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Main Narrative: Networks and multilevel anthropology NSF Anthropology proposal

INTRODUCTION. This project focuses on multiple levels of networks and social organization. Leaf

(2009:16) defines social organization as specific networks of and cognition about links that allow members

to coordinate. “Organizations are people under some common group name and with mutually adjusted

behavioral expectations…. We can describe the positions and their mutual relationships.” White (2010b),

however, shows many examples of ethnographic network analysis that identify organizations in these

terms except that groups and positions are implicitly recognized rather than formally named. In either case:

“Mutual adjustments … based on agreements, in turn, are the same things as mutual systems of obligations

and privileges or rights and duties. This inevitably involves the creation of sanctions” (Leaf:17-18). Not all

networks are organizations, but “the members of organizations can form networks, and groups of

organizations can form networks” (Leaf: 80). That is, organizational networks can be multilevel as

formalized by Harary and Batell (1981) as a particular type of system, one that is by definition multilevel

and potentially recursive: a network in which a node at one level may contain a network of relations at the

next level. Imagine, for example, “drilling down” from a network of organizations to an organizational

node, which opens up a view of the networks of individuals within it. This multilevel construct is used

recurrently in this project to locate how various ethnographic networks are embedded. It focuses on actual

network ties rather than idealized positions, not just interpersonal networks but also among countries,

regions, or divisions within countries, to societies, and drilling down further to social network. Once

organizations or the whole communities studied by anthropologists are located in a network of networks,

we can then study how cases at this level are influenced by “peer effects” of other nodes or networks of

nodes at a higher level. Analysis of relationships among variables for a sample of cases only attains

empirical validity when “peer effects” are included in the model (see METHODOLOGIES). What Leaf

(2009:167) credits as “the best-developed general approach to describing networks is … the network

analysis of Douglas White ….”: “he has formulated definite ways to express the expectations for … patterns implicit in the stated organizational rules and

compare them with patterns in the networks (see White 1999). This generates the possibility of finding the often dynamic

relationships between the networks and the stated organizational charters over time…. [M]ultiple network analyses in a single

community can be treated as overlays, which can let us see how the organizational consequences of such organizational rules

interact, relating, for example, marriage networks to economic networks. For a demonstration of the way that a variety of these

ideas and techniques can come together in a single ethnographic analysis, see White and Johansen‟s Network Analysis and

Ethnographic Problems: Process Models of a Turkish Nomad Clan (2005)” (Leaf 2009:173). “White and Johansen's

„network analysis‟ is entirely different from the „network analysis‟ that was a rage for a while in the 1960s and 70s.... Network

analysis, as White has been developing it and as he and Johansen apply it here, is not just one technique or method but a whole

armamentarium of them, united under a system of general and powerful conceptions of social organization as such" (Leaf 2005).

The present project has a range of core network constructs, like Harary and Batell‟s multilevel system-

concept of network embeddings, that help to guide data integration, analysis, and comparison of new

results in the context of multilevel ethnographic network analyses. In kinship organizations the simplest

multilevel core-constructs are those generated by the arrangements of ties between families at one level

and, drilling down to one family, the ties within it among family members. Although symbolically “the set

of words which fulfil the domestic purposes of the English words „father‟, „mother‟, „brother‟ … and so on

– is used to denote what we might otherwise be inclined to regard [in many societies] as economic,

political, legal, or religious relationships” (Leach 1982:317), White and Johansen (Chapter 4) critique any

assumption that “the nuclear family was a universal building block out of which larger kinship units were

constructed” (Murdock 1949).1 For broader social circles, between-family networks define in a far more

reliable way such structural features as relinking and structural endogamy2 (2005:194-198, 456-458) that

form the basis for discoveries about social cohesion or networks of exchange and their consequences for

some groups or outcomes and not for others. The long-term studies (Johansen and White 2002) of Aydιnlι

1 The PI‟s resounding critiques of Murdock (White 1968) were the basis of their arms-length collaboration (Murdock and White 1969). PI White in 1969 reoriented

SCCS away from an HRAF model toward historical networks solutions of Galton‟s problem and extensively reoriented the approach since then. 2 Without assuming biological kinship, structural endogamy uniquely measures the largest set of nodes (families) of an inter-family graph in which every pair is

connected by two or more intermarriage paths that are disjoint. Anthropologists know this as redundancy of kinship ties. If individuals are the nodes this organizational

boundary between families is confounded by all the smaller cliques connecting individuals within families (the lower level of a multilevel kinship graph).

