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The Geography of Conflict Diamonds: The Case of Sierra Leone


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In the early 1990s, Sierra Leone entered into nearly 10 years of civil war. The ease of accessibility to the country's diamonds is said to have provided the funding needed to sustain the insurgency over the years. According to Le Billon, the spatial dispersion of a resource is a major defining feature of a war. Using geographic information systems to create a realistic landscape and theory to ground agent behavior, an agent-based model is developed to explore Le Billon's claim. Different scenarios are explored as the diamond mines are made secure and the mining areas are moved from rural areas to the capital. It is found that unexpected consequences can come from minimally increasing security when the mining sites are in rural regions, potentially displacing conflict rather than removing it. On the other hand, minimal security may be sufficient to prevent conflict when resources are found in the city.
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The Geography of Conflict Diamonds:
The Case of Sierra Leone
Bianica Pires1(B
)and Andrew Crooks2
1Biocomplexity Institute of Virginia Tech, Arlington, VA 22203, USA
2Computational Social Science Program,
George Mason University, Fairfax, VA 22030, USA
Abstract. In the early 1990s, Sierra Leone entered into nearly 10years
of civil war. The ease of accessibility to the country’s diamonds is said
to have provided the funding needed to sustain the insurgency over the
years. According to Le Billon, the spatial dispersion of a resource is a
major defining feature of a war. Using geographic information systems
to create a realistic landscape and theory to ground agent behavior, an
agent-based model is developed to explore Le Billon’s claim. Different
scenarios are explored as the diamond mines are made secure and the
mining areas are moved from rural areas to the capital. It is found that
unexpected consequences can come from minimally increasing security
when the mining sites are in rural regions, potentially displacing conflict
rather than removing it. On the other hand, minimal security may be
sufficient to prevent conflict when resources are found in the city.
Keywords: Agent-based modeling ·Geographic information systems ·
Civil war ·Conflict
1 Introduction
In the early 1990s, Sierra Leone, a small country on the western coast of Africa,
entered into nearly 10years of civil war. Sparked by an abusive government and
fueled by an illicit diamond market, the decade-long war killed an estimated
70,000 and displaced another 2.6 million people [1]. It is said that the primary
driver of the war was the country’s most abundant and valued resource, dia-
monds [2]. While the resource has resulted in growth in other countries such as
Botswana, Sierra Leone has experienced some of the highest levels of poverty
in the world. Unlike the diamond mines of Botswana, however, the alluvial dia-
mond mines of Sierra Leone cover widespread areas in remote parts of the country
where mining areas cannot be easily fenced and security is minimal [3].
Le Billon [4] argued that the spatial dispersion of a resource is a major defin-
ing feature of a war, impacting the type of conflict that may emerge. An agent-
based model (ABM) was developed of the resource-driven conflict to explore
Springer International Publishing Switzerland 2016
K.S. Xu et al. (Eds.): SBP-BRiMS 2016, LNCS 9708, pp. 335–345, 2016.
DOI: 10.1007/978-3-319-39931-7 32
336 B. Pires and A. Crooks
Le Billon’s [4] theory. Some of the earliest ABMs of rebellion include Axelrod’s [7]
model of new political actors and Epstein’s [8] civil violence model. More recent
ABM’s have explored in-group dynamics and ethnic salience (e.g., [911]). While
the ABM presented here shares similarities with prior ABMs that have explored
income, resources, and identity as drivers of conflict, it also introduces some key
differences. Utilizing geographic information systems (GIS) and socioeconomic
data of the country, a landscape and population that better represent the actual
setting being modeled is created while the behavior of agents draws from theory.
Different scenarios are run as the diamond mines are made more secure and the
mining areas are moved to the capital. It is found that unexpected consequences
can come from minimally increasing security over the diamond mines in rural
regions. For instance, while minimal increases in government control stopped
rebel activity in the south, it displaced the conflict to a district in the north,
which had not seen violence in prior runs of the model.
