Christine Mathew
June 4, 2015
Spatial Modeling Final Paper: Examining Epstein’s “Rebellion” Model
Introduction:
Rebellion is defined as political violence inflicted against a state by
civilians (Boswell, 540). Literature based around the subject of rebellion often
describes the phenomena of civil violence as a product of a series of external
grievances in combination with political factors. Understanding rebellion and why
people rise up and join a rebellion helps to reveal explain how conflicts have
evolved over time (Humphreys, 436). Research about rebellion can also be
useful in re-examining how conflict resolution strategies are conducted, and how
they can be improved (Humphreys, 436).
There have been many attempts in the literature to try and understand
why some people stand up and demonstrate against a government while others
remain compliant. It’s important to realize that not every person has a choice in
whether or not they rebel, as coercive forces often times manipulate people into
taking a position in a conflict (Grigoryan,171). However, there are both rational
and non-rational reasons that are used to help explain why people rebel. On the
rational side, one may choose to rebel in the hopes that doing so may lead to
changes that would improve their condition (Gurr, 130). Non-rationally, there are
psychological findings that suggest that the simple act of expressing one’s
frustrations or grievances is “self-satisfying” (Gurr, 130). However, this still
leaves researchers relatively unaware about the specific associated factors and
internal thresholds that once surpassed, lead an agent deciding to rebel Joshua
Epstein took a complex systems approach to addressing this gap in the literature
by creating a model about rebellion, and the various elements associated with
rebellion.
Contrary to what one may assume, rebellion is rarely sparked out of public
grievances alone, as there are many external systematic elements that play key
roles. One theory suggests that rebellion is likely to occur when the discontented
public has been granted the freedom to ignite action (Gurr, 144). For example,
rebellion is more likely to ensue when a government’s military control has
become diminished, as the lack of enforcement is seen as a weakness and an
opportunity to take arms. It is commonly expressed among scholars that rebellion
occurs not only when people feel justified to commit violence, but also when their
external circumstances allow for rebellion to be feasible (Daly, 473).
The objective of using Epstein’s rebellion model is to examine what local
behaviors at an individual level drive the emergent pattern of rebellion across a
landscape. A complex systems and simulation modeling approach is necessary
to achieve this objective because individual decisions about whether or not to
rebel is largely influenced by neighbor’s actions, and as rebellion rarely spans an
entire geographic region it is useful to model how the shape of rebellion
maneuvers over space and time (Daly, 474). In order to understand the behavior
of rebellion, it is critical to discern what leads an individual to rebel or remain
quiescent. I hope to achieve this objective by running a parameter sweep to
conduct a sensitivity analysis, which will determine which parameters the model
is most sensitive to, ultimately revealing what is influencing individual’s decisions
to rebel.
Methods:
The purpose of this model is to examine the inner workings of civil
violence, and how decentralized rebellion responds to repression attempts of a
government authority. Specifically, this model can be utilized to research what
factors spark rebellion at an individual level.
The entities included in this model are agents and cops. Agents are
characterized by their grievances, risk aversion, probability of arrest, and their
vision. An agent’s grievance is comprised of two parts: hardships (which
represents hardships ranging from physical challenges to socio-economic
hardships) and perceived legitimacy of the overall regime. Agents also have a
unique risk aversion level, which is significant overall in the sense that agents will
rebel depending on whether or not they believe it is worth the risk. Cop
characteristics include vision, which allows them to “see” what agents to arrest.
Cops are able to move within the spaces visible to them, and arrest only the
active agents within their visible range. The global variables in this model are the
factor determining the arrest probability of an active agent, and the threshold at
which the level of grievance must be great enough to trigger an agent to rebel.
When examining the process of the model, it’s import to know what is
going on within each actor at an individual level. Based on their randomly
assigned levels of grievance and perceived legitimacy of the government, agents
will either rebel or remain quiescent. If the agents are actively rebelling, they turn
the color red, and if they are quiescent, they are green. As the shade of green
per agent becomes darker, the level of grievance associated with that agent
becomes higher. Cops, denoted as blue triangles, are able to move about the
landscape, and depending on how far their vision is set to, they can spot agents
who are either active or quiescent. Cops arrest and imprison any active agents
within the scope of their vision. Grey agents are imprisoned agents, and their
sentence is determined by the jail term parameter. Jailed agents leave prison
with the same level of grievance that they had upon arrest. All actions take place
within one discrete time step, which is not representative of any specific length of
time. Jail terms are reduced at the very end of each tick. The spatial resolution is
33 by 33.
One basic principle taken into account is that agents will determine
whether or not they should rebel depending on their grievances and how they
perceive government legitimacy. The model works off the notion that if
grievances are high, and government legitimacy is low, then there may be
rebellion. However, the model also adds the element of each agent’s calculated
risk, by taking into account the theory that states that the risk of getting arrested
is lower with a large population of active agents and a small number of cops, so
agents are more compelled to rebel in this situation (Epstein, 7243).
