Geography, Mobilization, and the Outcome of Insurgencies

Sebastian Schutte

(Paper prepared to be presented at the 6th ECPR General Conference, 25th - 27th Aug. 2011, Reykjavik,

Iceland)

Nations are born, grow, �ght, conquer, are conquered, become empires, and rise and fall on thegreat stage of physical geography and human passions and knowledge, and not on the homogeneouswhite planes on which we draw our diagrams.Kenneth Boulding

Abstract

Insurgency has been identi�ed as the predominant type of warfare in civil wars. While the bulk of

quantitative con�ict research is focused on war onset, less is known about the processes and dynamics

that drive insurgency once it is underway. This study provides a theoretical model of how insurgency

develops as a function of geography and insurgent mobilization. The model suggests that geographic

conditions crucially determine the accuracy of incumbent force and thereby its public perception which in

turn determines the rebels' ability to mobilize. These expectations are operationalized in an Agent-Based

Model and tested with large-N data on the outcome of insurgencies. The empirical results con�rm the

theoretical expectations and even contribute to improving the prediction of insurgent civil war outcomes.

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1 Introduction

The most frequent form of large scale political violence in the post-1945 era are civil wars (Harbom and

Wallensteen, 2005). De�ned as an armed con�ict between a government and another political actor from

within the same country, many civil wars are fought based on irregular tactics. In a typical scenario, the

regular armed forces of a government face a public insurgency employing guerrilla warfare (Fearon and Laitin,

2003, Kalyvas, 2005).1 The majority of quantitative research on civil war is focused on war onset and initial

motivation of the rebels. Ancient hatred (Geertz, 1963, Huntington, 1993), economic reward (Collier, 2000,

Collier and Hoe�er, 2004), and ethnic nationalism (Cederman, 2008, Weidmann, 2009) have been proposed

as explanations of why civil wars start. But civil wars can display puzzling attributes once they are underway,

including speci�c spatial distributions of violence (Buhaug and Gates, 2002, Raleigh and Hegre, 2009, Kalyvas

and Kocher, 2009, O'Loughlin and Witmer, 2010) and counter-intuitive dynamics of mobilization: insurgent

movements can start with a very small number of initial combatants and grow in size despite heavy losses

and military inferiority to the incumbent (Fall, 1965, Macaulay, 1978, Herbst, 2004). It is therefore obvious

that a more complete understanding of civil wars must pay attention to their internal processes and drivers,

as it has been done is a small number of recent publications (Lyall and Wilson III, 2009, Buhaug, 2010,

Weidmann and Salehyan, 2011).

This paper attempts to contribute to this line of research by answering the following research question:

What is the role of geographic factors in determining the outcome of insurgencies? The relevance of this

project is threefold. First, a theoretical dispute on the e�ects of force is made explicit and resolved the-

oretically. Second, the inclusion of Agent-Based Simulation techniques allows for illustrating the complex

mechanism of reactive mobilization in insurgencies. Third, the implications of this research are policy-relevant

in that they help to understand the counter-intuitive e�ects of military force in insurgent civil wars.

The paper proceeds as follows: a thorough discussion of the existing literature in the next section paves

the way for a subsequent theoretical discussion. After that, a theoretical model of reactive mobilization under

varying geographic conditions is speci�ed and implemented as an Agent-Based Model. Several hypotheses are

derived and tested in a large-N con�ict-level analysis. Finally, the empirically results and their implications

will be discussed.

1To illustrate the importance of insurgency consider the following numbers: Fearon and Laitin (2003) introduced a datasetwith 127 cases of civil war since 1945 that lead to at least 1000 battle death. Drawing on a subset of this data, Connableand Libicki (2010) identi�ed 89 clear cases of insurgency. Kalyvas and Balcells (2010) report a declining importance of insur-gency in the post Cold War era in comparison to symmetric confrontations in civil wars. However, they identify �symmetricunconventional� wars as a new category that shares many aspects of insurgencies.

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2 Existing Literature

Insurgency and irregular warfare have received major attention throughout the twentieth century, starting

with the works of Lawrence ([1927]1998) on the British-Arab guerrilla campaigns during Word War I. After

World War II and the successful partisan activities against the Axis powers in Europe and South-East Asia,

the topic received major attention in the works of communist theorists, such as Mao ([1938]1967) and Guevara

(1961). The French defeat in Vietnam led to a growing American involvement and counterinsurgency doctrine

moved into the focus of Western military advisers and scholars. While IR literature was majorly concerned

with interstate war throughout much of the 1970s and 80s, a turn toward civil wars has brought the notion

of insurgency back on the agenda in the last two decades in the context of contemporary civil war literature.

There are two recurrent themes in all of these scattered schools. First and most importantly, every serious

student and practitioner of insurgency has stressed its population-centric nature. In Lawrence's case, the

orientation toward the population is a more abstract commitment to the Pan-Arab cause and a vision of

liberation from foreign rule. In his famous metaphor, Mao ([1937]1961, 93) compared the revolutionary more

directly to the �sh swimming among the peasants as if those were the sea. He also stressed the necessity for

a large population basis for guerrilla war to succeed (Mao [1938]1967, 11; 66). Guevara (1961, 2) echos this

basic doctrine: �The guerrilla is supported by the peasant and worker masses of the region and of the whole

territory in which it acts. Without these prerequisites, guerrilla warfare is not possible�. Consequently, the

counterinsurgency school has been equally focused on the problem of public support. Military adviser John

Paul Vann was one of the �rst outspoken critics of military tactics that alienated the civilian population in

Vietnam (Sheehan, 1988, 106). Vietnam would become both the cradle for counterinsurgency theory as well

as a testbed for its application. In order to secure the loyalties of the local population, the �strategic hamlet

program� was intended to separate the `�shes' from the `sea'. Resettling a signi�cant number of peasants

into forti�ed compounds was meant to deny the insurgents access to the civilian population (Greiner, 2009,

34). Awareness to the problem of civilian loyalties developed up to the highest levels of command. The

expression of �winning hearts and minds� of the Vietnamese was made famous through its ritualized use

by President Johnson. The comprehensive failure of the American struggle for South Vietnam temporarily

lowered its military focus on counterinsurgency. With the ongoing wars in Iraq and Afghanistan, the interest

has reemerged and the emphasis on population centric warfare is an essential part of today's doctrine (DOD,

2007, 1-27).

The question of why civilians participate in ongoing insurgency has also been approached in the con-

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text of con�ict research. Rational choice models portray the problem in terms of straight-forward utility

maximization: rebels engage in rebellion if the prospects of economic revenue exceed the individual risks.

Following this logic, Grossman (1999, 269) stated that in many insurgencies the rebels are indistinguishable

from bandits or pirates and Collier (2000) developed a model in which �rebellion is a special case of crime,

with all the di�erences from conventional crime arising as a consequence of the particular loot sought by

rebels� (page 841). Clearly, the solution to stopping an insurgency within this model is simple: increase the

risk for the rebels through the increased application of military force. Certain military leaders have put their

money on this horse. As Kissinger put it: �I refuse to believe that a third-class power such as Vietnam does

not have a breaking point� (Greiner, 2009, 22). Greiner coins the term �tonnage ideology� referring to the

assumption that a su�cient amount of attrition will translate into enemy surrender through a breakdown of

either will or risk-reward calculus (Greiner, 2009, 23). Another source of this kind of reasoning is Air Power

Theory starting with Douhet (1921). Douhet perceived the airplane as an inherently o�ensive weapons and

suggested that future wars would be decided mainly by the strategic bombing capacity of the adversaries. A

breakdown of the will of the civilian population was the essential mechanism in his reasoning. A prominent

World War II advocate of this logic was British Air Chief Marshal Arthur Harris: �There are a lot of people

who say that bombing cannot win the war. My answer to that is that it has never been tried yet.� (Lowe,

2007, 56). Pape (1996) o�ers a scienti�c study on the abilities and limitations of coercion through air power.