2 Background
Theorists have pointed to opportunity, along with motivation and group identity,
as indicators of war (e.g., [5,6]). Opportunity can come in the form of financing,
the availability of recruits, and the ability to garner these resources with relative
ease, which can be due to factors such as geography, economics, and availabil-
ity. Others have focused on the financing of war through “lootable” resources
(e.g., [6]). Le Billon [4], while agreeing that lootable resources are a factor in con-
flict, argues further that the spatial dispersion of a resource is a major defining
feature of a war, impacting the type and duration of rebellion.
According to Le Billon [4], conflict characteristics are affected by two geo-
graphic factors: (1) the location of resources as they relate to the country’s center
(proximate versus distant) and (2) the concentration of resources (point versus
diffuse). Distant resources (i.e., in remote areas) are easier for rebel forces to
capture and control, while proximate resources are easier to secure and are less
likely to be captured (e.g., coffee). Diffuse resources are widespread over large
geographic areas, making the resource more difficult to secure (e.g., alluvial dia-
monds). Point resources, however, are concentrated in small geographic areas
and typically require mechanized extraction (e.g., kimberlite diamonds) making
them easier to secure and less likely to be exploited [4]. Assuming an environ-
ment that is ripe for conflict, the geographic features of a resource can influence
the type of conflict. This relationship is illustrated in Table 1.
3 Model Development
An ABM was developed in MASON [12] to explore the role of geography in the
resource-driven war. GIS data was utilized to create the modeling landscape,
while socioeconomic data provided initial agent attributes. Due to the localized
nature of social processes, including civil violence, ABM combined with GIS is
ideal for modeling the unique environment of Sierra Leone and the long-lasting
The Geography of Conflict Diamonds: The Case of Sierra Leone 337
Table 1. The relationship between the resource dispersion and conflict type [4].
Concentration/Relation to center Diffuse Widely spread with
minimal control
Point Concentrated
in small areas
Distant Warlordism Secession
Located in remote territories
Proximate Rioting / mass rebellion State control or coup
Close to center of power
conflict it endured. For brevity, a high-level overview of the model is presented
here. The detailed model description using the Overview, Design Concepts, and
Details (ODD) protocol [13], the source code, and data to run the model can
be downloaded from The model’s ini-
tialization process is discussed in Sect. 3.1; the agents’ behavior is discussed in
Sect. 3.2; and the model’s outputs are reviewed in Sect. 3.3.
3.1 Model Initialization
The modeling world encompasses the country of Sierra Leone, an area of approx-
imately 71,740 km2. Each run of the simulation begins by reading in the spatial
dataset and building the environment using data from the Global Administra-
tive Areas database [14], the Oak Ridge National Laboratory [15], the Peace
Research Institute Oslo [16], and OpenStreetMap [17]. The agent population is
created using data from the Republic of Sierra Leone 1985 and 2004 Population
and Household Census [19] and the Oak Ridge National Laboratory [15], while
socioeconomic data, which provided information on age distribution, income
levels, and employment statistics, came from the Republic of Sierra Leone 2004
Population and Housing Census [22,23] and Statistics Sierra Leone’s Annual
Statistical Digest [18]. Due to the computational constraints of modeling the
complete population of Sierra Leone (approximately 4.9 million), the population
within each parcel is reclassified to equal one percent of the total population.
Model runs performed at varying populations yielded similar qualitative results.
Note that households are not explicitly modeled here. The idea of a household is
used only to ensure that agents can be assigned an income even if unemployed.
Table 2summarizes the input parameters used in the model.
The model proceeds in one-month increments. While the decision to join the
rebellion may occur in a short time period (hours or even minutes), there is
a lag of weeks or even months between the time someone makes that decision
(or is forced to make that decision) and the time they are actually ready for
combat [24]. In addition, the war lasted years. From a modeling perspective, we
are interested in capturing the dynamics of the conflict over the years, not days
or hours. We also need to consider the balance between spatial and temporal
computational resources. The modeling world is the entire country of Sierra
Leone and simulates the dynamics of a war as it spreads a country.
338 B. Pires and A. Crooks
Table 2. Input parameters and variables.
