The Evolution of Identity Structures: Modeling Kinship in Pakistan's ...

The Evolution of Identity Structures: ModelingKinship in Pakistan’s Rural North-West

Frontier Province

Armando GellerGeorge Mason Univ.

[email protected]

Joseph F. HarrisonGeorge Mason Univ.

[email protected]

Matthew RevelleGeorge Mason Univ.

[email protected]

June 5, 2010

AbstractKinship is an essential factor in the study of armed conflict. A better understandingof kinship mechanisms and related behavioral and social patterns can contribute toa better understanding of armed conflict. We introduce an empirical computationalsocial science framework to explore basal mechanisms in the production of identityand the evolution of friend-or-foe relationships. An agent-based social simulationmodel is developed based on empirical data taken from an existing case-study of theSwat Valley in Pakistan. The modeling proposed has generalizable demonstratorvalue in that it is shown how published qualitative data can be used in buildingcredible and cross-validateable formal models that then can be re-used in modularmodeling exercises. We produce insight that shows that the social structure itselfshould be assumed an endogenous source of volatility in armed conflict and thatinstead of looking only at the emerging structures, the mechanisms that lead to thealliance and friend-or-foe patterns bear importance in their own right.

1 IntroductionKinship is an essential factor in the study of armed conflict. Knowledge of kin-ship mechanisms and patterns contributes to a better understanding of conflict ingeneral. We introduce a computational social science framework to explore basalmechanisms in the production of identity and the evolution of friend-or-foe rela-tionships.1 These mechanisms are lineage and affinity. Through their study we gainfundamental insight into the formation of loyalty groups—and antagonist groups.Violence between villages, for example, varies according to the genealogical dis-tance between these villages (Lindholm, 1981, 152).

We focus on two research questions. First, how can we dynamically modelsocio-demographic and marriage interactions using simple rulesets, and how doesidentity emerge from these interactions? Second, how does computational model-ing of kinship patterns inform our reasoning about friend-or-foe categorization inarmed conflict?

Tapper (1991, 46) names as important social organizational principles patri-lineality, affinity, and residential propinquity. We focus on the first two, using for itthe notion of kinship.2 Kinship is a defining factor in social life, including armedconflict. The kinship group defines solidarity (Evans-Pritchard, 1929, Malinowski,1930) and is a fundamental social structure, deriving its significance from a com-mon progenitor. But kinship is more than mere genetic relationships. Functionaland emotional relationships make it an inherently extendable concept that highlydepends on the local culture (Read, 1998).

Recent research on conflict underlines the importance of using qualitativedata (Sambanis, 2004) and the role the local fabric plays in conflict (Lubkemann,2005). A similar trend can be observed in statistical analyses of conflict, wheredisaggregated datasets afford testing hypotheses in a more nuanced fashion (Ce-derman, Girardin, and Gleditsch, 2009). We extend this research by adopting adynamic view through exploring the social mechanisms that underlie the reportedrelations and correlations. Understanding political structure implies developing anunderstanding for how these structures change, what processes are related to thesechanges, and what causes the changes (cf. Fischer 1994, Lindholm 1979). Becausewe agree with (Shapiro, 2005) that research should be problem-driven, we start with

1The research presented is part of a larger effort to build a realistic social landscape to understandattitudes and allegiances in irregular warfare. For the purpose of this paper we understand identityas being present in multiples in a person, related to the multiple roles that individual incorporates.This is crosscutted by the “person”, the socially defined aspects of the self, and the “individual, theuniquely experienced side of self. Identity is constructed of all these things.

2It has not escaped our attention that kinship is a much broader concept than commonly thought.

real data—what (Wendt, 2009) calls “events”—when thinking about structures anddynamics.

For the purpose of this research we focus on the Swat Pashtun in Pak-istan. Extensive documentation of their culture and kinship system is ideal forour purposes, though any other equally well documented case would work. Re-search comparable to ours was conducted for example by Read (1998) and Fischer(1994). Read (1998) studied the effects of culturally mediated decision making onemerging social structure by means of social simulation. Fischer (1994) created anobject-oriented social simulation and examined the ideal formulation of arrangingmarriage in Pakistan and the related constraints. He was interested in the behaviorof a social system given a set of social norms. We expand this kind of research intothe sphere of political culture and complexity.

An agent-based social simulation model is introduced that is founded on em-pirical data taken from a case-study of the Pakistani Swat Valley. The representationof genealogies through computer assisted methodologies is helpful in “illustratingand elucidating points of social organization, terminology, and wider relations ofkinship and society.” (Fischer, 1994, 80-81) The model’s main purpose is to evolve,describe, analyze and understand identity structures in a computational frameworkbased on a common understanding of kinship mechanisms as reported in a casestudy. The structures are generated using empirical, that is ethnographic descrip-tions of social behavior (cf. Janssen and Ostrom 2006). This approach is inverseto social network analysis, where patterns in a given structure are identified. Themodeling proposed has a generalizable demonstrator value in that it is shown howpublished qualitative data can be used in building a credible and cross-validateableformal model that then can be re-used in modular modeling exercises. We expectour research also to have anticipatory value through the systematic analytic descrip-tion of a social order and thus the postulation of a pattern that has in itself predictivepower.

Section 2 provides a short overview of relevant research. Section 3 describesthe elaboration of the agent behavior rulesets. The model is discussed in Section4, and cross-validation and output is reported in Section 5. Section 6 provides adiscussion of the results and situates them in extant research.

2 Kinship, computational models, and armed con-flict

Many aspects of kinship have been studied in anthropology, such as its role in sociallife, the kinds of social needs it satisfies, and the influence it exerts on society (e.g.

Malinowski 1930). Kinship systems describe a basal social structure, namely theone given by blood lines. The initial situation given by father, mother, and child isdefining for later life (Malinowski, 1930). Evans-Pritchard (1929, 7) notes: “Thesolidarity of the sibling group is shown in the first instance in the social relationsbetween its members.”