A central element in his model is also the balancing costs and bene�ts (page 16). With regard to the policy

debate on how to defeat the Afghan insurgency, this logic is far from dead. Former US ambassador to India

and deputy national security adviser Robert Blackwill makes no secret of his intentions to partition the

country to then unleash the Air Force:

The sky over Pashtun Afghanistan would be dark with manned and unmanned coalition air-craft � targeting not only terrorists but, as necessary, the new Taliban government in all itsdimensions. Taliban civil o�cials � like governors, mayors, judges and tax collectors � wouldwake up every morning not knowing if they would survive the day in their o�ces, while involvedin daily activities or at home at night (Blackwill, 2010).

While the rationalist school and advocates of tonnage ideology do not necessarily agree on all aspects of

counterinsurgency, they share a �rm belief in the basic mechanism: the quantity of violent force by the

incumbent is proportional to the probability of victory, because the use of force translates into increased risks

for the rebels. According to the classic collective action problem (Olsen, 1965), this should provide incentives

for free-riding within the insurgency and therefore a weakening of its military capabilities.

Translated into an observable implication this approach suggests a positive association of the number of

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killed insurgents with the probability of incumbent victory. The strong emphasis on the �body count� as a

measurement of military success in the Vietnam war is a prime example for this logic in action (Greiner,

2009, 105).

A series of scholarly and military contributions strongly disagrees with this theory and its implications.

Especially the use of indiscriminate violence is assumed to have massively negative e�ects for the perpetrator

(Kalyvas, 2006, 150). The killing of innocent civilians and the destruction of property can alienate the

population from the user of force. Even if violence is targeted against combatants, their killing might help

the opponent to recruit additional combatants from their kin. This problem also surfaced in the strategic

discussion on Afghanistan. General Stanley McChrystal refers to this e�ect as �insurgent math� (Hastings,

2010). In an exceptional contribution to the problem of civilian alienation, Ellsberg (1970, 6) introduces

a interesting metaphor. The rebels sometimes provoke military action by the state which in turn alienates

the civilian population and aids the rebels. According to Ellsberg, this mechanism resembles the use of the

opponent's weight in Judo: instead of creating a comparative advantage for the opponent it is essentially

used against them bringing them to fall the harder and faster the more weight they put behind their attack.

Kilcullen (2009) coins the term �Accidental Guerrilla� referring to elements of the local population that

are drawn into the �ght instead of being a-priory adversaries of the incumbent. Clearly, this accidental

process is closely linked to incumbent behavior in the �eld: as civilian casualties mount and destruction of

property continues, more locals might be willing to retaliate independent of antecedent strategic loyalties.

The US Army Counterinsurgency Handbook also stresses the relevance of avoiding unnecessary destruction

and violence and explicitly rejects the body count indicator (DOD, 2007, 5-27). While all of these accounts

approach the problem from slightly di�erent angles, the basic mechanism hereafter referred to as �reactive

mobilization� is obvious: instead of increasing the risks for the rebels and weakening the insurgency, state

violence can have the opposite e�ect. More civilians are repelled by the government's actions and join the

rebellion. Fearon and Laitin's widely cited study opened the �oor for incorporating the alienating e�ects of

violence into their global analysis, reducing economic variables to proxies for more complex social processes:

We hypothesize that �nancially, organizationally, and politically weak central governments renderinsurgency more feasible and attractive due to weak local policing or inept and corrupt counterin-surgency practices. These often include a propensity for brutal and indiscriminate retaliation thathelps drive noncombatant locals into rebel forces (Fearon and Laitin, 2003, 3).

The central role of civilians in civil wars has been underlined repeatedly in the literature: �The �ght is

conducted through the people [...] Its like a man who has to hit the opponent through the body of the

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referee� (Durell, 1957, 224 in Kalyvas 2006, 91). In his 2006 book, Kalyvas laid out a theoretically thorough

explanation of how violence against civilians is applied in civil wars. A central piece in this theory is

the distinction between selective and indiscriminate violence. The former is applied more accurately against

combatants, o�cers, and informants of the adversary and the latter is applied less accurately against civilians.

Drawing on a stylized spatial model that consists of �ve zones, Kalyvas (2006) concluded that violence is used

most indiscriminately in zones of predominant, but incomplete control. Assuming that zone 5 is associated

with full insurgent control and zone 1 is associated with full incumbent control, violence is most likely to erupt

in zones 2 and 4. With regard to how violence feeds back into the dynamics of control, the theory makes the

case for both coercive and alienating e�ects, but ultimately assumes both violence and civilian collaboration

to be endogenous to military control (Kalyvas, 2006, 12;118-132). In a recent study explicitly devoted to

explaining the outcome of insurgencies, Lyall and Wilson III (2009) argue that modern combat tactics have

undermined the incumbent's ability to win over the civilian population: increasingly mechanized armies

and long-range supply systems make contact with the civilian population less frequent and less important.

However, the authors argue that this type of interaction with the civilian population is essential to secure

loyalties and to learn to tell apart friend and foe. Their study provides an especially interesting point of

departure since it is also devoted to the problem of understanding the outcomes of insurgent civil wars.

Therefore, their empirical study is an important benchmark for outcome prediction and provides essential

control variables for the subsequent analysis.

Before turning to the empirical analysis, however, there is a second recurrent theme in many studies

on insurgency to be taken into account. This theme is geography and the line starts again with Lawrence.

The superior ability of irregular forces to travel the desert informed his strategic decisions (Lawrence, 1998,

157). Mao (1967, 7) assumed guerrilla war to be most feasible when employed in large countries (such as

China) where the conventional forces of the incumbent or the invader tend to overstretch their lines of supply.

Guevara put a higher emphasis on escaping the state's reach through the utilization of di�cult terrain:

As we have already said, guerrilla �ghting will not always take place in the country most favorableto the employment of its tactics; but when it does, that is, when the guerrilla band is locatedin zones di�cult to reach, either because of dense forests, steep mountains, impassable desertsor marshes, the general tactics, based on the fundamental postulates of guerrilla warfare, mustalways be the same (Guevara, 1961, 10).

Consequently, the counterinsurgency community has also identi�ed geography as a decisive factor. John Paul

Vann was troubled by the low degree of urbanization in South Vietnam in the early 1960s. With an estimated

85% of the population living in the countryside, guerrilla recruitment could take place largely unnoticed by

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Figure 1: Boulding's original LSG concept: the states A and B have unequal military powers. Although A'spower declines over distance, it is still superior to B's even in B's capital.

the central government (Sheehan, 1988, 50). In the context of con�ict research, geographic indicators have

been also taken into account. McColl (1969) o�ered a geographic model that identi�ed suitable territorial

bases for guerrilla movements.2 A classic model on the role of distance in interstate wars was presented by

Boulding (1962). Boulding assumed that a state's ability to project power toward an enemy was dependent on

both its military strength and the distance that separated the adversaries. His notion of a �Loss of Strength

Gradient� (LSG) assumes that for every unit of distance, a certain number of personnel had to be subtracted

from the �ghting forces and added to the supply troops. In theory, putting this relationship into numbers

allows for the exact calculation of the geographic limits in power projection. While still employed in the

context of interstate con�ict research (for example Gartzke, 2010), the model has also been modi�ed to serve

in the context of civil war research (Herbst, 1992, Buhaug and Gates, 2002, Herbst, 2004, Cederman, 2008,

Buhaug et al., 2008, Buhaug, 2010). Figure 1 illustrates Boulding's model.

