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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
description.
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
reality.
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.
4 OUR CASE STUDY:
MODELING THE CITY
OF TIJUANA
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.
A DISTRIBUTED AGENCY METHODOLOGY APPLIED TO COMPLEX SOCIAL SYSTEMS - A Multi-Dimensional
Approach
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