The emergent result from the traits is the spread of rebellion, a pattern that
stems from the localized decisions made from individuals to rebel. The agents
feel less risk of being imprisoned when there are other active agents around
them, creating a positive feedback loop where participation in the rebellion is
reinforced. Out of individual acts of dissent emerges a widespread rebellion
throughout the landscape, which is measured by the amount of agents switching
their quiescent state to an active one. The emergent property is also sensitive to
and triggered by a decrease in government legitimacy in the model. The agents
were explicitly programmed to decide to rebel or not, and based on their risk
aversion values, the agents made decisions about whether or not the possibility
of being imprisoned is worth rebelling.
The individuals are assumed to sense and consider the possibility of being
imprisoned, and use this for further evaluation when making the decision to rebel
or not. As the local ratio of cops to agents lessens, agents sense that the
probability of getting arrested also lessens, which encourages rebellion.
Stochasticity is present in the random values of grievance (composed of hardship
and perceived government legitimacy) and risk aversion assigned to each agent.
The data collected from conducting a parameter sweep included how many
agents were active when cops had vision ranging from 1-10, government
legitimacy from 100-0%, a stable cop density at 5%, and a maximum jail term
spanning 0-50 time steps. A parameter sweep testing the number of active
agents when all of the aforementioned parameters are set was run in order to
conduct a sensitivity analysis. I evaluated the model based off of surrounding
literature describing the phenomena that may be encountered surrounding the
subject of rebellion in respects to the shape it takes based off of decisions made
at a local level. Though most of the literature focused on large political factors
and their effects on rebellion, this model shows that individual decisions to rebel
are also influenced by the actions of neighboring people.
Results:
The results of the sensitivity analysis indicate that rebellion is largely
shaped by the amount of government legitimacy perceived by the agents.
Grievance alone is not enough to spark rebellion, but when grievance is high and
government legitimacy is low, rebellion will ensue (Fig. 1). However, if agents
have a high level of grievance but government legitimacy is also high, the
chances of rebellion are low. As one can see in Fig.1, government legitimacy hits
zero and a few time steps pass before agents begin to rebel, and when they do,
the rebellion sparks quickly. This means that there must also be other factors at
play. In order to determine if the maximum jail term was having a significant
effect on agents’ decision to rebel, the jail terms were also included in the
sensitivity analysis. The results (Fig. 2) show that maximum jail time does not
quell rebellion nor does it incite rebellion in any way. Another parameter
measured in the sensitivity analysis was the amount of active agents when cops
vision was altered to a maximum of 10 (Fig.3 - this figure zooms in towards the
end of the model simulation run, where rebellion peaks, in order to give a closer
look at the data). As one can see, the amount of cop vision does not have much
influence on individual agent decisions to rebel. When examining Figure 4, it is
clear that there is a spike in rebellion when agent population increases. This
aligns with the theory that if the local ratio of agents to cops is high, agents will
be more likely to rebel because there is less of a chance of them being arrested
(Epstein, 7243). For example, a person in a crowd of ten people is more likely to
rebel if 1 cop was around, than if there were only three potential rebels and 4
cops. The finals results conclude that local decisions to rebel are largely sensitive
to the amount of government legitimacy there is, as well as how many agents are
around, which diminishes their chance of being arrested, and increases the
likelihood of rebelling. As each period of time passes, cops arrest active agents
within their vision.
Figure 1
Figure 2
Figure 3
Figure 4
Discussion:
The results of the model suggest that simple local behaviors, for example one’s
individual grievances, perception of government legitimacy, and personal risk
aversion, can lead to the larger phenomena of rebellion. Rebellion can be
considered a complex system however, because the shape of rebellion is also
influenced by and can adapt to external factors, such as the overall public’s
perception of government legitimacy, the number of available cops or the power
authoritative forces, and the number of people participating in the rebellion. A
positive feedback loop where participation in the rebellion encourages others to
also join in ultimately reinforces the movement of civil upheaval. Stochastic
elements include the individual level of grievance, risk aversion, and personal
perception of the government. Though this model is not completely deterministic,
rebellion always ensued when government legitimacy was non-existent, or had a
value of 0.
Conclusion
Examining Epstein’s model on rebellion helped me understand that
marginalization or collective societal grievances alone do not result in outbursts
of rebellion towards a central authority. Instead, people are largely influenced by
the actions of others, and by collective attitudes felt towards the central authority.
I was surprised to learn that neither top down repressive policies, nor a single
influential rebellious person, were able to instigate rebellion. Instead, the entire
process was fueled from the bottom-up. Other elements that I hope to see
studied in future models of rebellion include the likelihood of rebellion in
geographic regions that have a history associated with rebellion versus in a place
where rebellion is rare or has never happened. The model revealed that people
are more likely to rebel if the risk of doing so were low and if a large amount of
people were also rebelling, so I am also curious if the model could include a
parameter that could account for people seeing on the media or receiving
information from asocial media about how distant neighbors are behaving in a
rebellion. Maximum jail time did not deter rebellion in any fashion, so this model
could contribute to a larger discussion on the ineffectiveness of specific
government control tactics, and how other methods should be explored for
conflict resolution. Understanding the nature of rebellion and how it manifests
from a local level could both benefit authoritative forces, and the frustrated and
fired up masses fighting to have their voices heard.
References
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