Apart from distance, mountainous terrain has been identi�ed as a determinant of war onset. Fearon and

Laitin (1999) theorized that especially rough terrain allows �for a guerrilla movement to subsist and thrive�

(page 21). Although the percentage of mountainous terrain in a given country is fairly basic indicator, Hegre

and Sambanis (2006) found this measurement to be a robust predictor for civil war onset. With more large

and accurate event data sets being available to the research community, the question of where �ghting takes

place in armed con�ict has emerged as an independent research topic. The spatial footprint of insurgent

warfare has been analyzed for Liberia (Raleigh and Hegre, 2009) and Vietnam (Kalyvas and Kocher, 2009,

Kongsgard, 2010). Moreover, the settlement patters have been linked to both the likelihood of civil con�ict

2In the study of interstate war, geography has been identi�ed early on as an essential variable (von Clausewitz, 1873, BookV, Chapter 17). Apart from the direct military aspect of terrain utilization, the spatial arrangement of political actors shapesand constraints the opportunity structure in armed con�ict. The conceptual distinction between opportunities and motivesfor war places a high emphasis on this aspect of geography (Starr, 1978). Research on interstate war has also advanced theunderstanding of �relevant neighborhoods�: the set of states within military reach for any potential aggressor (Lemke, 1995).

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as well as the occurrence of con�ict events (Weidmann, 2009, Weidmann and Ward, 2010).

With a strong emphasis on geography and civilian population, the existing literature has laid out many

of the conceptual tools for this research. However, several theoretical problems remain unsolved. Although

the signi�cance of civilian population and geography in insurgent warfare has been widely acknowledged

they are usually treated as unrelated topics and their exact roles remain disputed. A theoretical approach

that integrates the two is missing so far. Moreover, some of the open theoretical questions deserve explicit

attention:

First, and most importantly, there is the unresolved question of how state reach can explain attributes of

modern insurgencies: The LSG provides an elegant and simple formalism for why rebellion is more likely and

more successful in a state's periphery. However, the two most prominent advocates of state reach explanations

stress the mostly historical relevance of their research:

The airplane and now the missile have brought about a revolution of quite unprecedented dimen-sions, as we have suggested earlier. For the air-born carrier or weapon, the world is an almostfeatureless globe: coasts, mountains, deserts, and forests hardly exist as long as there are landingstrips on the other side of them. The intricate geographical structure of national power, therefore,which rests on the combination of sea-power with a very low LSG and land-power with a muchhigher LSG, has largely been swept away as far as air-power is concerned. Everywhere now isaccessible to everybody; there are no nooks, corners, or retreats left, and no snugly protectedcenters of national power (Boulding, 1962, 272).

Similarly, Scott (2009, xii) remarked that his own theory of statehood's inability to permanently integrate the

South-East Asian highlands is largely irrelevant for the post Word War II period. Scott assumed that terrain

friction accounts for a large variety of political attributes of the region, including the geographic shapes of

states, the spatial distribution of literacy, and much of its early modern history of political violence. However,

with the infrastructural and logistical advancements of the last sixty years, terrain friction has lost much of

its signi�cance according to Scott.

In Boulding's case, the question of how a geographic model that was deemed outdated �fty years ago by its

own creator can deliver reasonable predictions for the present springs to mind. The mechanism he proposed

assumes supply lines to be hold and maintained at all times by soldiers that are permanently occupied with

this task. It is agnostic to mechanized forces or air lifting capabilities as potential game changers. With few

exceptions, modern states can execute violence within their territory. Any air force today has the technical

ability project its force within the territorial boundaries of its home state. Moreover, successful insurgencies

were won against superpowers with global strike capabilities, such as the United States and the Soviet Union

in Vietnam and Afghanistan. This �rst remaining question can be summarized like this: if the problem of

8

state reach has been largely solved for the post World War II period how can it then explain aspects of

modern insurgencies?

Another unresolved question is the systematic e�ect of violence. Qualitative studies, military accounts,

and anecdotal evidence suggest the existence of reactive mobilization as a mechanism in insurgency: indis-

criminate force creates a backlash in popular reactions and leads to accelerated mobilization for the opponent.

However, this logic is in direct contradiction to the the assumption that increased force leads to victory. Vi-

olence should increase the risk for the insurgent and thereby increase the incentives for free-riding. Instead,

violence seems to sometimes have the opposite e�ect. Despite heavy losses and dim life expectancies for

the individual combatant, some insurgencies were recruiting at peak capacity. The Loss Exchange Ratio in

Vietnam was usually estimated to be around 1:10 against the insurgents. Palestinian Intifada movements

grew strong despite the smallest chance of victory. Afghan Mujaheddin are known for suicide tactics that

completely deny worldly reward to the attacker. Any yet, for many insurgencies the number of available small

arms presents the limiting factor in mobilization and not the lack of volunteers.3 The second open question

is therefore the following: under which conditions does application of violence by the incumbent strengthen

or weaken the insurgency?

A third open question is why counterinsurgency e�orts that explicitly emphasize the problem of alienating

e�ects of violence still fail to reliably secure civilian loyalties. A focus on the selective application of force

is central to counterinsurgency doctrine. Awareness to the problem of civilian alienation can be traced back

at least to World War II. However, a considerable number of insurgencies end with either a rebel victory

or a negotiated settlement (Connable and Libicki, 2010, 27). Interestingly, the advancement of weapons

technology, long- range communication, precision munition, and military doctrine does not appear to have

a�ected this pattern in the post-WWII era. Factors beyond the development of arms technology seem to be

at work in determining the success and failure of insurgencies. The third remaining puzzle can be put like

this: which factor outside the belligerents' control determine success and failure of insurgencies.

3 Theoretical Synthesis

Drawing on a distance-decay mechanism largely inspired by Boulding (1962) and Kalyvas' (2006) typology of

violence, a theoretical model is developed in this section. Most importantly, a strong link between geographic

3Ho Chi Minh was con�dent that more American small arms for Saigon would translate into more captured arms for the VietMinh and therefore a stronger rebellion (Sheehan, 1988, 100). Many Eastern European partisan groups as well as the Cubanmovement of the 26th of July demanded recruits to bring their own ri�es as a condition for joining the group. Therefore, thepopular will to join the lethal �ght can sometimes even exceed the rebels' ability to supply new combatants.

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conditions, types of applied violence, and resulting e�ects of incumbent control are incorporated into the

model.

3.1 Loss of Accuracy and Rise of Resentment

Boulding's LSG might be suitable to estimate a state's potential reach, but it does not explain the devel-

opment of insurgencies over time. It is static in that it predicts con�ict locations and outcome based on

a preexisting distribution of military capability and geography. The theory also fails to account for the

endogenous dynamics in insurgent mobilization. Moreover, a series of cases where actors with global strike

capabilities lost in insurgent warfare point to possible limitations of the paradigm.

Kalyvas typology of violence provides some of the building blocks for a more advanced explanation. His

theory is concerned with the explanation of types of violence in a stylized, one dimensional space based on

the assumption that violence and collaboration are largely endogenous to military control (Kalyvas, 2006,

12; 118-132). The theory is as such not suitable for explaining where military control and therefore patterns

of violence arise geographically. It is also not focused on the question how alienation or coercion feeds back

into the dynamics of con�ict. Moreover, the underlying stylized model does not allow for the identi�cation

of geographic regions that are more or less likely to be controlled by either military actor and more or less

prone to di�erent types of violence.

