s task is to present
alternatives, or ‘fields of action’ to its components.
Correspondingly, the composed agent is itself
constrained by a field of action that the
superstructure to which it belongs presents. We
therefore drop customary assumptions made in
traditional social disciplines and MAS about what is
considered a decision-making unit. To arrive at this,
we redefine what a unit of decision is
by unscrambling behavioural influences to the point
of not being able to clearly delineate what the
individual is, who is part of a group and who is not,
or where a realm of influence ends; the boundary
between an individual self and its social coordinates
is dissolved.
The systems are complex entities that represent
a whole that cannot be understood by looking at its
parts independently (Yolles, 2006). The proposed
Márquez B., Castanon-Puga M., Castro J. and Suarez E..
DOI: 10.5220/0003514402040209
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 204-209
ISBN: 978-989-8425-54-6
2011 SCITEPRESS (Science and Technology Publications, Lda.)
intermediate agent can be thought of as a person, a
family, a social class, a political party, a country at
war, a species as a whole, or a simple member of a
species trying to survive. The archetype of the
agents we attempt to describe can be summarized as
a group of colluded oligopolists, such as the oil-
producing countries of OPEC. As a whole, they
share the common interest of jointly behaving like a
monopoly and restricting their production, but they
cannot avoid having an incentive to deviate and
produce above their quota.
Reductionist linear science has concentrated on
the study of entities that are clearly delineated,
where one could separate what belongs to an agent's
nature against the backdrop of what does not. The
relevant agent is taken to be exogenous, and
therefore disconnected from the system to which it
belongs. At their core, these traditional disciplines
are based on a selfish and unitary agent, or atom of
description. Implicitly or explicitly, these paradigms
claim that all aggregate complexity can be traced
back to the lower level of the system: the strategies
and actions of the selfish agent. In other words, these
represent research agendas that purposely de-
emphasize the existence of any level other than that
of the individual.
The methodology we are aiming to create represents
a novel approach to simulation architectures,
creating a language that links the social sciences to
programmable terminology and that can thus be
broadly applied. The methodology of DA represents
a general theory of collective behaviour and
structure formation (Suarez, Rodríguez-Díaz et al.,
2007), which intends to redefine agency and reflect
it in multiple layers of information and interaction,
as opposed to the traditional approach in which
agency is only reflected in individual, atomized and
isolated agents (Márquez, Castañon-Puga et al.,
2011). The methodology proposed consists of eight
2.1 Determining the Levels of Agency
and their Implicit Relationships
We analyse the social system and the existing
relationships to determine the necessary number and
topology of the necessary levels of description. To
this end, we first identify the problems to solve, so
that we can describe their operation within a
physical framework. This process will in turn allow
us to identify the input and output variables of the
system as a whole as well as all necessary
subsystems, whether these variables are decision
variables or other measurable parameters.
In this process, it is imperative that we adopt a
holistic approach that does not attempt to reduce the
system to its individual components. In the real
world, no phenomenon exists in isolation
(Heylighen, 2008). We must therefore in this step of
our methodology establish the objective functions of
all levels of agency that are considered, as well as
the interactions that are prevalent in the
corresponding networks.
2.2 Data Mining
Studies within the social sciences and in particular in
economics are normally performed on a large data
platforms, in situations in which there is too much
rather tan to few data points. The most problematic
aspect of this stage of the modelling process is to
define data sets that match the desired architecture
(Marquez, Castañon-Puga et al., 2010). The
continuous expansion of available information for
social simulation makes the use of data mining
unavoidable. In our case study, for example, we
have access to many different sources of quantitative
and qualitative data describing both economic as
well as sociological aspects of reality. Many of these
data sets are readily available from governmental
sources. Data mining provides us with the process
for extracting implicit information, such as social
patterns, that reveal ingrained knowledge.