However, kinship structures represent more than mere lineage systems. Whilekinship indeed often defines the elementary family, it also provides a basis for so-cial structures (e.g. extended family, local group, clan, and tribe), functional re-lationships (e.g. marriage), and basic mechanisms such as affinity and emotions(Malinowski, 1930, Radcliffe-Brown, 1941). Read (2001, 114) rightly points outthat kinship’s “true” nature lies somewhere in between: “Once we recognize thatgenealogical tracing and the terminological space can both be culturally salient, yetneed not be congruent . . . there is no conflict . . . .” Critiques of kinship studies suchas Schneider (1984) and Kuper (1982) take it from here when noting that kinship isa rather fluid and constructed kind of thing. People may perceive a kin relationship,a common critique runs, where there actually exists no genetic relationship. Kinshipstudies have been also critiqued as being too absolute in assuming that identifyingsuch things as kinship, marriage patterns, residence rules, etc. will explain soci-eties’ intricacies. The newer literature, for example Sneath (2007), often discreditsin a post-modernist, constructivist fashion the older one by highlighting the impor-tance of overlapping relations within a group of people, based on gender, role, etc.,and the individual as actively constructing relationships. What Barth (1965) (seebelow) on the one hand identifies as tribes is interpreted on the other hand by thenewer literature such as Sneath (2007) as aristocratic lineages.

Social identity, its range within an aggregation of individuals, and how anindividual is placed in this aggregation depends on social constructions of kinship(Read, 2001). Kinship structures, processes, and mechanisms thus understood alsoplay a vital role in political culture. We understand political culture as a repertoireof cognitive, behavioral, and normative patterns in the production of power as wellas in the organization of groups for the pursuit of cooperation and conflict. Fromsuch a viewpoint kinship gains a new, almost entrepreneurial connotation.

Kinship is extensively studied in relation to armed conflict. Snyder (2003)reports in a survey article on anthropology and war that the essential criterionfor group cohesion and group establishment should not be territorial, but that thedecision-making autonomy lies at the level of unit identity. Kinship structures af-fect the creation of fighting alliances and increased external kinship ties increasethe likelihood of war. In other words, war patterns depend on types of kinshipsystems (Ferguson, 1990). Statistical work that would support this view, however,are inconclusive. Fearon and Laitin (2003) find that countries with more ethnic orreligious diversity are not more prone to experience conflict and that other factors

such as poverty, political instability, rough terrain, and large populations are bet-ter indicators for conflict onset. Their findings are also supported by analysis forcountries that are ethnically or religiously polarized. Cederman et al. (2009, 432)contest this view: Locating their study on the meso-level between aggregate stateactors and individual agents they find that “border-crossing ethnic affiliations havea considerable impact on the likelihood of ethnonational civil wars.”

Mechanisms and processes related to kinship that are underlying the produc-tion of horizontal and hierarchical social structure and the dynamic change of socialstructure itself have received increased attention in recent research. Read (1998),for example, developed a multi-agent model to explore in a given society on theone hand females’ decisions that affect birth-spacing and, on the other hand, howmarriage is affected by cultural rules. Based on his modeling results, Read takesa standpoint of a “dynamic tension” between what has been culturally specified as“proper behavior” and what is necessitated by ecology (cf. Smith 2003). Model-ing making use of kinship has also found its way into conflict research, where forexample Geller and Moss (2008) studied power structures in the Afghan conflict.Alam, Meyer, Ziervogel, and Moss, 2007 report on a model devised for informingpolicy makers of the spread of HIV/AIDS in South Africa using ethnographic dataon kinship and friendship networks.

The fact that kinship plays a pivotal role in social life and therein particu-larly in the socio-political and -economic realm can be made fruitful in particularfor agent-based modeling. “Kinship in human societies carries with it not only aconstructed basis for transforming a group of individuals into a system of intercon-nected individuals, but also a commonly understood conceptual basis of expected,and expectable, behaviors.” (Read, 2002, 7253) Agent-based models of socialitiesare representations of individuals (i.e. agents) that have cognition and behaviorand that interact with each other autonomously according to a specified set of rules(Ferber, 1999). Because kinship structures—and their broader societal effects—are notoriously challenging systems to model and understand, modeling comes inhandy as a methodological disambiguation technique (Moss, 2008).3

We contribute to this growing body of work with a formalized and dynamicmodel that represents the basal mechanisms of patrilineality and marriage as de-scribed in one case-study for a given target population.4 By borrowing the notion of“near-decomposability” from Simon (1996) we provide new dynamic insight intosome of the mechanisms underlying the relationship between kinship and armedconflict. We deem this to be important, because it enriches work conducted on

3Models of kinship structures are of course also necessary when asking “what if” questionspertaining to cultural changes and the effects such changes may have on a broad range of societalpatterns.

4We thus take a less constructivist standpoint as for example Read Read 2002.

conflict’s internals (e.g. Kalyvas 2006) and the internals of organizations active inconflict (e.g. Weinstein 2005).

3 Evidence from the North-West Frontier ProvinceData serving the needs of computational social scientists should inform about theactor, the actor’s behavior, and the actor’s reasoning. The behavioral data used byus stems, where not otherwise indicated, from Barth’s 1965 case-study on SwatPathans.5 We contrast his data with more recent and more generic research find-ings. The evidence deemed necessary to develop an agent-based model of kinshipstructures in Pakistan’s North-West Frontier Province is summarized in Table 1.

Pashtun society is primarily and foremost a patrilineal one where kin iden-tity, rights, and status are passed from father to offspring (Barth, 1965, 22). Sib-lings are considered equals and there exists no institutional sibling hierarchy (Barth,1965, 23). Pashtun society maintains social links over large distances, well beyondthe immediate neighborhood. Neighbors, in fact, may have distinctly different so-cial relations from one another (cf. Hill and Dunbar 2003). A mutually recognizedkinship link between two Pashtuns means they will share associations and providesupport to one another, both materially and in defense of life (Barth, 1965, 34).

If patrilineal rules set up rigidly the inherited kin links, the marriage rulesform a different type of kin rules, that have a greater degree of flexibility. Marriageis primarily to create bonds between families and the spatial and social movementof the female agents are completely controlled and decided by the male agents(Barth, 1965, 2). Polygamy is permitted for the men and there are virtually no agerestrictions between the people that get married.

The rules with respect to incest and unlawful marriage are very specific:“Incest prohibitions apply only to the third generation ascendant or descendant,to siblings of father and father’s father, to ascendant or descendant of wife, and todivorced wife of ascendant or descendant. . . . A man may not have intercourse with,nor marry, two women so related that if one of them were a male, they could nothave intermarried.” (Barth, 1965, 36)

Except for the divorced women, all women are marriageable and the nearestagnate above puberty has the right to impose a marriage. There is a bride priceresulting out of the marriage, usually around 65000 AFS (Tapper, 1991, 141-156),but a bride prefers at least marrying a man of equal status. However, in the end

5For a critique on Barth (1965), see (Edwards, 1998). According to Edwards, Barth consideredearlier work by Evans-Pritchard’s to be too structure-oriented and aimed to correct it. Agent-basedmodeling serves both views through the ideas of methodological individualism and emergence.