However, a theoretical synthesis between these models is possible based on additional assumptions. Most

importantly, the application of violence and the tactics of their application are severely restricted by tech-

nological, cognitive, and historical factors. Let us consider technological aspects �rst. A nuclear bomb will

never be able to apply violence selectively. It is inherently indiscriminate. Similar in this regard, but less

devastating is conventional, strategic bombing: dropping tons of explosive from the stratosphere has a greater

potential for indiscriminate destruction than direct small arms �re. No one has expressed this logic more

clearly than John Paul Vann:

This is a political war and it calls for discrimination in killing. The best weapon for killing wouldbe a knife, but I'm afraid we can't do it that way. The worst is an airplane. The next worst isartillery. Barring a knife, the best is a ri�e � you know who you're killing (Sheehan, 1988, 317).

Technological constraints are somewhat symmetrical for incumbent and insurgent. For a rural insurgent

movement, the available forms of violence might be ambushes and hit-and-run attacks within its area of

control, as well as terrorist attacks outside this area. Insurgents going global are rarely capable of assas-

sinating leading decision makers or military commanders. Instead, they resort to indiscriminate, terrorist

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methods such as using improvised explosives against civilians. A similar picture arises for the incumbent:

patrolling controlled and slightly contested areas in searching for insurgents is an option. Outside these areas,

the use of ground troops becomes increasingly costly. Aerial bombardment and the use of heavy arms on

the ground are the remaining options for the state. Aerial bombardment and international terrorism are

prototypical examples of indiscriminate violence and both tactics can be applied globally. Individual arrests

and targeted assassinations are prototypical examples of selective violence, but their application is restricted

to the controlled or contested areas. We can therefore assume that long-range attacks are �on average� less

discriminate than short range attacks and we would expect civilian alienation to be a function of distance as

well. Thereby, it is not necessarily the quantity of force that diminishes over distance as assumed by Boulding,

but its quality : arms branches with a low Loss of Strength Gradient tend to apply force indiscriminately. In

Vann's words, a knife should be the weapon of choice in a political war. But a knife combines the highest

accuracy with the shortest range. An airplane combines the lowest accuracy with the highest range. in

between the two, there are ri�es and artillery that follow the same trade-o�.

Cognitive accuracy also declines as a function of distance. As language and customs are known, the

identi�cation of loyalties is comparatively easy. Disagreement with the government can be inferred from

subtle cues. Sub-tones and connotations can give away collaborators. Clothing, decoration, and dialect allow

inferences on ethnic and religious ties. Knowledge of local history allows for a better understanding of local

grievances. These insights are part of modern counterinsurgency theory:

Learn about the people, topography, economy, history, religion, and culture of the area of opera-tions (AOs). Know every village, road, �eld, population group, tribal leader and ancient grievance.Become the expert on these topics (DOD, 2007, Appendix A, Paragraph A2).

This demand is reasonable in theory but increasingly di�cult to achieve in practice as distance to the own

cultural circle grows. Europeans and Americans in Algeria and Vietnam found a variety of ethnic, linguistic,

and cultural aspects that are mind boggling to the outsider and hard to generalize from. Many anthropologists

would �nd this task extremely challenging. Mental access to foreign regions is hard to acquire and hard to

communicate. Therefore, the cognitive ability to identify loyalties also diminishes as a function of distance.

The absence of cognitive access to local customs can provide incentives for brutal and indiscriminate behavior.

Incumbent violence in many counterinsurgencies involves torture as a means to acquire information.4 Third

party actors in counterinsurgency operations often struggle with the problem of understanding local societies.

4In an attempt to circumvent this problem, incumbent forces have sometimes tried to use local informants to acquire accurateinformation on political loyalties. This is problematic in that it creates opportunities for civilians to utilize military force forsettling private vendettas. The CIA-led �Phoenix Program� during the later stages of the Vietnam War relied on civilianinformants and su�ered from exactly this problem (MacPherson, 1984, 625).

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Figure 2: The �Loss of Accuracy� Gradient is graphically illustrated in this �gure. As distance to the powercenter increases, accuracy declines. This leads to an increase in resentment toward the user of force.

But even governments �ghting insurgencies within their own territorial boundaries face this problem. While

insurgents often mobilize local combatants for local operations, draft-based regular armies employ soldiers

usually in areas unknown to them.

Several empirical implications arise from this discussion. First, unlike Boulding's model, the presented

theory assumes that the quality (not primarily the quantity) of force is a function of distance in modern

warfare. As distance between the incumbent and the insurgent grows, the accuracy of their violent action

decreases. Compensating for the lack in accuracy with increased lethality only ampli�es the problem: more

innocent bystanders fall victim to the inaccurate use of force. Second, indiscriminate force usually creates

a backlash in popular opinions. The alienating e�ect of inaccurate violence vastly out-weights its deterrent

e�ect, simply because inaccuracy is not conditional on speci�c behavior: even supporters of the user of force

can fall victim to their measures. This logic is illustrated in Figure 2.

Revolutionary movements have sometimes tried to harness the backlash of indiscriminate violence into

bootstrapping insurgent movements: a state that can be bated into overreaction in �ghting a small guerrilla

army alienates the population and thereby helps the insurgents recruit combatants. Combined with an active

e�ort to build up a movement on the �y around a vanguard core, this mechanism forms the basis of Guevara's

�foco theory� of revolution.5

5Even the unsuccessful �Red Army Fraction� occasionally reechoed this logic in their writings, asserting that the West-Germanstate resorted to totalitarian measures in �ghting them, thereby alienating the population (RAF, 1982).

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Accounting for this systematic e�ect of declining accuracy and rising resentment o�ers a theoretical

solution to the unresolved questions mentioned above. This theory rejects limited state reach as a suitable

explanation for why insurgencies succeed. Instead, limited accuracy in the application of power is put

forward as an explanation. The assumption of economic self-interest seems to make false predictions about

increased risks leading to declining mobilization. Acknowledging the existence of non-material motives for

joining insurgencies resolves this problem. Victims of violence and their kin might simply seek revenge and

strive to �ght injustice. Translated into the terminology of the collective action problem, inaccurate force

provides revenge as a selective incentive to the victims and their kin. Such motives are elements of most

national liberation narratives from the German Landwehr to the Boston Tea Party. On an individual level,

reaction to violence might be an even stronger motivator than the prospects of economic reward. Reactive

mobilization as a systematic e�ect arising from the use of indiscriminate force is taken into account in this

theoretical extension. A remaining question is why counterinsurgency fails as a military doctrine in many

instances. The presented theory does assume that mostly static, geographic conditions account for the e�ect

of incumbent violence in insurgencies. This explains why insurgencies can still succeed even as the problem

of reactive mobilization is widely discussed. Having contrasted the Loss of Accuracy with both Kalyvas and

Boulding, it is time to simulated the proposed mechanism to further illustrate the theory and derive empirical

implications.

3.2 Agent-Based Model

The full set of proposed mechanisms is demonstrated in an Agent-Based Model. There are two speci�c reasons

for preferring this methodology over others in the context of this research. The proposed mechanism describes

a complex interaction between three political actors under varying geographic conditions. While Game Theory

would certainly be an elegant mathematical approach to coping with endogenous strategic interactions, it is

usually restricted to two player games. More critically, Game Theory is usually used in non-spatial model

that assume players decide on their moves under perfect information. Restricted information and spatial

interaction are core elements of the proposed theory. In accounting for those, a simulation approach is

preferred over analytic solutions.