2.3 Generating a Rule-Set
We propose the use of a Neuro-Fuzzy system for the
automatic generation of the necessary rule-set of our
simulation. This phase of the data mining process
can become complicated and computationally
intensive, as the Fuzzy system must determine the
necessary number of layers to describe the norms
and variables to keep track of in the simulation. We
further propose using the Nelder-Mead (NM) search
method, which is more efficient than other available
methodologies such as Genetic Algorithms, as has
been shown in multiple investigations (Rantala and
Koivisto, 2002). The NM method is widely used,
mostly because, in general, this optimization
algorithm tends to produce more precise models
with fewer rules (Stefanescu, 2007).
2.4 The Modelling Based on
Distributed Agency
Modelling based on DA allows us to better
understand the structures and relevant processes of
social systems (Gilbert, 2007). MAS models and
artificial societies are currently built on common
themes, generally using techniques that stem from
dynamical systems modelling, but also using tools
from cellular automaton platforms, genetic
algorithms and DA systems. The difference in
available approaches are normally concentrated in
the idiosyncrasies of the particular model and the
design of the research methodology (Drennan,
2005). The modelling process, the researcher must
build a basic model of the system to be analysed,
where the most important aspects to be represented
are stressed. This can normally be achieved using an
approximate dynamical representation in terms of
stocks and flows in the system, focusing on points
where information a decision making can be
transformed into decision rules. The process of rule
generation that will stem from this original
framework refers back to the previous step (defined
in step 3), and it is focused on the behaviour of the
agents that are influenced by the decision rules in a
probabilistic fashion.
2.5 Implementation
The implementation of the simulation can be done in
vast array of platforms, but the social scientist that
does not want to spend a large amount of time
working on code may simply choose to base this
step in the NetLogo simulation platform, which is
free, readily available, easy to understand and
widely used. Because of its voluminous library, this
platform is ideal for modelling social phenomena. It
is capable of modelling complex systems which can
independently provide instructions to thousands of
interdependent agents operating in a holistic
environment (Wilensky, 1999).
The NetLogo platform also allows us to easily
assign geographical information, that is, by creating
simulation data that represents vectors of
information that include a special component (Vidal,
2007). It is an appropriate platform for the modelling
of a wide range of complex systems that have
temporal dynamics, allowing the researcher to assign
independent instructions to different agents at any
given moment. This relevant characteristic of
NetLogo can provide the researcher with
opportunities for finding the connection between the
micro level of behaviour of a multiplicity of
individuals and the macro patterns of behaviour that
emerge from the interactions of the individuals
(Wilensky, 1999).
2.6 Validating the Model
Real-world simulations must include some form of
validation (Drennan, 2005). This validation will
ultimately reflect the consistency between the real
world and the artificial model. Based on the results
obtained during the validation, the process must then
go back to the beginning, so that the problems found
can be addressed and the model refined.
2.7 A Simulation and Optimization
In this phase of the process the researcher must
provide a statistical evaluation of the models outputs
to determine the quality of the simulation based
upon some pre-established evaluation criteria of
performance measurement. As part of this process, it
is important to verify whether the object of interest
reflects seasonal aspects, in which case the data must
be transformed so as to analysed the transformed
stationary data. Finally, in this stage a methodology
for experiment design must be adopted, and it must
be based on repetitions of the simulation performed
with the exogenous variables set at significantly
different levels.
2.8 Analysing the Outputs
In this last stage, the results of the model are
analysed, so that the researcher can understand the
aspects of interest in the behaviour of the system. It
is these outputs and their ultimate understanding that
can then be used to make sense of the social system
in study.
Computer Simulations can aide in the understanding
of social phenomena, by explaining and predicting
many aspects of its chaotic nature. This
methodology is academically young, but it has been
consistently growing in the scientific field and has
already been used successfully in a number of
research projects (Becker, Niehaves et al., 2005).
Furthermore, available computational techniques can
facilitate the selection of relevant data as well as
aide the processing of information, in processes that
involve high performance computing. It is through
this process that social simulation is developed and
potentially the most efficient way of making sense
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
of the vast amounts of information available today
(Rossiter, Noble et al., 2010).