ID Rule description Source ImplementationLineage Rules1.1 Patrilineality 22 Identity and status are passed from father

to offspring1.2 Equality of siblings 23 No institutional hierarchies between sib-

lings1.3 Patriarch death Eldest son takes over household, wealth

is evenly split between sons. If there is noson, then the brother takes over, then thecousin, etc.

Marriage Rules2.1 Social control over

female22 The social contact of females is controlled

by the father. Marriages are arranged.2.2 Marital residence 33 Most couples reside in the household of

the husband’s father2.3 Marriageability age 36 Female: 15, Male: 152.4 Poligamy 36 Men may have up to 4 wives2.5 Incest prohibition 36 Marriage prohibited with third generation

ascendant or descendant, siblings of fa-ther and father’s father, ascendant or de-scendant of wife, divorced wife of ascen-dant or descendant

2.6 Brideprice 37 † Kotara (fixed, avg. bride price Afs.65,000) Groom’s household must meetthreshold

2.7 Inter-caste marriage 20 There seems to be no consistent rule. En-dogamy rate: 60%. Hypergamy rate:23%. Hypogamy rate: 17%. Women of-ten marry upwards, men rarely do.

2.8 Widow remarriage 40 Widows are unlikely to re-marry

Table 1: Agent rule set based on qualitative data. All sources refer to page numbersin (Barth, 1965) except for entries marked with †, the data for which which is takenfrom (Tapper, 1991, 142).

the group as a whole decides who marries whom, thus implying a group decisionmechanism.

Divorce is rare and usually detrimental to both the woman and the man.It harms the man’s reputation (Barth, 1965, 40), damages kin relationships, andadversely affects the whole group.

Both kin and marriage relations are important for creating alliances, underthe form of local affiliations or maybe more distant affinal relations, to strengthenexisting ties (Barth, 1965, 35). Thus the Pashtun society evolves as a connectednetwork of ties of patrilineal and affinal relationships that eventually translate intowhat makes a Pashtun, as required by Pashtunwali, their “code of honor”.

4 Methodology and Implementation

4.1 Agent-based Computational Social Science

Modeling, including computer simulations, can be understood as a scientific activitywith the aim of replicating a target system in such a way that the replication will re-act to inputs in ways resembling the reaction of the target system (Troitzsch, 1993).Most agent-based models are simulations. They consist of cognitive agents thathave local knowledge of the system, possess limited resources, interact with eachother according to defined sets of rules, and can influence each other (Ferber, 1999).Cederman (2001) points out that agents, by their mere interaction, also constitutetheir own environment. The agents interact with each other on the basis of a definedset of cognitive and behavioral rules. The rules execute differently depending onenvironmental conditions and agent behavior changes accordingly. Sometimes theinteractions of agents and their environment can lead to counter-intuitive emergentphenomena. Agent-based modeling should be viewed as a complementary alterna-tive to existing quantitative and qualitative approaches.

There are several platforms available for implementing agent-based sys-tems. We implemented ours in MASON, a multi-agent simulation toolkit written inJava (Luke, Cioffi-Revilla, Panait, Sullivan, and Balan, 2005).

4.2 The Kinship Model: Rule formalization and implementa-tion

Constructing a coherent chain between the raw data, the formalized, “modelized”form of the data, and the computational implementation constitutes the method-ological core of KinMASON, the computational model. Table 1 shows the datadefining the computational rules of the agents. In the following section we describehow this data is incorporated into the computational model, i.e., how it is translatedinto Java computer code.6

6While the logic applied to create a coherent link between data source and agent rule appears tobe of broader value to the modeling community, the method used here is only meant for a small and

The model consists of two types of agents, male and female. All agents havethe same ethnicity (Pashtun), the same religion (Sunni), and belong to one of threedifferent castes (high, middle, low). Castes are assigned according to a uniformrandom distribution. Since all agents are Pashtun and Sunni, the only differentiationbetween groups is based on caste.

Male FemaleAge cohort Mortality rate Age cohort Mortality rate

0 0.08451 0 0.070771 0.0152 1 0.027155 0.00828 5 0.00833

10 0.00488 10 0.0048215 0.006 15 0.0063920 0.00914 20 0.0092525 0.01025 25 0.0110630 0.01188 30 0.0130235 0.01523 35 0.0162240 0.02143 40 0.0207945 0.03282 45 0.0286350 0.05125 50 0.0418855 0.08188 55 0.0639360 0.12017 60 0.0924365 0.17984 65 0.148770 0.26998 70 0.2360975 0.39284 75 0.3581980 0.54976 80 0.5101585 0.72216 85 0.6823890 0.82172 90 0.7918695 0.87173 95 0.85318

100 1 100 1

Table 2: Mortality rates showing the probability of dying at any given age. (Source:Statistics Division Government of Pakistan, www.statpak.gov.pk)

Mortality rates are derived from life-tables, which we obtained from theStatistics Division Government of Pakistan (www.statpak.gov.pk; see Table 2). Othermodel parameters are summarized in Table 3.

well defined textual corpus. Once this body of data grows larger, other methods, though not otherreasoning, needs to be applied.

Parameter Value SourceInitial conditionsNumber of households 10 Hand-picked to highlight model

dynamicsChildren per household (average) 4 Empirical; SDGP†

Children per household (std. dev.) 1 Empirical; SDGPWealth per household (average) 100,000 Educated guessWealth per household (std. dev.) 10,000 Educated guessOperating parametersFemale age of marriageability 5 Empirical; Barth 1965, Tapper

1991Female age of child-bearing 16 Empirical; SDGPFemale end age of child-bearing 30 Empirical; SDGPMale age of marriageability 18 Empirical; Barth 1965, Tapper

1991Birth rate‡ 0.35 Empirical; SDGPEndogamy rate 0.6 Empirical; Barth 1965, 20Hypergamy rate 0.23 Empirical; Barth 1965, 20Hypogamy rate 0.17 Empirical; Barth 1965, 20Bride price 65,000 Empirical; Tapper 1991, 141-156Number of generations for whichdead patriarchs are remembered

5 Empirical; Lewis 1965

Table 3: Model parameters. † Statistics Division, Government of Pakistan(www.statpak.gov.pk). ‡ Birth rate signifies the probability of each married womanof child-bearing age bearing a child in a given year.