Parameter Range Default value Reference
Initial number of agents 1–4.9 million 49,000 [15,19]
Percentage of population in the initial
opposition group
0–1 0.005 [2]
Age Grouped in age ranges 0–6, 7–17 18–64 [23]
Income level 1–3 1–3 [18]
Empl oyment st atus 1–4 1–4 [22]
Vision 0–370 25 Authors estimation
Likelihood to mine if food poor 0–1 0.01 [5,6]
Likelihood to mine if total poor 0–1 0.05 [5,6]
Rebel threshold if adult and not a
0–1 0.1 [5,6]
Rebel threshold if adult miner 0–1 0.01 [5,6]
Rebel threshold if minor 0–1 0.01 [5,6]
Distance to diamond mines 0–1 0–1 [14,16]
Remoteness 0–1 0–1 [17,25]
Government control over mines 0–1 0 [26]
Maximum parcel risk 0–1 0–1 Authors estimation
3.2 Agent Behavior
The PECS (Physical conditions, Emotional state, Cognitive capabilities, and
Social status) framework is a cognitive architecture that provides a flexible
framework to model human behavior [20]. Using PECS to implement agent
behavior, Fig. 1provides details on the specific motives (i.e., needs) and the
set of potential actions available to the agent. The Intensity Analyzer is respon-
sible for determining the action-guiding motive from the set of possible motives.
Two sub-models discussed here – the Needs Model and the Opportunity Model
– are incorporated into the Intensity Analyzer to determine agent behavior.1
The Needs Model. As illustrated in Fig. 1, agents can have three motives:
(1) the need for basic necessities such as food and shelter, (2) the need for
security of employment, housing, and financials, and (3) the need to main-
tain the home. These motives represent the two most fundamental levels from
Maslow’s [21] hierarchy of needs.2Agents meet these needs through a house-
hold income, whether from employment in the formal market, employment in
the illicit diamond market, or employment of a “household” member. While
1A third sub-model, the Identity Model, activates the identity of the agent based on
the outcome of the Needs and Opportunity Models. A detailed description of this
sub-model is provided in the ODD, which can be downloaded from https://www.
2While the Needs Model is responsible for determining the agents’ motive, and as
such, could be called the “Motives Model”, it was instead named after the humanistic
needs theory for which it draws from to highlight the application of theory.
The Geography of Conflict Diamonds: The Case of Sierra Leone 339
Set of possible motives
(1) Need for basic necessities,
such as food, water, and
(2) Need for security of
employment, shelter, and
Set of possible actions
(2) Get employment as
Diamond Miner
(3) Remain employed with
Other Employers
(1) Rebel
(3) Need to maintain
household (4) Stay home
Fig. 1. Motives and actions via the Intensity Analyzer (adapted from [20]).
the Needs Model determines the action-guiding motive, the Opportunity Model
helps determine the final goal and, subsequently, the final action the agent will
The Opportunity Model. Drawing from opportunity-based theories, which
have stressed such factors as the accessibility to resources, the geographic concen-
tration of rebels, and economic factors, agents in the model require opportunity
to join the illicit mining industry or to rebel. The first factor of opportunity is
the accessibility to resources. This is driven by three criteria: the presence of
diamond mines, the remoteness of the area, and the level of government con-
trol (or security) surrounding the resource [4]. The second factor is economic in
nature. We use a simple likelihood to mine variable to model this, where the
lower income brackets are the most vulnerable to joining the conflict. The final
factor is the concentration of rebels within an agent’s “vision”. The more geo-
graphically concentrated the rebels, the easier it is to overcome challenges of
collective action and to mobilize. In the model, if the first two factors of oppor-
tunity are met, then there exists the opportunity to mine in the illicit market.
If the third factor is also met, then there exists the opportunity to rebel. In the
case of those agents forced to rebel, however, economic factors are not consid-
ered, as these cases were largely children abducted and violently coerced to join
the conflict [1].
The Action Sequence. An agent can perform one of three activities at each
time step: mine, rebel, or do nothing. If an agent stays home or works in the
formal market, the agent will do nothing (agents “going to work” is not explicitly
modeled). If the agent joins the illicit diamond market, that agent will leave its
current employer and will join the diamond mining industry. If the agent’s income
level was zero, it is increased to one. An agent who becomes a rebel, on the other
hand, does not work for any employer, as the agent is either being forced to rebel
or is seeking to take control of a mining area for purposes beyond that of the
average independent miner.