3.2.1 Description

The stylized Agent-Based Model di�ers from attempts to model insurgencies more generally such as �Rescape�

(Bhavnani et al., 2008) and �RebeLand�(Cio�-Revilla and Rouleau, 2010). The mentioned works aim at

13

explaining how complex interactions of factors can bring about civil war. The model presented below is used

to explain the outcome of insurgencies based on the introduced theory. In its focus on reactive mobilization

the model is similar to Bennett (2008) simulation approach which focused on the early stages of insurgencies.

Bennett's model (like the analytic model by Ellsberg (1970)) introduced three types of agents and set out to

simulate casualties and mobilization as a function of violence. As such it has been an important guideline

for the model described below, while lacking the geographic focus so central to the presented theory.

The model distinguishes among three types of agents: insurgent forces, incumbent forces, and civilians.

Each civilian holds a value for government Legitimacy ∈ R{0, ..., 1}. This value determines if the civilians

remain neutral (0.2 - 0.8) or join one of the military actors. In every time step each military actor gets a

chance to use lethal force against another agent. Basis for this decision is the fraction of enemy agents in

the agent's neighborhood. Moreover, a local accuracy threshold for the military actors de�nes which fraction

of adversaries in their neighborhood is su�cient for triggering an attack. The decline in accuracy for the

military actors is expressed in two intersecting gradients. The slope of these gradients is de�ned by only

one global constant: the Territorial Balance. As theorized above, numerous factors might in�uence the

actors' ability to apply force accurately. However, the simulation model uses a stylized aggregate of these

cognitive, technological, and cultural factors. If this balance favors the insurgents, their accuracy declines

less as a function of distance from the upper right corner of the simulated space. Should the balance favor the

incumbent, however, the insurgents' accuracy declines rapidly. To control this essential TerritorialBalance

for the adversaries, the location of the accuracy parity at the 0.5 level can be de�ned as a fraction of the

maximal distance from the upper right corner of the simulated space.

In every time-step the military actors get a chance to attack. Should they choose to do so, an agent from

their neighborhood is removed at random. If this agent happens to be a civilian all other civilians in its

neighborhood are alienated from the user of force. Their value for government legitimacy is changed toward

to military opponent of the user of force. Civilians can also be assimilated by either side: If no violent

action takes place over a certain amount of time, civilians change their government legitimacy values toward

the average value in their neighborhood. This mechanism is intended to represent assimilation of civilians

towards the providers of security. All agents conduct a random walk across the simulated space to allow

for repeated interactions between the actors. The logic of the main loop of the simulation is expressed in

Algorithm 1.

14

Algorithm 1 Pseudo-code for the main loop of the Agent-Based Model

For all military agents{if (accuracy of location < fraction of civilians in the neighborhood) �> attackif (civilian hit) �> civilians in neighborhood alienated}

For all civilian agents{if (no attacked witnessed in the last k rounds)�> change legitimacy level toward average of the neighborhood}

For all agents{move one step in a random direction}

If (only military agents of one side left) �> stop

Figure 3: Screen-shot from the Agent-Based Model under varying Territorial Balance. Light gray areasindicate superior accuracy for the incumbent. The frontier of the colored areas indicates where the accuracyof the actors drops below 0.5, that is a 50% probability of hitting a civilian. The plot on the left hand sidealso shows a close-up of the simulation. Civilians are represented as smileys according to their happinesswith the government. Military agents are visible and represented as stick �gures.

3.2.2 Simulated Results

Figure 4 shows the results of the model runs for the risk parameters of insurgent and incumbent. For

each parameter combination, 20 runs were executed with varying random seeds. As the plot indicates, the

territorial balance crucially determines the outcome of the simulated insurgency. Several parameters that

15

potentially also in�uence the outcome were varied in the simulation runs. Most importantly, the initial

loyalties and the civilian solidarity constant were varied across runs. As Figure 4 (left) indicates, balance

settings below 0.4 and above 0.6 reliably bring about defeat or victory. Only settings in between see a

larger variance on the outcome. The Territorial Balance remains a decisive factor in Figure 4 (right) were

biased initial loyalties factor in as well although the outcomes become less certain. Generally, the introduced

territorial balance out-weights initial loyalties as determinants of outcome. The model therefore serves as an

initial demonstration of the proposed mechanism. One additional observation is that the winning side always

su�ers more losses than the losing one, directly contradicting the Vietnam-era focus on a high �body count�.

0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

1.0

Insurgent Victory as a Function of Territorial Balance

Territorial Balance

Pro

babi

lity

of In

surg

ent V

icto

ry

Average Outcome: Loyalties = 0.5 Solidarity = 0.5

95% Conf. Int.

0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

1.0

Insurgent Victory as a Function of Territorial Balance

Territorial Balance

Pro

babi

lity

of In

surg

ent V

icto

ry

Average Outcome: Loyalties = [0.4,0.6] Solidarity = [0.3,0.7]

95% Conf. Int.

Figure 4: The probability of insurgent victory as a function of the territorial balance in the agent-basedmodel. Each parameter combination was run 20 times with varying random seeds. The �balance� variablere�ects how much of the territory under more accurate control for either actor. A balance setting of 0.5indicates a neutral setting. Lower settings favor the insurgents, higher ones the incumbent. The lines aroundthe outcome probability indicate 95% con�dence intervals for all runs. Please note that all �controls� werehold constant for the simulation runs on the left hand side, leaving only varying random seeds to generatevariable outcomes. On the right, biased initial loyalties and various levels of civilian solidarity were tested,substantially widening the error bars. Such factors obviously in�uence the proposed mechanism and waterdown the e�ect of �territorial balance�.

16

3.3 Hypotheses

The main hypothesis of this study directly results from the theory and its simulation: Whoever exercised the

more accurate control over the bulk of the population is more likely to win the civil war. Since the center of

state power is usually associated with the capital city while the most remote areas of the country are easiest

to be controlled by the rebels, a more formal expression of this idea would be:

� H1: Average distance to the population from the capital has a positive e�ect on insurgent victory

Other conditions that a�ect accuracy have also been touched upon in the literature. Most importantly, John

Vann's concerns over the low degree of urbanization in South Vietnam has been picked up in modern con�ict

research (Kalyvas, 2006, 12). Data on the local population, a highly concentrated police force, and advanced

means of communication make urban environments easier to control for the state than vast countrysides.

More importantly, the state's ability to gather information and rely on informants allows them to single

out insurgent cells and detain enemy combatants without having to rely on large-scale destruction. At the

same time, insurgents are deprived of the ability to selectively attack incumbent forces. Usually, the cities as

centers of state control are also the sites of terrorist tactics employed by insurgent forces. And while delayed

explosions of concealed bombs allow terrorists to wage war without facing the superior forces of the state

directly in the cities, they also cause large scale su�ering of the civilian population and shift loyalties toward

the state. One would therefore expect the degree of urbanization to in�uence the outcome of insurgencies:

� H2a: The percentage of urban population negatively a�ects the probability of insurgent victory.

Along these lines, natural land cover can allow insurgents to retreat after hit-and-run attacks, or to build up

forces outside the state's reach. Open terrain should have the opposite e�ect: The state can exercise control

with fewer forces and more e�cient reconnaissance. The absence of accurate information might again create

incentives for the application of inaccurate violence and the resulting backlash in public opinion. Therefore,

natural land-cover should have an e�ect on the outcome of insurgencies:

� H2b: The percentage of population living in open terrain should have a negative e�ect on insurgent

victory

� H2c: The percentage of population living in forested terrain should have a positive e�ect on insurgent

victory

The section below presented empirical tests of these hypotheses followed by a general discussion.

17

4 Empirical Analysis

With Territorial Balance being the key explanatory concept, suitable geographic measurements have to be

de�ned to operationalize it. Drawing on a dataset by Lyall and Wilson III (2009) that provides information

on outcome of insurgent civil wars and many important control variables, this measurement can then be

tested in large-N. This section will give a detailed description of the corresponding indicators followed by a

discussion of the empirical results.

