Abstract Social and Political Systems Simulation
The Concept of the Space of Ideas and Object-Oriented Simulation
Stanislaw Raczynski
Universidad Panamericana, 478 Augusto Rodin, Mexico City 03920, Mexico
Keywords: Organization Model, Simulation, Discrete Event, Agent-Oriented, Social Simulation, Soft Systems,
Complex Systems.
Abstract: We present an abstract, discrete event model of interactions between organizational structures, using the
agent-base modeling. The parameters of agents, like ability, corruption level, resources and lust for power
are taken into account, among others. The aim of the simulation is to visualize the evolution of the
organizations and the stability of the whole system. It is pointed out that the "steady state" of the model can
hardly be reached. Instead, for most parameter configurations, the model enters in oscillations.
1 INTRODUCTION
The very beginning of the organization theory
should be dated to Plato (427–347 BC), and
then Socrates and Aristotle. The recent theory
is rooted in concepts developed during the
beginnings of the Industrial Revolution in the
late 1800s and early 1900s. Important ideas
have appeared at the beginning of the past
century (Weber, 1948). The idea of the system
appeared in behavioral sociology and other
social sciences, see Gunnell (2013). Recall
that system behavior is not just a sum of the
behavior of its components (non-linearity).
The model presented here is an abstract
one, not related to any real social or political
system. Our aim is to simulate the interactions
between some hierarchical structures and to
see how stable the whole system is. So, the
results should be treated as qualitative only.
This kind of model refers to political, trade
unions and business organizations rather than
welfare or benevolent institutions.
The main goal of any political party is
always to obtain power and nothing more.
Political organizations act as a new agent,
using its members as nothing more than a
medium to achieve its goal. However, in this
model the organization itself is not an active
process (software object or agent). The
organization macro-patterns are the result of the
entity activities. Here an approach and tools
similar to those of Raczynski (2004) are used.
In this work we use agent-based modeling
(ABM). An interesting agent-oriented model,
called the BC model, can be found in the article
by Krause (2000). In that model, the agent
attributes include "opinions", and the interactions
between agents depend on the distance between
their opinions in a non-linear way. Similar
examples can be found in Latane and Nowak
(1997), Galam and Wonczak (2000), Chatterjee
and Seneta (1977) and Cohen, Kejnal and
Newman (1986). Some quite interesting results,
more closely related to the terrorism problem, are
described by Deffuant et al. (2002).
Another, agent-oriented approach is used by
Lustick (2000), in which the agents interact on a
landscape. It is shown that macro-patterns emerge
from micro-interactions between agents. An
interesting conclusion is that such effects are more
likely when a small number of exclusivist
identities are present in the population. The
simulation of other mechanisms of clustering is
described by Younger (2003). That article deals
with the creation of social structures in the
process of food and material storage.
Some more general concepts of
537
Raczynski S..
Abstract Social and Political Systems Simulation - The Concept of the Space of Ideas and Object-Oriented Simulation.
DOI: 10.5220/0005007705370544
In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),
pages 537-544
ISBN: 978-989-758-038-3
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
“computational sociology” and ABM
modeling can be found in the article by Macy
and Willer (2002). Other general
recommended readings in the field are: Bak
(1997), Cioffi-Revilla (1998), Gotts, Polhill
and Law (2003), Axelrod (1997), Epstein and
Axtell (1996) and Holland (1998). An
interesting contribution on modeling the
structure of the Osama bin Laden organization
is included in a Vitech Corporation page (link:
see References, Long, 2002).
Knoke (1994) supposes that the most
important elements of political power are the
relationships of influence and domination
among social actors. Influence is the exchange
of information about preferences and
intentions; domination is the exchange of
material sanctions to reward or punish
compliance with commands.