Agents that are miners or rebels will move on the modeling landscape. These
agents need to be near the diamond mines, but at the same time it is assumed
340 B. Pires and A. Crooks
that they will want to move to a location that will minimize its potential level
of risk as much as possible. Utilizing cost surfaces developed to create the initial
landscape, an agent will move to a parcel within its vision that is closer to
the diamond mines but more remote than its current location. The agent will
continue to move until it cannot find any parcel within its vision that would be
better (i.e., closer to the mines and more remote) than its current location.
3.3 Model Output
The model exports a set of comparative statistics, including the number of agents
by a set of labor attributes and income levels. Statistics are collected at the
district-level by time step so that changes in the conflict’s dynamics can be
assessed across time and geographic location. The spatial dynamics of the conflict
as it evolves across time are observed through the interface during model runs.
4 Simulation Results
This section describes the model results. First, sensitivity testing was performed
to ensure the model was working as intended and to establish qualitative agree-
ment of model results to empirical data of the conflict. To determine initial
default parameter values, the model was calibrated by adjusting parameter set-
tings and selecting values based on observed visual results that most closely
replicated the actual conflict from a qualitative perspective. Figure 2shows
average intensity levels of rebel activity. Because the model does not simulate
events, intensity here is a function of that proportion of the total population
that rebelled.
Fig. 2. A visual comparison of model results to actual events. a: Average model results
using default parameter values. b: Actual event intensity [26].
Next, two experiments were performed to explore Le Billon’s [4] theory.
As discussed in Sect. 2, Le Billon [4] examined four types of conflicts and the
environmental factors required for each to emerge. To explore this theory, two
experiments were performed: (1) an experiment where resources are distant
The Geography of Conflict Diamonds: The Case of Sierra Leone 341
and government control (i.e., security) is varied, and (2) an experiment where
resources are moved closer to the country’s center and government control is var-
ied (all other parameter values are set to the default values shown in Table 2).
The Impact of the Spatial Dispersion of a Resource on Conflict Type
When Resources are Distant. To explore the potential impact on a con-
flict between having distant, diffuse resources, which is associated with conflicts
of warlordism, and distant, point resources, which is associated with secession
attempts, government control is varied in increments of 0.05 and the diamond
mines, whose relation to the “center” of the country is already distant, are main-
tained at their current locations. Government control of zero represents the min-
imum securities typically found with diffuse resources while government control
of one represents the increased security over point resources.
Figure 3illustrates the spatial dynamics of rebel intensity as government
control is increased. Results shown are the average rebel intensity during year
10 of the conflict. Figure 3a–b show that at lower levels of government con-
trol, the resulting violence was widespread with some regions experiencing very
high levels of rebel activity. In this case, the resulting spatial dynamics of the
violence was similar to the actual areas where conflict was the most intense.
Because of the geographic similarities between the real-world case of warlordism
in Sierra Leone and model results, the model output supports the theory that dis-
tant, diffuse resources are associated with conflicts of warlordism. As expected,
Fig. 3c–d show that with increasing government control, the intensity of rebels
and the geographic spread of the violence decreased. There are a few unex-
pected results, however. For instance, while minimal increases in government
control were enough at times to stop rebel activity in the south, the conflict
looked to be displaced to a district in the north. As government control was
increased systematically to simulate a resource situation going from diffuse to
point, rebellion occurred in smaller, more contained areas, often on the bound-
aries of the country. Given these spatial dynamics and the unique geographical
location and size of the conflict, a situation of secession may be feasible.
The Impact of the Spatial Dispersion of a Resource on Conflict Type
When Resources are Proximate. Freetown is the country’s capital, most
populated city, and main financial center [27]. Freetown can thus be considered
the “center” of Sierra Leone. In this second experiment, the diamond mines are
moved to Freetown and results are observed as government control is varied from
zero to one at increments of 0.05. Figure 4shows the spatial dynamics of rebel
intensity as government control is increased. Results shown are the average rebel
intensity during year 10 of the conflict.
Environments with proximate, diffuse resources are associated with conflicts
of mass rebellion or riots near the center of power. When government control
is low, this experiment seeks to simulate this environment, as shown in Fig. 4a.