KinMason uses a centralized “matchmaker” to evaluate potential pairings,and to choose and select couples for marriage. The matchmaker finds suitable cou-ples via the algorithm presented below. There is a set of females F and a set ofmales M. For each male mi a female f j is randomly chosen from a subset of fe-males for which the marriage rules as introduced above apply. If such a female f j isfound, marry her to mi, remove her from the list of available females F and updatethe kinship links. If f j was the fourth woman mi marries, then remove him from thelist of available men. The process is shown in Algorithm 1.

The matchmaker must find the subset of females which are compatible tomarry a given male. Compatibility is determined by a rule-based system. The rulesfrom Table 5.2 are codified here.

The inter-caste marriage rule (Rule 2.7) is probabilistic rather than strictlyprohibitive. Inter-caste marriages are allowed, they are just less common that same

Algorithm 1 MATCHMAKER(F , M)F = f1 · · · fm (set of available females)M = m1 · · ·mn (set of available males)M← SHUFFLE(M)for mi ∈M do

Fc← COMPATIBLESUBSET(F , mi) (see Algorithm 2)if LENGTH(Fc)> 0 then

j← RAND(1, LENGTH(Fc))MARRY(mi, f j)REMOVE(F , f )if LENGTH(mi.spouses) = 4 then

REMOVE(M, mi)

Algorithm 2 COMPATIBLESUBSET(F , m) returns the subset of F which are com-patible to marry m

F = f1 · · · fm (set of prospective brides)m = prospective groomFc← COPY(F)for f ∈ F do

if f and m are too closely related (see Rule 2.5) thenREMOVE(Fc, f )

if f and m are from incompatible castes (see Rule 2.7, which is stochastic)then

REMOVE(Fc, f )return Fc

caste marriages. Barth (1965, 20, 40-41) recorded the rates of inter-caste marriages:60% of marriages occur between couples from the same caste (i.e. endogamy), 23%of marriages occur between a man and a woman of a lower caste (i.e. hypergamy),and only 17% involve a man marrying a woman of a higher caste than his own.

Marriage is strictly prohibited between two people who are closely related.Incest prohibitions apply only to the third generation ascendant or descendant, tosiblings of father and father’s father, to ascendant or descendant of wife, and todivorced wife of ascendant or descendant (Barth, 1965, 36). We implemented thisrule based on network distance in the kinship graph. If the network distance be-tween a potential bride and groom is 3 or lower, marriage is prohibited. Note thatthis allows marriage between cousins, which is actually not only common, but evenpreferable for it is considered a secure marriage (Fischer, 1994, 83).

When the network distance is 4 or higher, marriages are allowed except in

the case of grandparent’s siblings (network distance of 4). To handle this specialcase, we keep a generational counter and prohibit marriages with network distanceof 4 which differ by more than 1 generation.

For purposes of verification, we systematically compare model output andinternal mechanisms against a set of test cases. Type-two test cases involve logicinstead of mere value verification and include: “If the oldest son dies, who becomeshead of household?”, “What are the ties between old and new household?”, “If themother dies, who is the step mother?”, “Where do widows go?”, “Where do non-tied agents (“hermits”) go?”, If the father dies, how do two brothers keep a linkbetween themselves?”.

The number of parameters and the size of the behavior space is kept as smallas possible and closely matched with the available evidence. Like most empiricallydriven models, there are several parameters to adjust, but we have kept the rulesetto a bare minimum. Large parameter and rule sets tend to obfuscate the drivers andfactors of the simulation results.

Modeling, of course, is also simplification. We refrain from modeling hag-gling over bride price and we choose marriage pairs with a centralized mechanism.Consequentially our agents do not act strategically, for example, building affinalrelationships with distant allies to strengthen existing ties as described in (Barth,1965, 40).

5 Results

5.1 The life and death of P073

To help explain the micro-dynamics of the simulation, we narrate the life of agentP073 (marked with a yellow circled “A” in Figure 1(a)). P073’s story is typical ofmany other agents in the simulation. Figure 1 depicts two screenshots from thesimulated kinship network at time steps 29 (left) and 30 (right). It is a graphicalrepresentation of the evolution of the network P073 is embedded in. Each vertexdenotes an agent. Triangles denote male, circles female agents. Red, purple, andblue coloring indicates class: Red is low, purple middle, and blue high. Dead agentsturn gray. Bold solid edges between agents denote blood relations; thin solid edgesmarriage relations. Edges are added when children are born to connect them to theirfathers and when couples are married to connect the wife to the husband. Whenagents die, they and their edges are removed from the network, except for heads ofhouseholds who are “remembered” for some a number of generations (determinedby model parameter 18 in Table 2).

P079

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i 29 d 30 f i

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Figure 1: Screenshots of the kinship graph at time steps 29 (a) and 30 (b). Males areshown as triangles, females as circles. Heads of household are drawn as diamonds.Bold edges represent patrilineal relations, thin edges marriage connections. Thehigh caste is drawn in blue, the middle caste in purple, and the low caste in red.

In time step 7 agent P073 is born to parents P012, the father (circled green“B”in Figure 1), and P013, the mother (circled green “C”), as their 6th child and 3rdson. 22 time steps later P073 marries P106 (circled green “C”). Their marriageimplies that all conditions according to rules subsumed under ID 2 in Table 1 weremet. P106’s father, P035, is from the red caste. P106 is now member of the bluecaste; she used to be a member of the red caste. This is an example of caste changeaccording to rule ID 3.2 in Table 1. At time step 30 we see in Figure 1 (right)that P073’s father, P012, died. According to rule ID 1.7 in Table 1 P014 (circledyellow “E”) succeeds his father P012 and becomes the new head of household. Be-cause P073 is already married he can found his own household and becomes headof household himself according to rule ID 2.2 in Table 1 (circled yellow “A”). Intime step 44, P073 and P106 have a daughter, P144. The mother, P106, is 19 yearsold, which is in accordance with property number 7 in Table 3. P073 and P106 havemore children at time steps 48, 49, and 50. In time step 52, P073 marries his secondwife, P146. In the same time step he has another child with his first wife, P106, aswell as in time step 54. In time step 62 he has his first child with his second wife,three years later they have their second child. One year later, in time step 66, P073dies. He was 59 years old. One of his sons will succeed him as head of household.

After having developed a more thorough understanding of individual agentbehavior and interactions, and the model’s micro dynamics, we turn to aggregateviews of the model. Two questions—already posed in the introduction—are in theforeground, namely how group identity structures and friend-foe patterns emerge.For the former we look at group formation (intra-group) processes and cohesivenetwork measures; for the latter we focus on measurements for inter-group identi-fication. Before we do so, let us briefly characterize the social structures that areevolving as a result of the implemented socio-demographic and marriage micro-interactions.