This growing field is intrinsically
interdisciplinary, naturally linked to the sciences of
complexity and to systems theory (Miler and Page
2007). To carry out a useful simulation of a social
system the methodology must be holistic. The
intention is to create a representation that can make
reference to different levels within a given reality
within a general methodology; taking into account
that each level is separated from others in ways that
cannot be described in a reductionist fashion, that
are to some extent in different dimensions and thus
following different rules and temporal granularities
(Suarez, Rodríguez-Díaz et al., 2007). One of the
corollaries of this approach is that an entity that is
represented as a multiplicity of agents in one level
may be considered a unitary agent in another level of
A complex system is composed of subsystems
that may be simple and complex, linear and
nonlinear. Simple, linear systems are in turn
composed of particles and the system. On the other
hand, complex systems require at least three
hierarchical levels: particles, agents and the system
(Halloy, 1999). In a complex system—such as that
of a group, an organization, a growing population or
a market economy, where the self-created
organization comes about from of the interaction of
many component parts—the macro patterns are not
easily discernible or understood from the
understanding of the behaviour of the individual
parts, whether these are simple components, autarkic
agents or rational consumers (Mitchell and Newman,
2002; Ashby, 2004). One of the main challenges of
our approach is to provide a methodology to analyze
the many different levels associated within a social
The proposition implies that the researcher
observe behaviour, and then use backwards
induction to portray the forces at play that could
have given rise to the decisions taken, as well as
patterns and structures that emerged. Traditionally,
we have begun with a clearly defined agent and tried
to understand its actions as a maximization of
objectives given constraints. In the proposed
paradigm, we assume maximization occurs, and then
work towards the delineation of the benefited entity.
As such, this proposition is not a theory or
hypothesis, but rather a language in which different
models can be expressed. The complexities of the
proposed architecture can be endless. This
notwithstanding, the paradigm for a new pandemic
and inter-disciplinary science built in a distributed
agency architecture would accept the
intercommunion by means of a parsimonious model
that is broad enough to accept the nature of realistic
agents, but at the same time tractable enough for the
capabilities of an appropriate MAS simulation,
expressed at a minimum desired level of realism.
The methodology therefore intends to advance the
development of a common language in which novel
ideas can be transmitted across disciplines.
Such a language allows us to compare a model in
which disentangled humans in a given culture have
some degree of independent agency, but are also to
some degree objects of their social circumstances—
to another one in which countries are trying to
position themselves in the evolving global arena, but
are nonetheless fighting with their internal political
differences, as well as with established international
norms and existing treaties. In sociology, for
example, the individual is ascribed little agency
when compared to the group or social structure;
classical economics, on the other hand, grants zero
agency to upper level creatures, as the selfish actions
of individuals are carried by an invisible hand to an
efficient allocation. As it applies to evolutionary
biology, this distinction represents the core of the
controversy between individual selection theory and
group selection theory. The language of distributed
agency can also serve as a common ground in which
individual vs. group selection theories can discuss
their visions of evolution. Just as the process of
evolution perfects individuals, it must as well have
the same effect in groups and societies. The
surviving members of a cooperating group, however,
will not be ‘fittest’ at an individual level; their
individual traits and natures, for example, only make
sense within the context of the cooperating group.
The principal difference between MAS and our
proposed approach is that in our methodology the
space includes transformations performed by a
higher level of agency. This upper-level agent is
composed of lower-level subcomponents the may
enjoy agency in their own right. It is the
responsibility of this intermediate agent to present its
subcomponents with individual phase-spaces that are
tailored to induce the desired behaviour from the
lower-level agents which inhabit it, when it chooses
according to its own objective function. Therefore,
for our proposed work-in-progress case study, if we
consider a municipality an agent, this upper-level
agent is composed by subcomponents, which in our
case study of the city of Tijuana, Mexico, will be
represented by the AGEBS that compose this city.
AGEBS is the terminology used to describe the
different areas of the city that are in turn are
composed of neighbourhoods.