The very basic and comprehensive text on
the organization theory can be found in Daft
(2013). The book contains classic ideas and
theories, and real world practice. The
problems and questions addressed are related
to the growing bureaucracy, management
ethical lapses, competitors, government, the
environment and the structural changes
needed. However, Daft does not consider
modeling and simulation as an important tool
in organization design.
Another, (ABM)-oriented approach can be
found in Crowder et al. (2012) and Hughes et
al. (2012). In these publications we can find
notes on the potential advantages of the ABM
approach in the field of organizational
psychology.
Many models deal with the survival of the
societies. Cecconi and Parisi (1998) simulate
a survival problem in terms of individual or
social resources storage strategies. Saam and
Harrer (1999) simulate the problems of social
norms, social behavior and aggression in
relation to social inequality. Staller and Petta
(2001) discuss the emotional factor in social
modeling. They introduce the emotions as an
essential element of models that simulate
social behaviors. Stocker, Cornforth and
Bossomaier (2002) examine the stability of
random social network structures in which the
opinions of individuals change. They show that
hierarchies with few layers are more likely to be
more unstable than deeper ones. See also Moss de
Oliveira and Stauffer (1999) for a model of aging
and reproduction. The problem of survival and
self-destruction treated from the ABS framework
can also be found in my Raczynski (2006).
Adamic and Adar (2005) address the question
of how participants in a small world experiment
are able to find short paths in a social network
using only local information about their
immediate contacts. On the email network they
find that small world search strategies using a
contact’s position in physical space or in an
organizational hierarchy relative to the target can
effectively be used to locate most individuals.
From a newer publications we should mention
the book edited by Edmonds et al. (2007). The
editors aimed to present a flyover of the current
state of the art. The papers are divided into three
parts: model oriented, empirically oriented, and
experimentally oriented. In the other publication
of Edmonds (2012) we can find an analysis of the
role and effects of context on social simulation.
Silverman et al. (2013), present a model of a
human population which illustrates the potential
synergies between demography and agent-based
social simulation. Elsenbroich (2012) asks what
kind of knowledge can we obtain from agent-
based models. The author defends agent-based
modeling against a recent criticism. Sibertin-
Blanc et al. (2013) present a framework for the
modeling, simulation and the analysis of power
relationships in social organizations
The agent-based modeling is a powerful tool,
very different from other modeling paradigms,
mainly Systems Dynamics (SD). In SD we start
from the interaction rules for the model variables
and from the structure of the real system to
generate the system trajectories. In the ABM the
interactions between the global variables are
unknown, and the model is constructed defining
the events that may occur in the “life” of model
components (agents). Some artificial intelligence,
like the ability to take decisions and to interact
with other agents can be added to the agent
specification. The global behavior of the model,
the trajectories of the model variables and their
eventual relations are the results of the
SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand
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simulation. In other words, the agents form a
system, which behavior is not just a sum of
the actions of individual components. This is
the property of non-linearity (see Schachter
and Singer (1962)). Obviously, no differential
equations are defined or used, like in SD. This
is the great advantage of ABM simulation,
because not all what occurs in the real system
is governed by the differential equations
(something difficult to understand by
electrical engineers). An exhaustive
comparison between SD and ABM has been
done by Borshchev and Filippov (2004).
Our model is rather abstract and can hardly
be validated for real organization in a
quantitative sense. However, a qualitative
comparison con real organization dynamics
may be done. For example, the oscillatory
pattern of the size of real competing political
parties coincides with the results of our model,
Figure 1: Oscillating nature of organization dynamics.
as shown on figures 1 and 3. The model can
be used to get hints for the properties of the
real system behavior. Note that the members
of the model organizations move over a
political map we introduce here. This map is a
multi-dimensional "space of ideas", which
coordinates may represent, for example, the
level of "democratic orientation",
"totalitarism", "religious orthodoxy" of the
moving entities.
The concept of corruption in this paper
should be interpreted in the very general
terms. It may be an unethical/illegal behavior,
or just a deterioration of certain ideological
patterns or opinions. The corruption level can
be associated with a spot on the political map.