5.2 Network and other statistics

A natural way of looking at kinship structures is via dendrograms. They allowfollowing the evolution of group structures over time in terms of hierarchical clus-ters. In kinship studies these clusters can be interpreted (with increasing clustersize) as families, clans, and tribes. Figure 2 depicts the evolution of a patrilinealdendrogram, i.e. only the patrilineal structure is reproduced. The coloring of thedendrogram distinguishes different time periods.

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P22794

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P1220

P834

P1252

P5017

P7250

P13724

P19780

P2582 P2660

P6799P6865 P6831

P3631

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P13338 P13491

P16132

P25505

P15991

P23568

P13767

P19487

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P22765P21940

P19443

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P25550 P24958 P25023

P16920 P17296

P19829 P20013

P19557

P14583

P22631

P12416

P9681

P24838

P2189 P2225 P2239

P20410P16167 P16156

P13062

P14272

P13177

P8260

P15782 P18301 P18227 P18387

P2420

P12482

P7557P6941

P19870

P18130

P18587 P18850 P18533 P18412

P17340

P16111

P17185

P14067

P10063 P10179

P20559

P13770 P13947 P14542

P21393

P21801

P8919 P8640

P15107

P24090

P1411

P16960

P24341 P23867 P23931

P26200

P23175

P879

P9737 P9576 P9868

P23296 P22907 P26167

P24583 P24837

P15426 P14983

P11273

P13061 P13211 P13332 P13258

P9470 P9298

P4779

P19711P17064

P9281

P16521

P4446

P23248P26110

P775 P861

P5250

P3506

P25277

P18501

P25745 P25540

P26232

P26293

P16764 P16722

P16644

P741

P15593

P23706

P22045 P21800

P4948

P1690

P3725

P21329

P8488 P8459

P9972

P3275 P3392

P5450 P5494 P5669 P6220

P022

P16921

P8401

P17017

P13709

P10716

P25795

P15318

P18502

P23905 P24467 P24494

P1940 P1891

P9695

P1346

P26275

P5730

P23824

P23015

P2727

P24224

P15462

P6677

P4653

P25195

P690

P22169 P20148

P26276

P4411

P19078

Figure 2: Dendrogram of patrilineal kinship structure at time steps 10 (light green),50 (green), 100 (dark green), 200 (light blue), 400 (blue), 600 (purple), 800 (lightred), and 1000 (red).

The simulation produces three distinguishable types of scenarios: “extinc-tion”, “hegemony”, and “pluralism”. The scenarios are depicted in Figures 3, 4,

and 5 as sequences of network graphs correlated with population size time-series.Each sequence consists of graphs constructed from data recorded at time steps 10,50, 100, 200, 400, 600, 800, and 1000.

In the extinction scenario (Figure 3) the population goes extinct after aninitially unknown number of time steps. While this is obviously a counter-factualsimulation result, the mechanisms driving it produce valuable insight. First, thedecrease in population numbers has to do with the distribution of wealth in the pop-ulation. As a result of the unequal distribution of wealth, many parent agents can nolonger afford to marry their child agents off, which diminishes population diversity.A diminished population diversity further decreases the chances of marriage ac-cording to the marriage rules, which leads to a decrease in the number of offspring.Less agents mean not only a smaller likelihood of finding a marriage partner, butalso a less dense and more compartmentalized network, the compartmentalizationbeing an indication for the social exclusion of groups due to lack of inter-groupmarriage.

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Figure 3: Sequence of network graphs for an example of the “extinction” pattern,at time steps 10, 50, 100, 200, 400, 600, 800, and 1000 correlated with the numberof total population (solid).

In the hegemon scenario (Figure 4) one caste takes over the other castes

and ends up being the only survivor at the end of the simulation run. The caseis interesting despite being counter-factual in that it exemplifies the importance ofcritical size for the survivability of a particular group of people in the model. Oncea certain agent population size is reached an agent group becomes biologically self-sustainable. However, as with the extinction case, the hegemon case exemplifies theimportance of modeling complex dynamics. While many processes on the inter-agent level are dealt with in a stochastic manner, this emergent phenomenon isof chaotic nature. Which caste eventually will prevail—or afore, if and when thepopulation will die out—cannot be predicted on the basis of the initial parameters.

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Figure 4: Sequence of network graphs for an example of the “hegemon” pattern, attime steps 10, 50, 100, 200, 400, 600, 800, and 1000 correlated with the number oftotal population (solid).

The data shown in Figure 5 belongs to the pluralism scenario. It is charac-terized by a relatively equal distribution of caste size and wealth in the total pop-ulation, and a steady increase in total population size. The three processes takentogether in theory allow for the realization of the maximum number of marriagesunder the model’s social mechanism regime. That is, pluralism is not only favorableto marriage within a caste (endogamy), but also with regard to relative upward (hy-pogamy) and downward marriage (endogamy). Runs of the type of pluralism also

suggest to evolve into a kind of system behavior that in the longue duree resemblesa dynamic equilibrium or at least an evolution that converges into a steady state.

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t=800 t=1000t=600t=400

Figure 5: Sequence of network graphs for an example of the “pluralism” pattern, attime steps 10, 50, 100, 200, 400, 600, 800, and 1000 correlated with the number oftotal population (solid).

So far we have seen that the model is driven by the socially important ideasof variety and equality, and meta-stability on the agent-level characteristic to com-plex social systems.7 Furthermore, we noted that these processes are not indepen-dent of each other and that instead population diversity, population size, and relativewealth distribution are inter-correlated.

What can we say about the types of networks created by the agent-interactionsand social mechanisms and processes described? We expect to find densely clus-tered networks with small average path-distances. Endogamy tends to favor clus-tering and the evolution of tightly knit networks, while exogamy creates shortcuts

7For the purpose of this paper we understand complexity as a “condition in which agent behaviorand social interaction combine to generate macro-level outcomes that could not be predicted fromknowledge of the behavior and nature of interactions alone, and result in sporadic volatile episodes,the timing, magnitude, duration and outcomes of which are themselves unpredictable.” (Geller andMoss, 2008, 322).

to distant areas in the network. The kinship and marriage mechanisms implementedfavor the creation of vertices with a high degree centrality, such as heads of house-hold.

Figure 6 shows degree, closeness, and betweenness centrality for 10 “plu-ralism” runs at different time steps.8 Centrality measures can be interpreted asindicators of an agent’s power (Degenne and Fors, 1999, Chap. 6). Central actorswith high degree centrality have a large number of connections to other networkmembers. Central individuals with high closeness centrality can reach any actor inthe network in relatively fewer steps. Betweenness centrality represents the relativenumber of geodesics that pass through an actor. Closeness and betweenness areindicators for controlling network communication.