4.1 Territorial Balance Indicator

Both theory and model in the previous sections have put a large emphasis on distance as a crucial determinant

of accuracy in the application of violence. Several equi-�nal mechanisms seem to fuel the decay of accuracy

as a function of distance such as the range-accuracy trade-o� in modern weapons technology and the decline

of situation awareness in foreign human and physical geographies. Incumbent distance to the bulk of the

population therefore has to be operationalized to test for these proposed e�ects.

Data on the spatial distribution of population (CIESIN, 2005) and the location of capital cities and

international boundaries (Weidmann et al., 2010) were combined to form a single indicator for �Territorial

Balance�. Intuitively speaking, the Territorial Balance Indicator (TBI ) measures what fraction of the total

population the government has in reach for what fraction of the sum of all distances to its inhabited territory.

For any given distance D from the capital, the fraction of the population pd<Dwithin that distance can be

calculated and plotted, according to tbD =∑

pd<D∑p ,where p is the total population of a country and d the

distance from the capital to the most remote inhabited spot, normalized to [0, 1]. With growing distance tbD

values form a curve eventually connecting the points (0,0) and (1,1). Figure 4.1 shows the resulting curve for

the United States, based on the 1990 spatial distribution of population.

How can we interpret the exact shape of the curve in Figure 4.1? The curve simply expresses the fraction

of the population that can be reached for any given distance from Washington. At �rst the TBI curve climbs

rapidly as the densely populated east cost is covered. Around a cumulative population value of 0.5, the

curve �attens out as the distance covers the less densely populated Midwest. It �attens out even further as

the Rocky Mountains are reached and then suddenly spikes around a distance value of 0.7 as the densely

populated west coast is integrated. Finally, the sparsely populated wilderness of Alaska appears as an almost

�at section on the right end of the curve.

For the statistical analysis, the described indicator has to be collapsed into a single number. Analogous

18

Figure 5: TBI curve for the United States. The curve on the right describes what fraction of the Americanpopulation is within distance from Washington. The TBI expresses the area under the curve divided bythe whole area of the plot. With the United States' population balance in the east, the resulting TBI iscomparatively high. On the left, a visualization of the GPW dataset (CIESIN, 2005) for the region is visible.

to a ROC curve that plots the rate of true positive predictions against the rate of false positives in statistical

prediction, the area under the curve (AUC) is chosen as a suitable indicator. Formally: TBI = aa+b , where

a is the area under the curve and b the area above it. A caveat of this approach is that global data on

population numbers only dates back to the year 1990. Therefore, the post-WWII period that is frequently

in the focus of civil war research cannot be analyzed entirely based on this indicator.

4.2 Population and Accessibility

Apart from distance, other aspects of geography could in�uence accuracy in the application of force. To

capture these e�ects, a global land-cover dataset (Hansen et al., 2000) was acquired. This data distinguishes

among 12 types of terrain ranging from rain forest to open cropland and urban areas. To keep things simple,

land-cover values were divided into two categories: open and covered terrain. The high resolution land-cover

data was re-sampled to correspond to the roughly 5km² cell size of the population dataset. Each cell-centroid

in the population dataset was then associated with the nearest cell-centroid in the land-cover dataset. This

approach allows for calculating the fraction of the population that is either living in forested (covered) or

open terrain. Data on total population and the percentage of urban population was obtained from the the

United Nations Population Division. The great advantage of this data source over the described static GIS

19

approach is that it provides time-variant data. In the empirical analysis, these variables will be referred to

as �%URBAN�, �%OPEN�, and �%FOREST�.

4.3 Empirical Results

Possible outcomes in insurgent civil wars are incumbent victory (1), settlement of the hostilities without

decisive victory of either side (2), or insurgent victory (3). These outcomes can be ordered from victory

to conclusive defeat for either side. To preserve the ordinal information in the dependent variable, several

Ordered Logistic Regression Models were used to assess the relevance of the proposed variables. The time

period under investigation ranges from 1970 to 2010. The reason for limiting the time period to this range lies

in the static nature of the main independent variables. A population-related variables in their current form

rely on geo-referenced data that can only be obtained from 1990 onwards. Since these aggregate measure on

the country level are not subject to drastic annual changes, 1990± 20 years was chosen as a suitable time

frame for the analysis. Limiting the time-frame to 1990-2010 was also considered, but the resulting number

of observations would have been too small for substantial inferences.

Mounting methodological objections against a sole reliance on p-values (Ward et al., 2010, Schrodt, 2010)

require additional assessments of the predictive capabilities of newly introduced variables. Therefore, both in-

sample and out-of-sample predictive performance are included in the empirical analysis. Moreover, stepwise

variable selection allows for an identi�cation of the central variables.

To create a baseline for the statistical analysis, Model 1 replicates the base model from the Lyall and

Wilson III (2009) study. The main independent variable of their study, modern combat tactics, had to

be omitted in this analysis. Modern combat tactics according to Biddle's (2006) de�nition were generally

introduced after WWI. Therefore, there is simply no variance on this indicator for post-1970 insurgencies.

However, Lyall and Wilson III (2009) still provide a formidable point of departure both theoretically and

empirically. Model 2 includes all introduced variables, including the population-accessibility measurements.

In order to test the main hypothesis of my contribution, the distance facilitated loss of accuracy, the �Terri-

torial Balance Indicator� (TBI ) was added to the base model in Model 3. Model 4 includes the base model

plus two additional variables from the Lyall and Wilson III (2009) study, the degree of mechanization in the

state's armed forces (MECH.) and the availability of helicopters for the state (HELI.). The TBI value is still

highly signi�cant, even as one controls for these additional two variables. Model 5 contains a subset of the

variables in the full model which were chosen based on a step-wise model selection algorithm that minimizes

the AIC value.

20

Model 1 Model 2 Model 3 Model 4 Model 5(Intercept 1—2) −0.6256 −0.7973 −1.8755 −0.9357 −0.8381

(0.7063) (0.0453)∗∗∗(0.4922)∗∗∗(0.9113) (0.5842)(Intercept 2—3) 2.1735 2.25 0.9558 2.0938 2.1728

(0.7623)∗∗ (0.5168)∗∗∗(0.6181)∗ (1.0447)∗ (0.7841)∗

REGIME 0.1116 0.1263 0.1085 0.1307 0.1449(0.0464)∗ (0.0524)∗ (0.0462)∗ (0.0507)∗ (0.0477)∗

SUPPORT 0.7813 0.9748 0.8073 0.9785 1.0532(0.3422)∗ (0.3708)∗ (0.3427)∗ (0.3746)∗ (0.355)∗

POWER −0.2501 −0.−0.5731−0.276 −0.6208 −0.6233(0.1951) (0.2526) (0.1972) (0.2606)∗ (0.2569)∗

ENERGY −0.2807 −0.3408 −0.2645 −0.2718 −0.265(0.1818) (0.2183) (0.1825) (0.1913) (0.1849)

OCCUPATION 1.291 1.109 1.308 1.0513(1.0982) (1.3186) (1.1049) (0.0001)

ELEVATION −0.0001 −0.0001 −0.0001 0.0001(0.0037) (0.0004) (0.0004) (0.0001)

DISTANCE 0.0004 0.0005 0.0004 0.0005 0.0005(0.0004) (0.0004) (0.0004) (0.0003) (0.0004)

COLDWAR −1.387 −1.318 −1.384 −1.3645 −1.304(0.555)∗ (0.5827)∗ (0.5547)∗ (0.5866)∗ (0.556)∗

MECH. −0.0246 −0.0525(0.2929) (0.291)

HELI. 1.7541 1.8482 1.897(0.8848). (0.8536). (0.8173)∗

%URBAN 0.0083(0.0145)

log(POPULATION) 0.0074(0.302)

TBI −2.5398 −2.017 −2.2861 −2.3788(0.1788)∗∗∗(0.3298)∗∗∗(0.5562)∗∗∗(0.393)∗∗∗

%FOREST 0.0051(0.0137)

%OPEN −0.0077(0.0163)

N 64 64 64 64 64AIC 129.52 136.93 130.94 129.64 124.44logL −54.76 −51.47 −54.47 −51.82 −52.22Ord. deviation in-sample 33 25 26 27 29Ord. deviation out-of-sample 38 45 38 40 38Standard errors in parentheses

. indicates significance at p < 0.1; * at p < 0.05; ** at p < 0.01; *** at p < 0.001

Figure 6: Results from the empirical analysis. Five Ordered Logistic Regression Models were estimated toget a clear picture of the relevance of the proposed measurements.