The main assumption is that corrupted spots
provide little benefit to the model entities. So,
the new entities tend to avoid these places.
Blake (2005) considers rationalizations, which
are mental strategies that allow employees (and
others around them) to view their corrupt acts as
justified. Another approach can be found in Pinto
(2008), Lambsdorff (2012), or Earle and Spicer
(2008). However, most of the academic papers on
this subject are based on historic data analysis or
psychological and social issues, rather than
computer simulations.
An interesting, quantitative approach to the
concept of corruption can be found in Caulkins et
al. (2013), related to the earlier work of Schelling,
(1978). Caulins et al. are looking for a "stable
equilibrium levels of corruption" in their model.
The point of equilibrium is found as a solution to
an optimization problem. The decision makers or
leaders are supposed to follow the solution to a
linear-quadratic infinite time nonlinear optimal
control problem. The model is continuous, and its
dynamics is described by ordinary differential
equations.
However, my point is that in the real world,
and in particular in the dynamics of organizations
with human factor, nothing obeys differential
equations, and sometimes even a simple logic. So,
the ABM model, where the only thing we define
are possible events in the most elemental model
components (members of the organization), seems
to be more realistic. As for a possible point of
equilibrium, its existence is rather questionable.
The real organizations are in constant movement
and hardly can rest in a theoretical "equilibrium
point". See, for example, the data provided by
PewResearch Center for the People & the Press,
"A closer look at the Parties in 2012", available
from http://www.people-press.org/2012/08/23/a-
closer-look-at-the-parties-in-2012/ . Figure 1,
taken from that article, shows the oscillatory
nature of the dynamics of the main US parties,
that coincides with the results of the presented
model.
As our model provides qualitative results only,
it can hardly be strictly validated, for example
through input-output transformation. The main
point of this paper is that the ABM modeling can
provide interesting hints on organizational
dynamics. The resulting model movement can be
interpreted as the orbital stability known from the
control theory, see Weinstein, M. I. (1986).
AbstractSocialandPoliticalSystemsSimulation-TheConceptoftheSpaceofIdeasandObject-OrientedSimulation
539
However, remember that no differential
equations are used to describe the dynamics.
So, the concepts of control theory, like
stability, cannot be used here directly as done
by Caulkins et al. (2013).
2 THE MODEL
Our model consists of three hierarchical
structures interacting with each other over a
common (abstract) region. Let us comment
some terms used here.
Entity or agent. An individual that can be
a member of a hierarchical structure.
Organization. A collection of entities,
with a hierarchical structure. In this simulation
no initial structure is imposed on the
organizations. They are self-organizing,
starting from the "chaos" (chaotic set of
entities). Each organization has a corruption
parameter, telling haw corrupt or "spoiled"
the organization is. The corruption level is
calculated as the weighted average of the
corruption parameters of all its members. The
weight is equal to the reciprocal of the entity
level in the organization. The head of the
organization has level 1 (this is the level in the
structure, not the corruption level), its
subordinates have level 2, 3… etc.
Political Map (PM). This is one- or multi-
dimensional region, where the entities are
placed. The PM should be treated in a very
general terms. It can be just a geographical
region, or a generalized space of ideas or
political orientation. For example, in a 2-
dimensional case, one axis may be a religious
orientation (from atheism to religious
extremist), and the other may be the ideology
(from democracy to totalitarism).
PM Corruption Field (CF). The political
and social ideas are subject to wear. What was
supposed to be a good idea a hundred years
ago, is hardly considered as good now. The
CF is a function of the spatial variable
(position on the PM), that tells how "good" the
spot is. It returns zero if the spot is completely
spoiled and one if it is a good one. The value
of CF is used by the entities that appear (are
born, created) on the PM. It may also be used to
control the random walk over the PM. The higher
the CF is, the higher is the probability that the
new entity occupies the place. This property of the
spot on the PM may be the ideological
deterioration (obsolete and erroneous trends and
beliefs) or just a position that, after some time, no
longer provides incentives and benefits to the
entity.