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Figure 6: Degree, closeness, and betweenness centralization for 10 simulation runsof the pluralism scenario at time steps 10, 50, 100, 200, 400, 600, 800, and 1000.The gray ribbon indicates the standard deviation from the mean.

Watts (1999) reports small-world structures likely to be present in many realsocial networks. Geller and Moss (2008) report to have found a small-world-likestructure for Afghan power structures. The network topologies we expect to find arelikely also going to be of small-world nature. Small-world networks are interestingbecause they combine two seemingly irreconcilable features: high clustering andshort path distances (Watts, 1999, 508-509). In other words, a small world graph isdefined by n� kmax� 1 while exhibiting C� k/n (Watts, 1999, 511), where n isthe number of vertices, k the number of (undirected) edges, and C the clustering co-efficient. Small-world networks differ from random networks with regard to mean

8Applying graph-theoretic measures over a network snapshot, could increase the risk of losingthe context of a particular agents position in the network. An agents behavior may not just be reducedto the notion of a mere node. Since in complex systems, it is very hard to anticipate how emergingpatterns result from interactions at the micro-level, it could be misleading to apply measures on justa single snap-shot of the network.

sparseness, mean k, and decentralization, kmax� n, which are distinct features ofthe former. Thus, high C and short path distances, L, are concepts that need to beunderstood relative to other network topologies, such as random graphs. We expectCsmall−world �Crandom and Lsmall−world � Lrandom.

In Figure 7 the values of 10 representative networks generated in the kin-ship simulation (circles) are compared with 10 Erdos-Renyi random networks (tri-angles). As hypothesized, we find sub-networks that are characterized by the pres-ence of connections between almost any two nodes within them and a prevalenceof short paths between vertices.

Small-world networks are of interest to us because they exhibit propertieswhich are “sufficiently well connected to admit rich structure, yet each element isconfined to operate within a local environment that encompasses only a tiny fractionof the entire system.” (Watts, 1999, 499). The description’s relatedness to whatSimon (1996, 209) coined “near-decomposability” is evident: “most things are onlyweakly connected with most other things”.

5.3 Cross-validation

Simulation models are validated on the macro-level by comparing computationallyderived statistical signatures (such as spatial distributions and time-series) againstreal world data (Moss and Edmonds, 2005, Axtell, Axelrod, Epstein, and Cohen,1996). Axtell et al. (1996) use the notion of relational equivalence to show that twosystems exhibit similar internal behavior (comparison of internal validity). Indeed,macro-level cross-validation is not always appropriate or feasible, depending onmodel purpose and data availability. In such instances, case-by-case comparisonand techniques from comparative research come into use. The simulation is thenseen as a further case that needs to be understood against other, similar cases. Theoutput from the simulation is compared against general accounts taken from theliterature other than the one used to inform the model (out-of-sample).9 Out-of-sample validation procedures deny tautological conclusions (though not post-hoc

9The “easiest” way to conduct cross-validation, of course, would be to compare simulation re-sults to Read’s (2002) universal structure genealogical grid that is shared cross-culturally. However,the limits of our own model impede us from choosing such an approach. Kinship is not just a ge-netic concept, but in many respects defined by cultural ascription of kinship rules and roles (seealso Hirschfeld 1986). Though the kinship model presented by us is rather genealogical than ge-netic for it makes use of culturally defined marriage rules, it does not add up to the refined kinshipterminology described by Read (2002).

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fallacies).10

Where possible, we use real data and data structures to cross-validate. How-ever, with some minor exceptions we cannot compare actual numbers against eachother. Tapper (1991, 60) reports in her study of Maduzai Pashtun women that out

10Note that population distribution, hypo- and endogamy ratios, and the like are hardwired intothe model. Comparing related model output to actual demographic and ethnographic data is in sucha case more accurately considered being a verification, not a validation issue.

of 208 recorded marriages in the core lineages only 4 were hypogamous. This is amuch lower rate than we got from Barth (1965, 20) and subsequently used in ourmodel. This case is not particularly illuminating either, since we hard-wired thehypogamy rate into the model.

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Figure 8: Distribution of son counts per man, comparing our simulation data tothe genealogical record observed in the Pashtun Tribe Tree. Men without sons areexcluded, just as they would be excluded from genealogy records. The simulationdata is separated into heads of household and the rest.

We instead compare the output of our model to aggregate data collectedfrom Pashtun genealogy charts. Genealogy charts of this kind are useful, but theirlimitations should be clearly noted. Specifically, they show only a subset of the

tribe’s actual history and the sample is skewed toward successful patriarchs. Fe-males are not included, nor are men who have no sons. Nonetheless, the genealogydata provides a partial view of the distribution of son counts of patriarchs.

We compare this distribution to the data from our simulation, divided intopatriarchs and non-patriarchs (see Figure 8). Men without sons are excluded, justas they would be excluded from the genealogical record. All three distributionsare long-tailed (likely lognormal) indicating that it is more common to have a fewsons than to have many. We also see that the empirical data matches the set ofpatriarchs more closely than non-patriarchs. This is a positive result and confirmsour hypothesis that the genealogical record would be skewed toward patriarchs.

In general it is difficult to extract consistent metrics from the kind of ethno-graphic information that we chose to use. For example, Lindholm (1979, 488) statesthat patrilineal societies do not exhibit complex hierarchies. While it is intuitivelyintelligible what he means—only men pass identity down the genealogical line—itis difficult to apply the statement to real data and thus include it as robust measurein the cross-validation process. The same applies for Lewis (1965, 92) who writesthat tribes can be measured in terms of generational depth: The more depth thereis, the stronger the patrilineal society is. What he refers to is the cultural idea of a“founding father”. But there is no hint toward a robust metric that would allow todistinguish between what is a “shallow” and what a “deep” society.

As mentioned before, general accounts of what constitutes kinship (Read,2001), how unilineal descent groups are organized (Fortes, 1953), and what con-temporary polities look like in a tribal society (Lindholm, 1979) are of interest tous. Can we identify such general patterns in our data?