While rebel activity emerged in the model, it was largely contained to the capi-
tal and its surrounding areas. Although the resources were placed in Freetown,
342 B. Pires and A. Crooks
Fig. 3. Average model results in year 10 when resources are distant. a: Government
control is 0.0. b: Government control is 0.2. c: Government control is 0.4. d: Government
control is 0.6.
Fig. 4. Average model results in year 10 when resources are proximate. a: Govern-
ment control is 0.0. b: Government control is 0.25. c: Government control is 0.35.
d: Government control is 0.45.
The Geography of Conflict Diamonds: The Case of Sierra Leone 343
which is located in the district of Western Area Urban, its neighboring district
(Western Area Rural) actually experienced higher levels of rebel activity (see
Fig. 4a–b). Given the geographic location of rebel activity and the spread of the
violence in the model at low levels of government control, these results provide
support to the idea that diffuse, proximate resources are associated with rebel-
lion. From Fig. 4, we find that only minimal increases in government control are
required to rapidly drop the intensity of rebel activity, supporting the idea that
proximate resources are easier for the government to control. As government
control was maximized, an environment with proximate, point resources is mod-
eled, as shown in Fig. 4c–d. These types of resources are associated with conflicts
of state control or coups. A coup would occur in the country’s center of political
power, however, at relatively low government control levels (0.25 and above), no
rebel activity ensues in the capital. Thus, we cannot support or reject the notion
that proximate, point resources are associated with coups.
5 Conclusion
Since diamonds were discovered in Sierra Leone, the government has been unable
to control the activity and provide residents with the benefits of having the
resource [27]. Through the interplay of ABM and GIS, the model presented
explores Le Billon’s [4] theory and the impact that the unique environmental
and socioeconomic attributes of a region and its population can have on the
onset of conflict. The resulting intensity and spatial characteristics of conflict in
the model provided support to Le Billon’s [4] theory that the spatial dispersion
of a resource can lead to warlordism, secession, and mass rebellion. However, the
model did not implement the necessary detail to support Le Billon’s [4] claim
that proximate, point resources lead to a coup. Furthermore, in future work,
agent movement could be empirically calibrated to the displacement levels of the
population. Nevertheless, by applying simple behavior we were able to explore
theory and test “what if” scenarios. When an environment is ripe for conflict, this
type of model could potentially provide insight into the locations most prone to
conflict and the characteristics of a conflict. Different conflict types may require
unique intervention strategies [28], an important consideration for policy.
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Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.
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While territory, oil, and water are frequently mentioned as resources likely to promote interstate conflict, diamonds have emerged as a prominent factor in explanations of civil war. In this article, the authors report on a new database on diamond deposits and production and analyze the relationship between diamonds and armed conflict incidence. They find a strong bivariate relationship between diamonds (particularly secondary diamonds) and the onset of civil war. Adding diamond dummies to standard models of civil war, the results are more mixed. The production of secondary diamonds increases the risk of onset of ethnic war, but not other types of war. The authors find evidence that secondary diamonds are positively related to the incidence of civil war, especially in countries divided along ethnic lines. Primary diamonds, on the other hand, make ethnic war onset and incidence less likely. The authors also find that the impact of diamonds has been substantially stronger in the post-cold war era.
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This article examines how the relationship between ethnic polarization and civil war could be moderated by different degrees of ethnic salience. Using an agent-based computational model, we analyze the polarization—conflict relationship when ethnic salience is ``fixed''—high for every member of two nominally rival ethnic groups— and ``variable''—permitted to vary across individuals as a function of relative income. We find that (1) when salience is fixed, conflict onset is twice as high at low levels of polarization compared to when salience is permitted to vary, with the difference decreasing at high levels of polarization; (2) the relationship between conflict onset and the range over which we calculate variable salience is positive and robust for low and moderate levels of polarization; (3) the relationship between polarization and conflict onset is robust even under minority domination, if one holds salience fixed; and (4) holding ethnic salience fixed effectively amplifies the negative effect of polarization on economic performance.