Time. The model time is measured in abstract
time units (TU).
Entity personal data are as follows.
Ability. This is just the ability to climb in the
hierarchy of the organization. Note that such
concepts as intelligence or education do not exist
in this model, being irrelevant in politics.
Lust for power. This is the most important
entity parameter. In other words, the entity may
become a leader if it really wants, which occurs in
the real political life.
Resources. The financial or other resources
that help the entity to climb in the hierarchy.
Corruption level. Takes values of honest to
totally corrupt. The corruption level can be caused
by the unethical/illegal behavior or other causes,
like the rationalization tactics used by individuals
committing unethical or fraudulent acts.
PM coordinates. The place the entity takes on
the PM. In general, it is the entity political
orientation. In our simulation the PM is two
dimensional (mostly for the sake of image
clarity) and its image on the screen is a square.
Life time. The life time determines when the
entity dies or just disappear from PM (natural
death). Life time is defined as a random variable
with density function exp(70.0).
Superior. The pointer to another entity, the
"boss". The entity is one of the subordinates of the
boss.
Subordinates. Pointers to the subordinates of
the entity. For the sake of clarity in the
organization images, it is supposed that the entity
should have four subordinates. So, if the number
of subordinates is less than 4, the entity attempts
to catch more subordinates. The ability, lust of
power, resources and the corruption level are
relative, with values in [0,1].
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2.1 Interaction Rules
There are no global rules: the entities are
being launched and what we obtain is the
result of their individual actions. An
organization is just a data structure and does
not take any actions of its own. However,
organizations behave as if they had a specific
goal: grow and keep growing.
Figure 2: Dark PM spots - spoiled or corrupted area,
white - "good" places.
The simulation program has been coded
using the Bluesss simulation system. Recall
that main concepts of Bluesss are processes
and events. A process is a template, like a
class declaration in object-oriented languages.
At the run time objects (entities) are
generated, being instants of the process
declaration. Within a process a series of
events are declared. The event execution is
controlled by the Bluesss system, which
invokes events in discrete time instants,
according to the clock mechanism and to the
internal event queue. For more detail consult
http://www.raczynski.com/pn/bluesss.htm.
The model includes two processes: entity
and monitor. The "organization" is not
represented by any particular process; it is just
a data structure. So, the organization itself has
no "awareness" and does not take any actions.
Model entities are created by the monitor
process. After being created, the entity takes a
place on the PM, due to a simple rule: the
higher is the corruption level on the spot, the
lower is the probability the entity will appear
there. The monitor also initializes three
organizations, marking three (randomly chosen)
entities as organization heads, and nothing more.
In this paper, the growing organizations have a
simple hierarchical structure. The actions taken by
the entities are as follows.
Seek for subordinates. At the very beginning,
only the organization top entities (heads) seek for
subordinates. This is done repeatedly, until the
entity has gained four subordinates. Each of the
subordinates starts to seek for their subordinates,
and so on. Any entity that has its superior and less
than four subordinates does it. The seek is based
on the distance between the entity and its potential
subordinates on the PM.
Die. This makes the entity disappear from the
PM. The event occurs at the end of the entity life
time. If the entity was a member of an
organization, then one of its subordinates (say X,
if any) takes its place. A subordinate of X takes
the place of X and so on, iteratively. Note that
the entity can also be “erased” by an action of one
of its subordinates.
Climb. The entity eliminates his superior and
takes its place. A subordinate of the entity takes
its place and so on, iteratively. To be able to
climb, the sum of the entity lust for power, ability
and resources must be greater than the same sum
of its superior. This attempt is permanently
repeated.
Figure 3: The relative size of the organizations as function of
time.
Move. This is a slow random walk of the
entity over the PM. The entity changes randomly
its position by a small amount. The event is
repeated every TU.