Read (2002) uses four levels of social organizations, the first and second ofwhich relate to non-human primate species. Beyond the first level all organizationsare built from units of a lower level. To shift from the second to the third level, thatis from a group to a societal group, the development of a shared belief system, aculture, is needed. Such a cultural representation is hardwired into the simulationby the matchmaker. Read also reports the social organizations’ characteristics. Thepatterns generated by our simulation should resemble those on levels three and four.Read (2002, 7252) reports that societies located on the third level are “composedof several residential groups, each of which may act as a corporate group withregard to resource ownership and access.” He continues that there typically “is nopolitical structure or institutionalized position of power and authority.” Societieson the fourth level, he states, may exhibit such a centralized structure of powerand authority. The type of clustered structures we describe and depict above asan emergent property can be seen as extended families and were they embeddedin a geographic space would constitute a kind of residential unit. It is easy to seehow the head of household could increasingly institutionalize a position of power

and authority with the ongoing of the simulation and the growth of society. Thisposition could then also be interpreted as the epicenter of a common identity.

Fischer (1994, 92) reports that marriage is used to consolidate status. It ishardwired into the simulation with fixed parameter values that agents can marry upand down. A further reflection of this principle is of course the idea that marriage iscostly. Two phenomena emerging from the simulation can be related to this: First,the simulated population or parts thereof are sensitive on the macro-level to statusconsolidation. Too much or too little status consolidation leads to dominance ofone group and disappearance of the other groups. Second, Figure 5 shows howafter generations new relations can be tied over large distances. Depending on howin- or exclusive access rules to other groups in the simulation are the pattern willdiffer. Whereas Fischer (1994) refers to individual status consolidation, which wemodel, the simulation displays the emergence of consolidational processes on theinter-group level.

6 Putting the simulation results into context: Iden-tity, friend or foe, and conflict

Earlier research on kinship emphasized its exclusive nature, often putting kinshipon the same level with genetic relationships. Such a view has become outdated andkinship instead has developed into a more inclusive concept consisting of genetic,cultural, and choice relationships (Read, 2001). The simulation presented takesthis into account and simulation output is not tantamount to theoretically expectedgenetic structures. Let us therefore look at the simulation results from a broaderangle. In particular we are interested in whether we can see evidence, based onthe simple implemented ruleset, for drivers and/or emerging structures of identity,friend or foe patterns, and lines of conflict.

The relationship between lineage and genealogy on the one hand, and polit-ical structures on the other is founded on firm evidence. Tapper (1991, 45) reportsfor Durrani social organization that all concepts of social groupings are influencedby the notion of patrilineal descent. And Lewis (1965, 92) asks the related piv-otal question: “To what extent are ancestors in the genealogies points of corporatecleavage or aggregation?” He continues to develop a conceptual scale of a society’sproneness to lineage. The proxy he uses is politico-jural lineage, expressing thedense connection between kinship and political structures, and the concepts natu-rally related to the latter, such as identity, friend or foe, and conflict.11 Depending

11Lewis (1965, 94), for example, asks how significantly politico-jural lineage segmentations aredependent on lineage segmentation. He further inquires whether cults and religions are organized

on the society, Lewis (1965, 92) further argues, that the significant functional en-tities can be each generation or chunks of generations. The relationship betweenstructural relations and genealogical organization thus varies Lewis (1965, 92).

Social meaning can be ascribed to the clusters and lineages produced in thesimulation. Relationships based on mere graph-theory and genealogical principles(as for example represented by the matchmaker) receive an additional connotationin that they mirror political solidarity (cf. Lewis 1965). Both, synchronically anddiachronically we can identify structures that resemble families, extended fami-lies, clans, and tribes. The kind of meaning ascribed to these structures dependson a variety of factors, including, of course, the local culture, but also historicalcircumstances and trajectories as well as opportunities.12 Segmentary lineage orga-nization is adjusted to accord with political realities by different processes (Lewis,1965, 97-101). Thus, there is no answer to the question how absolute the loyaltiesare of member of unilineal groups (Lewis, 1965, 101).

An example for such identity and loyalty structures are qawm (Geller, 2010).In Afghanistan and in parts of Pakistan a qawm is an opportunistic solidarity spacethat consists of a variety of overlapping and non-exclusive solidarity networks.13 Itcan mean (extended) family, tribe, descent group, ethnicity, “people like us” (Tap-per, 2008, 101), “an occupational group” (Roy, 1992, 75); or it can be a discourseof solidarity which is applied in fluid ways to different groupings: it can connotea complex interpersonal “network” (Roy, 1992, 22) of political, social, economic,military, and cultural relations (Mousavi, 1997, Rasuly-Paleczek, 1998, Roy, 1995,Shahrani, 1998, Tapper, 2008). These meanings are not exclusive: qawm do nothave clear boundaries nor do they divide Afghan society into mutually exclusivegroups.

If there is no evidence that the construction of identity—as defined above—follows strict rules, then what can we learn from the simulation model? The modelteaches us of the speed and uncertainty with which structures that directly influencethe identity of individuals and groups change. It educates us indirectly about friendor foe dynamics. Congruent with expectations based on research on attitude changein individuals (Crano and Prislin, 2006), we do not observe short-term volatility inagent identity. In the long-term, by contrast, we observe shifts in identity struc-

according to segmentary lineage, how much social activities are based on lineage. It is worthwhileto note that Lewis (1965, 107) comes to the conclusion that it does not make sense to measure thestrength of unilineality and that it is more important to understand the social functions that are tiedto it and the kind of politico-jural structures this evokes.

12It would be wrong to assume that kinship relationships are always ascribed in a consistentmanner.

13See Tapper (1991, 46-47) for an empirical account and Geller (2010) for a more general de-scription.

tures due to the separation or merger of lineages (see Figure 5). With this respectthe model sensitizes us about the unintentional consequences our decisions have.Imagine, for example, a military coalition being operative over several years in acountry with a population the identity of which is marked by heterogeneity. De-cisions will have to be taken about with whom to cooperate and with whom not.However, identities and relations between different identity entities are regularlyfalsely assumed to be intangible when in fact they change over time.

This brings us to the issue of friend or foe patterns. Probably the best knownfriend or foe pattern is: The enemy of my enemy is my friend. But does this prin-ciple also hold true in the context of the simulation at hand? It is likely to bemisleading due to the difficult relationship between ego and first cousin (tarbur)and “inward” cohesion. Lindholm (1979, 486-487) reports with regard to the lat-ter the following concentric patterns of solidarity in times of conflict: Brother +brother vs. first cousin + first cousin vs. second cousin + second cousin vs. . . . vs.whole lineage + whole lineage. Within such a structure the classic pattern of theenemy of my enemy is my friend cannot materialize. What instead materializes is areflex to form a line of defense against any outside enemy to protect the integrity ofthe solidarity group, this being the largest entity that is threatened and has a sharedidentity.