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Using an agent-based computational framework designed to explore the incidence of conflict between two nominally rival ethnic groups, we demonstrate that the impact of ethnic minority rule on civil war onset could be more nuanced than posited in the literature. By testing the effects of three key moderating variables on ethnic minority rule, our analysis demonstrates that: (i) when ethnicity is assumed to be salient for all individuals, conflict onset increases with size of the minority in power, although when salience is permitted to vary, onset decreases as minority and majority approach parity; (ii) fiscal policy—the spending and investment decisions of the minority EGIP—moderates conflict; conflict decreases when leaders make sound decisions, increases under corrupt regimes, and peaks under ethno-nationalist regimes that place a premium on territorial conquest; and lastly (iii) natural resources—their type and distribution—affect the level of conflict which is lowest in agrarian economies, higher in the presence of lootable resources, and still higher when lootable resource are “diffuse”. Our analysis generates a set of propositions to be tested empirically, subject to data availability.
A generous endowment of natural resources should favour rapid economic and social development. The experience of countries like Angola and Iraq, however, suggests that resource wealth often proves a curse rather than a blessing. Billions of dollars from resource exploitation benefit repressive regimes and rebel groups, at a massive cost for local populations. This Adelphi Paper analyses the economic and political vulnerability of resource-dependent countries; assesses how resources influence the likelihood and course of conflicts; and discusses current initiatives to improve resource governance in the interest of peace. it concludes that long-term stability in resource-exporting regions will depend on their developmental outcomes, and calls for a broad reform agenda prioritising the basic needs and security of local populations.
MASON is a fast, easily extensible, discrete-event multi-agent simulation toolkit in Java, designed to serve as the basis for a wide range of multi-agent simulation tasks ranging from swarm robotics to machine learning to social complexity environments. MASON carefully delineates between model and visualization, allowing models to be dynamically detached from or attached to visualizers, and to change platforms mid-run. This paper describes the MASON system, its motivation, and its basic architectural design. It then compares MASON to related multi-agent libraries in the public domain, and discusses six applications of the system built over the past year which suggest its breadth of utility.
Representing human decisions is of fundamental importance in agent-based models. However, the rationale for choosing a particular human decision model is often not sufficiently empirically or theoretically substantiated in the model documentation. Furthermore, it is difficult to compare models because the model descriptions are often incomplete, not transparent and difficult to understand. Therefore, we expand and refine the 'ODD' (Overview, Design Concepts and Details) protocol to establish a standard for describing ABMs that includes human decision-making (ODD + D). Because the ODD protocol originates mainly from an ecological perspective, some adaptations are necessary to better capture human decision-making. We extended and rearranged the design concepts and related guiding questions to differentiate and describe decision-making, adaptation and learning of the agents in a comprehensive and clearly structured way. The ODD + D protocol also incorporates a section on 'Theoretical and Empirical Background' to encourage model designs and model assumptions that are more closely related to theory. The application of the ODD + D protocol is illustrated with a description of a social-ecological ABM on water use. Although the ODD + D protocol was developed on the basis of example implementations within the socio-ecological scientific community, we believe that the ODD + D protocol may prove helpful for describing ABMs in general when human decisions are included.
In recent years autonomous agents have significantly gained in importance for the modelling of human behaviour. With this development a need for adequate design methodologies for complex agents emerges which are capable of modelling essential characteristics of human beings in connection with their decision making and behaviour control. In this article the PECS reference model for the construction of human- like agents is introduced which enables an integrative modelling of physical, emotional, cognitive and social influences within a component-oriented agent architecture. Furthermore the case study Learning Group is introduced which demonstrates the practical application of the design methodology and is intended to model basic social psychological mechanisms in the context of group formation and disbandment.
Natural resources, and diamonds especially, are commonly believed to play a significant role in the onset and duration of armed civil conflict. Although there is ample case study evidence that diamonds and similar resources have been used by rebel groups to finance fighting, there are few systematic empirical studies assessing the role of lootable resources in civil conflict. This is largely due to lack of reliable data on production and location. In this article we discuss priorities for the collection of data on conflict-relevant resources and introduce a new dataset, DIADATA, that provides a comprehensive list of diamond deposits accompanied by geographic coordinates throughout the world. The dataset includes characteristics relevant to conflict such as production status and geological form of the deposit. Particularly important is the distinction between primary and secondary diamonds, because the latter are more easily lootable. The dataset incorporates a spatial as well as a temporal dimension.