21

4.3.1 Control Variables

As it is visible at �rst glance, several control variables are signi�cantly associated with the ordinal coding of

rebel success. Regime type (REGIME ) and outside support to the rebels (SUPPORT ) are signi�cantly and

positively associated with rebel victory in Models 1 to 3. The positive sign for the REGIME variable seems

to con�rm the wide spread suspicion that democratic societies are more likely to pull out of insurgent civil

wars due to their lower tolerance to casualties. Domestic peace movements, for example, can make themselves

heard and put signi�cant pressure on the incumbent in liberal and democratic societies. Autocratic systems

on the other hand can keep �ghting counterinsurgencies without having to take into account the interests of

a broad electorate.

It should be mentioned, however, that this �nding seems to be driven by the restricted sample size and

does not hold for the entire post-WWII period, as reported by Lyall and Wilson III (2009). Outside support

to the rebels is also reliably and positively associated with rebel victory. Many successful insurgencies greatly

bene�ted from outside support. Foreign �ghters willing to joining the uprising, material support, and assisting

intelligence services can obviously strengthen the rebel forces. The state capability indicator (POWER) does

not reach statistical signi�cance. It is nevertheless negatively associated with insurgent victory which again

meets the intuition that stronger states are better at �ghting o� domestic challengers. The logged energy

use of the incumbent as a share of the energy use of the whole population (ENERGY ) also has a negative

estimate, but fails to reach statistical signi�cance. An indicator for whether the country under investigation

is occupied (OCCUPATION) does not turn out signi�cant, but has a large positive coe�cient. Distance from

the counter-insurgent's home country to the host country of the investigation (DISTANCE ), and the mean

elevation of the country (ELEVATION ) do not appear to in�uence outcome. Clearly, distance should play a

role according to Boulding's classic LSG model.

A more reliable indicator is the cold war dummy (COLDWAR). In all four models, COLDWAR is nega-

tively associated with insurgent victory implying that the probability of insurgent success was signi�cantly

lower during the cold war. African countries that received military aid from the superpowers might drive

this result: the Democratic Republic of the Congo, Liberia, Rwanda all witnessed successful uprisings since

the end of the cold war. Mao's assumption that a large population basis is aids insurgent victory and John

Vann's concern that insurgent mobilization can take place easiest in rural societies were tested empirically.

Both the percentage of the population living in cities as well as the natural log of the total population seem

to have no signi�cant e�ect.

22

4.3.2 Main Independent Variable

The territorial balance indicator (TBI ) seems to be strongly and robustly associated with rebel victory. As

explained above, TBI values close to 1 indicate that the state has all the population in the vicinity of the

capital city. Values close to 0 correspond to situations were the state has to reach out into its most remote

corners to reach the bulk of the population. Therefore, the large, negative estimate and the small standard

error strongly support the theoretical expectation of this research. This result holds also in Model 2-5 as

control variables for land-cover, urbanization, and changing weapon's technology are introduced. The TBI

remains highly signi�cant with the expected negative sign throughout the analysis. The relevance of the

variable is also re�ected in increase in in-sample predictive capabilities. The Ordered Logistic Regression

can be used to predict probabilities for each possible outcome of a given war. Taking the maximum of these

probabilities as the model's prediction allows for a comparison of this prediction with the empirical record.

The absolute value of the mismatch between prediction and empirical record provides a simple distance

metric that still pays tribute to the ordinal scale of the dependent variable. This in-sample deviation can be

formalized as dev =∑ |Y − Y |. The TBI indicator lowers the predictive ordinal deviation from 33 in the

baseline Model 1 to 26 in Model 3. Apart from improving the model �t, the TBI also mildly re�ects the

stylized expectation from the Agent-Based model. Figure 7 on the following page shows predicted probabilities

for Model 3, assuming average values for the remaining explanatory variables. TBI values ranging from 0 to

1 have a profound e�ect on the predicted probabilities for insurgent victory.

A more thorough analysis of the predictive capabilities of the model drawing on the �Receiver Operating

Characteristic� and out-of-sample cross validation techniques will be presented below in the next section.

After that summary and discussion will conclude this study.

4.3.3 Predictive Performance

Inferential statistics provides great tools for testing whether or not a proposed variable is actually associated

with phenomenon of interest. Once a robust relationship is established, however, additional metrics are

necessary in order to �nd out how reliable the proposed e�ect really is. The question if and to what extend

we can rely on the TBI in order to predict the outcomes of insurgent civil wars cannot be answered based

t-statistics alone. One frequently used assessment of predictive performance is the ROC curve: a plot of the

rate of false positive predictions versus the true positives for varying probabilistic thresholds. The ROC is

an easily interpretable statistic allowing for an intuitive visual representation of model performances. The

23

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Predicted Probabilities for the Average Country (Model 3)

TBI Values

Pro

babi

lity

of In

surg

ent V

icto

ry

Figure 7: Predicted Probabilities for the average country from Model 3. Not that these probabilities resemblethe simulated results, although the e�ect of Territorial Balance is not as strong as the simulation suggests.The empirical model predicts a more shallow decline of the probabilities of insurgent victory. Moreover, atheoretical TBI of 0 would only raise probabilities of insurgent success to about 0.5 and not to 1 as thesimulation suggests.

baseline of all predictions is a straight 45° line from the lower left to the upper right of the plot. Such a line

could be produced on the basis of pure guesswork: For every positive case predicted correctly one negative

case would be predicted as positive. Since the ROC curve requires binary prediction techniques each possible

outcome has to be analyzed separately. Three ROC curves are represented in Figure 8 on page 26 each

corresponding to a possible outcome.

Apparently, �Rebel Victory� seems to be the easiest outcome to predict for all four models. Model 1

already predicts around 80% of these outcomes for a little more than 10% of false positive predictions. The

area under curve as a fraction of the total area (AUC) is possible numerical representation of the model

performance. The baseline model comes close to an AUC of 0.88. Model 3 includes the TBI indicator and

slightly increases the predictive performance in-sample. An AUC value of 0.89 and an increased performance

at the top of the curve can be attribute directly to this variable. Model 3 has the largest AUC value of all

�ve models. Model 4 preforms worst in in-sample prediction of insurgent victory.

The second outcome scenario, a peaceful settlement of insurgent con�icts without decisive defeat of either

24

side seems to be hardest to predict based on the introduced variables. The corresponding ROC curves drag

more or less along the random baseline. Model 2 including all variables performs best, although it only

achieves an AUC of 0.62. This overall humbling predictive performance might be due to the lack of relevant

indicators, such as UN intervention attempts or the existence of a legal political wings of the rebel movement

as discussed by Cunningham et al. (2009). The entire presented theory has been agnostic to the possibilities

and the determinants of outside interventions or negotiated settlements. The poor predictive performance of

inconclusive outcomes very much re�ects this fact.