The reality of friend or foe patterns and conflict, of course, turns out to bea mixture of both and, hence, more complicated. “The pervasive hostility betweenthe sons of brothers”, Lindholm (1979, 488) reports, “led to the development of anetwork of alliances within every village that divided it across lineages into two ap-proximately equal parties, called dulla.” “These parties are not formally structured.They are simply a statement, couched in universal and abstract terms, of the fluidoppositions and alliances of individuals.” (Lindholm, 1981, 151) The dulla andall other internal wars would be seized in case of an external enemy and internalquarrels are often fought out using a neighbor’s help (Lindholm, 1979, 488). Par-ties, then, are merely conglomerates of equals and their dependents, with a nominalleadership, held together only by hostility toward personal and group enemies. Theeffort to cobble together such a conglomerate to then use it as a political instrumentis significant. Qawm, for example, are used for concerted political purpose (wolus,i.e. mutual support, tayyfa, i.e. political action of a group (Tapper 1991, 46-47).Azoy (2003) describes in his classic study the energy, prestige, and resources thatflow into such an endeavor.

More generally speaking, conflict needs to be understood as a combinationof structural preconditions combined with instantiations of the idiographic (Lubke-mann, 2005). Among others, Azoy (2003) makes very clear that organizing a groupfor temporarily limited conflict is costly due to overcoming structural impediments.The importance of the local is thus significant in terms of its structure. However,

our simulation puts emphasis also on the long-term temporal dimension. Assumingthat structural preconditions are static can be misleading.14 They are by contrast asource for endogenous variation in itself.

This bears two important consequences for conflict. First, the kind of uni-lineal kinship structure generated in our simulation is not inherently favorable towaging conflict. Tribes are not organized but in times of conflict (Lindholm, 1981,147). In fact, the structural impediments to group organization already start inthe household (Lindholm, 1981, 148). Social impediments need to be overcomebefore an organizational status is achieved that would allow to wage conflict. Sec-ond, armed conflicts do also take part in the strategic sphere. The computation-ally generated structure and its associated sudden changes introduce uncertaintythat renders strategic thinking and assuming strategic stability—for example interms of alliances—difficult. An example of such “unexpectedness” is the re-cent cooperation between the traditionally antagonistic Mehsud and Wazir tribesin North Waziristan under the leadership of the “normally” peaceful Dawar tribe inthe Taliban-led resistance against Pakistani military and Western coalition forces inthe region.15

There are of course many other factors and conditions that shape patternsof conflict we do not intend to describe here, such as the local socio-cultural valuesystem. Lindholm (1981), amongst many others, rightly points out that the interpre-tation of the actual incident and the derivation of the appropriate reactive behavior issubdued in the Pashtunwali (Janata and Hassas, 1975). Absence of or entrance intoa conflict is hereby regulated. It is thus often not an individual’s choice whether ornot to enter a conflict. More often, an individual, or group, is compelled by societalnorms of what is expected from an honorable man, a leader, a strongman, etc., andforeseeable consequences if the individual fails to meet those expectations.

Other conflict patterns are geographically situated. Tapper (1991, 46) namesresidential propinquity as an organizational principle. More generally, geographicalcloseness is important for both, cooperation and conflict (Tapper, 1991, 48). Landpressure and territorial expansion are further factors for conflict (Lindholm, 1981,153). But they remain beyond the scope of this article. Further factors for oftenextensive warfare that have not been mentioned are exiled individuals or groups(Lindholm, 1981, 153-154) and manipulatory third parties (Lindholm, 1981, 149).

14Such alliance formations might be stable with regard to internal power struggles in the senseof a strategic balance and stalemate, as Lindholm (1981) suggests, but certainly not with regard tointervening outsiders.

15Syed Saleem Shahzad, “Revolution in the Pakistani Mountains”, Asia Times, 23 March 2006.

7 ConclusionsThe aim of this paper was to build a realistic socio-cultural landscape to understandattitudes and allegiances in irregular warfare. Technically we aimed at demon-strating the development of a realistic social structure for general socio-scientificmodeling purposes. Against the background of advances in social theory and com-putational social science, it would be erroneous to believe that this ought to bepurely a technical exercise. On the contrary, it includes significant reflection uponthe case to be modeled, the modeling techniques to be applied, and the questions tobe answered.

We assumed that the transactional structure and values underlying kinshipand the arranging of marriages are replicated throughout the population of interestto us, i.e. the Pashtun in the Swat Valley. Kinship is not the defining elementin conflict patterns. But it is an important explanans in the analysis of conflictpatterns (cf. Salzman 1978, 1983). In fact, Lindholm (1981) explains the structureof violence amongst the Swat Pashtun in terms of a quasi fractal pattern, that isego—friend—enemy. The pattern is recurring in the household, the kin group, thevillage, the tribe, etc. It would be absurd to assume away the influence of kinshipin such patterns. Lindholm (1981, 154) is clear and right to point out—and withhim many others, such as Ferguson (1990) and Snyder (2003)—that behind thestructuring of violence in the Swat valley lies the local social order.16

We demonstrated a way of making use of largely available and highly qual-itative data structures, that is ethnographic data, in formal computational modeling.We then showed that results from rigorously empirical-driven models can be vali-dated against out-of-sample data and lead to insight that goes against our generalunderstanding of the role of social structure in conflict. Namely this is that thesocial structure should be assumed an endogenous source of volatility in armedconflict itself. Furthermore, we stress that instead of looking only at the emergingstructures, the mechanisms that lead to the these alliance and friend-or-foe patternsbear importance in their own right. They are structures of choice and opportunityrather than constants in the system, though constrained by it.

There are many ways by which this research could be extended. Of coursewe could play through all the strategic variants of kinship relationships, thoughthis might not be especially useful. The main work that now should be conducted istwofold. First, to come up with sociologically and empirically founded ways to sys-tematically deal with the boundary specification problem. Who belongs to whichfamily, clan, qawm, dulla, etc. is essential in order to understand the local archi-

16This is not to say that in contemporary armed conflict this local order is the sole defining factoror even cause.

tecture of violence that so much depends on blood and revenge and when to applywhich. Second, we need to develop some sort of “solidarity” measure that noteswhen kinship structures are tightly packed versus attenuated. In accordance withFerguson (1990) and Snyder (2003) the hypothesis to empirically test would then bewhether this measure of social cohesiveness and network strength correlates withacts of collective violence. These kind of questions are of increasing importancein the wake of social change, when agnatic ties start losing their strength (Tapper,1991, 49) and loyalties start to shift.

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