Propagate. The head of each organization
AbstractSocialandPoliticalSystemsSimulation-TheConceptoftheSpaceofIdeasandObject-OrientedSimulation
541
propagates his own corruption level to all
members of the organization. Each entity
changes its corruption level as follows
entity_corruption_level =
0.1*head_corruption_level +
0.9*entity_corruption_level
This event is repeated each time unit. So,
the corruption parameter within the
organization becomes more uniform.
Modify PM. The entity changes the local
value of the corruption field CF. The whole
PM region is divided into 900 (30x30) square
elements, each of them with its corresponding
CF value. In this event a factor value is
calculated using the following formula.
F = (corruption_level/level + orgcorr)*0.04,
Where corruption_level and level are
parameters if the current entity, and orgcorr is
the corruption level of the organization it
belongs to. So, the entities with lower level
value have less influence on the CF. The
entity repeats this event each 0.5 time units.
The value of the CF is truncated to [0,1].
On the other hand, the CF recuperates
constantly. The monitor process augments the
CF in each spot by 0.015, each time unit. All
this makes the CF change constantly,
depending on how corrupt is the organization
that occupies the spot.
3 SIMULATION
At the very beginning of the simulation run
the monitor process is activated. It creates
1000 entities randomly located over the PM
region. For each entity its parameters are
being defined and the events seek for
subordinates, move, modify PM and climb are
invoked. The entity event die is scheduled to
be executed at the actual model time (when
the entity was created) plus the entity life
time. If the entity has disappeared earlier, this
event is ignored. The necessary events of the
monitor process are initialized, like initiating
organizations (mark the heading entities)
organization state display, and CF recovery.
The monitor process also stores the model
state for further analysis and trajectory
plotting. Then, all other events are executed
automatically. The organizations grow, entities
move and execute their own events. The situation
after about 500 time units is shown on figure 2.
Organizations number 1,2 and 3 are marked
with circles, squares and triangles, respectively.
Small gray points represent new entities, not
affiliated yet. The lines are links superior-
subordinate. The big icon is the organization head,
and the size of the icons decreases for entities
with descending level. The monitor process shows
the situation on the PM with small time steps,
providing an animated image. It is a nice program
feature, where the entities move over the area and
the "spoiled" and "good" regions change intensity
and move.
There are some possible scenarios for the
model behavior. One could expect that the size of
Figure 4: Relative size of organization 3 after longer
simulation time.
the organizations as well as the other variables
will change chaotically. Another possibility is that
one or two organizations will collapse and, after a
long simulation time, and the strongest "winning"
organization will remain. The experiments show
that none of the above occurs. After a short initial
transitory interval, the model enters in quite
regular oscillations. Figure 3 (compare with figure
1) shows the relative size of the three
organizations. In our model everything is
stochastic, so every simulation is different.
However, this oscillatory nature of the model can
always be observed.
4 CONCLUSIONS
The main conclusion is that no steady state is
reached by the model and that the organizations
are in permanent movement. This movement,
after sufficient simulation time, is oscillatory, like
the stable cycles in non-linear, orbitally stable
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dynamic systems (see Chen, 2004).. The
model entities are "alive", executing their
events. Though the decisions they take are
very simple (where to appear on the political
map, climb etc.), they can be considered as
agents of an agent-oriented simulation. The
model may provide interesting qualitative
results. As mentioned in the introduction, the
historical data from the real world are similar
to those obtained from our simulations.
The important advantage of such
simulations is the possibility of obtaining
results that can hardly be reached by other
(analytical, sociological) methods. For
example, how can we see, from the model
description, without simulating, that the
organization size will oscillate with a period
of about 208 time units ? Another advantage
of the tool used here (Bluesss) is the open
nature of the model. New events can be easily
added to the entity process, reflecting a
possible entity behavior and resulting in other,
sometimes unexpected behavior of the
organizations.
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