A slightly better predictive performance of all models can be observed for the prediction of government

victory. The variables MECH. and HELI. in models 4 and 5 re�ect military capabilities of the incumbent

and naturally help to predict incumbent victory. It is again Model 3 that predicts best with an AUC of 0.77.

This overall performance of the models is better than for the case of a political settlement of the con�ict.

25

ROC Curve / Predicting Rebel Victory

False positive rate

True

pos

itive

rat

e

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Model 1: 0.87985

Model 2: 0.87985

Model 3: 0.89111

Model 4: 0.85982

Model 5: 0.86358

ROC Curve / Predicting Settlement

False positive rate

True

pos

itive

rat

e0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Model 1: 0.58725

Model 2: 0.62157

Model 3: 0.57157

Model 4: 0.59216

Model 5: 0.59216

ROC Curve / Predicting Government Victory

False positive rate

True

pos

itive

rat

e

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Model 1: 0.73792

Model 2: 0.82971

Model 3: 0.74517

Model 4: 0.80556

Model 5: 0.81884

Figure 8: ROC curves for the three possible outcomes.

The discussion of predictive performance has so far been restricted to within the sample. In order to

get a more realistic insight into the models' performance in out-of-sample predictions, leave-one-out cross

26

validation was also performed. The cross validation score was again calculated based on ordinal deviation as

introduced above. Cross validation simply chops the empirical sample into a training and a test set. Leave-

one-out cross validation �ts the statistical model on n-1 observation and then predicts observation n. This

process is repeated n times so that all observations are predicted once out-of-sample. The ordinal deviations

out-of-sample are consistently larger than those in-sample. While the TBI indicator does not worsen the

out-of-sample prediction, it also does not improve it in comparison to the base model. Interestingly, the full

model performs worst in the cross validation scheme. This might be due to the fact that additional variables

might very well decrease predictive success. Every estimated coe�cient is random variable in the frequentist

view. Every one of these estimates contributes random variability to the model predictions. Therefore,

a suitable metric is necessary that expresses the bene�t of adding suitable indicators against the costs of

adding random variability. Such a metric is the �Akaike Information Criterion� or AIC for short (Akaike,

1974). Intuitively speaking, the AIC rewards goodness of �t while punishing the inclusion of additional

variables. Lower AIC values indicate that a high goodness-of-�t was achieved based on only a small number

of explanatory variables. More formally, the AIC = 2k − 2ln(L), where k is the number of covariates and L

the maximum Likelihood. The regression table 6 on page 21 indicates the achieved out-of-sample scores and

the AIC values for each of these models. While out-of-sample tests do not belong to the standard procedures

in political science, they can help to put established �ndings into perspective. The TBI indicator that so

prominently dominates the regression table with its low p-value does not improve out-of-sample prediction

compared to the base model 1. In-sample prediction nevertheless bene�ts from the inclusion of this variable.

Model 5 that only includes signi�cant variables shows the lowest AIC value for all models. The additional

complexity and random variability that results from including multiple covariates is well balanced in this

model with increased goodness-of-�t. Model 3 including the TBI and the variables from the base model

preforms best in the prediction task with an ordinal deviation of 27 for the in-sample prediction and an

ordinal deviation of 38 for the out-of-sample case. In conclusion it is fair to say that the TBI is a good albeit

not perfect predictor for insurgent victory. The next section will sum up both the theoretical as well as the

empirical results and outline possible follow-up projects.

5 Discussion and Conclusion

The empirical analysis has con�rmed the theoretical expectation (Hypothesis 1) that capital-population dis-

tance is associated with outcome of insurgent civil wars. The Agent-Based Model has simulated a possible

27

micro-level explanation for this fact. Its central mechanism �reactive mobilization as a function of geograph-

ically determined accuracy� is derived from a broad theoretical literature and goes beyond a simple decline

of �repower as a function of distance.

The proposed e�ect of the Territorial Balance Indicator has been found in the empirical analysis and

shown to be a robust factor from both an inferential and predictive point of view. However, the Lyall and

Wilson III (2009) study has helped to put the proposed mechanism into perspective. Using their dataset

also allowed for the question of insurgent civil war outcomes to be treated in a cumulative manner, directly

building on established knowledge. In conclusion, instead of a strict geographic determinism that follows from

the theoretical discussion, a robust geographic tendency was found in the empirical record. Institutional and

military factors are also robustly related to outcomes in insurgent civil wars, especially to incumbent victory.

With regard to other possible geographic factors in�uencing accuracy, no conclusive support was found. The

percentage of the urban population is not signi�cantly related to outcome, thereby rejecting Hypothesis 2a.

Similarly, for Hypotheses 2b and 2c only small coe�cients were estimated, although they display the expected

signs. Possibly, over-aggregation to the country level and the lack of time variant information might be the

cause of this problem, but only future research can con�rm or reject this assumption.

Although the presented theory has passed these empirical tests, a number of problems need to be addressed

in the future. Most importantly, the gap between the individualist theoretical explanation and the country

level analysis needs to be �lled with more �ne grained data. Investigations on the level of ethnic groups

could provide this missing link. Con�ict events from recent insurgent civil wars could also be aggregated

to test for the proposed reactive patterns. Future Agent-Based Simulations could be adapted to work with

geo-referenced data and be compared more directly to the empirical record.

From a theoretical point of view, the study has generated a new synthesis between (counter-)insurgency

and con�ict literature and between geography and population as decisive factors in insurgent civil wars.

Boulding's (1962) classic distance-decay model has dominated the literature for decades. However, upon

close theoretical inspection, its proposed central mechanism fails to deliver a suitable explanation of how

insurgent civil wars end. A general decrease of the quantity of violence as a function of distance fails

to convince in the age of global strike capabilities and mechanized forces. Moreover, a sole focus on the

quantity of applicable violence falls short of explaining the problem of reactive mobilization in response to

indiscriminate violence. This study has proposed a new explanation involving a distance-decay of the quality

of violence. Highly selective violence deteriorates into largely indiscriminate violence as the distance to the

military actor's centers of control grows. A simulation model has helped to communicate this theory and

28

generated stylized insights. The Territorial Balance in the Agent-Based Simulation was operationalized as an

empirical indicator and successfully tested in large-N. If future research establishes the validity of the theory,

strong policy recommendations could be derived: a weak local incumbent, for example, might be far better at

beating an insurgency than a distant central government. Contrary to Boulding's implications, strengthening

this central government would not imply greater success on the battle�eld, but primarily more �erce resistance

and pointless bloodshed. Contrary to Kalyvas' (2006,12;118-132) expectations, civilian collaboration could be

strongly, but not entirely endogenous to military control. Instead, uncontrollable zones for either incumbent

or insurgent could emerge wherever geographic conditions prevent the actors from using force accurately,

leaving only homemade explosives or indiscriminate shelling as instruments of terror and fuel for resistance.

The expected deterrent e�ect of inaccurate violence that is central to classic air power theory might be

outweighed in many instances by reactive mobilization. If future research shows this to be the case, death

and destruction in�icted on an insurgent force should not be generally seen as military progress in �ghting

a rebel force, but simply as a actions that will trigger a violent reactions. According to this new perception,

a challenged state would have to face an insurgent force with the most even handed approach, defending

not primarily its territory or resources, but its reputation. This conclusion cannot be reached based on the

presented evidence alone, although it follows from the presented theoretical discussion. Future research will

have to complete the picture that has been outlined so far.

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