In our study we use three levels of agency: the
upper-level agent is represented by the whole city of
Tijuana, the intermediate agents are represented by
the AGEBS and the lower level agents are the
individual inhabitants of the city.
The purpose of this approach is to define poverty in
each level of agency. Originally, when it began to be
measured, poverty was defined in static and one-
dimensional, and was mostly studied in economic
terms, only referring to what where incomes lower
than was considered to be a minimum level
acceptable for society (Akindola, 2009). It is for this
reason that we consider necessary to propose a
concept defined in terms that are dynamical, diffuse
and multidimensional, as recent studies demonstrate
that poverty is not only determined by income levels
but also by the lack of certain non-monetary
resources and opportunities for improvement, such
as education and access to appropriate living
conditions. It is through this light that we want to
analyse poverty—as a multidimensional concept that
cannot be reduced to its individual causes (Akindola,
Figure 1: Levels of agents represented on the social
In the particular case of the city of Tijuana, the
data set used came from the Instituto Nacional de
Estadística y Geografía (INEGI), the Mexican
governmental organization in charge of gathering
data at a federal level including aspects that are
geographical, socio-demographic and economical.
The data set of the city of Tijuana is divided into 363
areas, known as AGEBS. “The urban AGEB
encompass a part or the totality of a community with
a population of 2500 inhabitants or more in sets that
generally are distributed in 25 to 50 blocks.” (INEGI
2006) For each AGEB we determine the degree of
poverty taking into account 10 income and
employment variables, 23 variables dealing with
education and 15 variables related to the resources
available in the household, such as a television, a
telephone or a refrigerator, among other articles. The
resulting matrix has a total information size of
48x363 variables.
The data sets for this case study were originally
compiled in an information system that is
intrinsically geographical. These systems helped in
the generation, classification and formatting of the
required data—a fact which facilitates the edition of
the different thematic layers of information, in
which one can quantify the spatial structure to
visualize and interpret the areas and different spatial
patterns in Tijuana.
Using the Neuro-Fuzzy system for the automatic
generation of rules, this phase of the data extraction
from the data may become complicated, as the
process needs to appropriately establish the number
of sufficient norms and variables that the study
needs to take into account. Using this grouping
algorithm, we obtain the appropriate rule-set
assigned to each agent representing an AGEB or a
inhabitant of it, the agent receives inputs from its
geographical environment and in turn much choose
an action in an autonomous and flexible fashion
(Wooldridge and Jennings, 1995; Drennan, 2005;
Gilbert, 2007). The purpose of this structure without
central control is to garner agents that are created
with the least amount of exogenous rules and to
observe the behaviour of the global system through
the interactions of its existing interactions, such that
the system, by itself, generates an intelligent
behaviour that is not necessarily planned in advance
or defined within the agents themselves; in other
words, creating a system with truly emergent
behaviour (Botti and Julián, 2003; Russell and
Norvig, 2004). Distributed agents do not necessarily
define agents in lower-levels of description, but
rather consider all levels of agency that are
interconnected in a type of organism that spreads
throughout the system (Suarez, Rodríguez-Díaz et
al., 2007; Suarez, Castañón-Puga et al., 2010).
The methodology we are proposing is developed in a
holistic manner, originally focusing on the
description and interconnection of different levels of
reality, whether these refer to either different
dimensions or different time granularities. The
applications of the approach are ultimately very
general, but they are particularly useful for
interdisciplinary analysis, where different disciplines
overlap or interact in their description of natural or
social phenomena. This general language links
together the developments in computational science
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
with those in the social sciences, as they pertain to
the nascent paradigm of complexity. The resulting
methodology represents a powerful alternative for
complementing, substituting or augmenting
traditional approaches in the social sciences. The
study of interdisciplinary connections, of
consilience, and of modelling several levels of
reality jointly remains an area of research with vast
fields of unexplored territory. The growing
disciplines of Computational Social Science and
Social Simulation should be trail blazers in this
effort. (Márquez, Castañon-Puga et al., 2011)
This work was supported in part by the Mexican
National Council for Science and Technology,
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