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Yörük Turkish nomads and of White and Johansen (2005) put organizational network behavior into

correspondence with other data on emergent sub-national political leadership, cultural beliefs, practices,

material environment, historical changes such as organizational movements from region to region, and

intensive or extensive organizational ties across regions. Using predictive cohesion analysis (White 1999-

2002, 2004), they exemplify how network organization at the inter-family level can retain the same

“name” while scaling-up to organize activities at broader levels. White and Johansen (2005:204) note, for

example, that organizational boundaries for a kabile may slide from lineage to clan and sometimes to tribe

depending on variations in extent of structural endogamy. White and Houseman (2002) show that upward

scalability in network cohesion and navigability is common where most marriages are consanguineal.

The underpinnings of this modern multilevel ethnographic network approach has roots in the theoretical

vision of Leach (1954, 1961), whose ethnographies emphasized the importance of probing empirical

relations and ties within and between groups and polities in connected territories rather than relying on a

priori ideal models, typologies and classification of features (Tambiah 2002). In doing so Leach expanded

the horizons of anthropology in terms of what might constitute a system. The specificity of his approach is

consistent with the theory of social organization laid out by Leaf (2009), which incorporates empirical

economics, cultural ecology, the language-and-cultural traditions of Sapir and cognition from the

viewpoint of developmental social-psychology. Leaf (2009:86-87) also supports Leach‟s (1954:283-284)

critique as against portrayal of societies as closed systems or unitary wholes in a static equilibrium. While

ethnography focuses on narratives and beliefs, context, structure, quantities, and relationships, what it too

often missed, especially in comparisons of cases, are the complex and networked ties and structures, and

their recursive levels, that evolve over time, often beyond the range of immediate observation and

narrative description (White and Johansen 2005: Chapter 1), i.e., as can be viewed with networks. Their

network analysis glossary (pp.449-452) defines many of the structural properties that may emerge when

social network data across generations are analyzed. This current project brings what we have learned from

the network sciences into alignment with many of the kinds of data used in ethnography and comparison,

building on the work of Schweizer and White (1998), White (see references), and many others.

OVERVIEW. This project applies new technologies archived online to new and reorganized

anthropological data on (a) ethnographic KinSources-KS kinship networks, (b) foragers and nonforagers,

and (c) World-system-WS variables whose postwar impact on human communities will be studied. In

Table 1, methods, coding of new data, and selection of new ethnographic cases added to the samples are

shown in the columns, and samples in the rows. The use of Pajek network software and (Repast)

simulation is the basis of a new scientific field in anthropology that uses network analysis of community-

wide data in which genealogical networks are the scaffolding for the overlay of other ethnographic social,

economic, and political, etc. data. Predictions of organizational features for each community will be tested

using calculations of cohesive subgroups within each of the 80 ethnographic kinship datasets. Repast ABM

simulation uses White‟s (1999) random permutation of marriages in each generation to compare with

actual patterns of marriage and the simulation options allow testing the fits of marriage rules to how actual

marriages are generated. Leaf (2009:167) credits as “the best-developed general approach to describing

networks is by simulation [in comparison to a random baseline] using the network analysis of Douglas

White ….”. Peer effects of KS (kin) or WS (country level) attributes and interaction networks is a new

technology used to estimate outside and internal influences on the variables of ethnographic societies in

each sample. Modeling results and networks constructed within or between communities and for country

(WS) data will be served in ArcGIS.com open access browser maps with multiple layers and drill-down

clicks to lower level layers or individual cases. The last two columns of the table show new coding needed

to replace missing data and some new post-1965 ethnographic cases needed to bring the SCCS sample up

to date for contemporary ethnography. We select for study here samples of several kinds of

ethnographically described cases for which sufficient network and coded attribute data exist:

(1) forager societies that lack domesticated plants and animals;

(2) societies organized around “productive-wealth” (Bell 2010), wherein groups have one or more

resources that are intergenerationally transmissible; and

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(3) societies of both types as impacted by contemporary, post-WW II, globalization (two levels of study).

Table 1 Pajek Repast Simulation Cohesion Peer Effects ArcGIS.com New Codes New Ethnogr. Cases 80 KinSources

1 X X X X X 3

400 foragers2 X X (Binford & Boehm)

85 Wrld-system3 X X 2 (country data)

1294 Atlas4 X X 0

186 SCCS5 X X 0

28 1945-19656 X X X 28 (SCCS)

30 Post 19657 X X X 30

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1 http://kinsource.net/kinsrc/bin/view/KinSources archives kinship network data contributed by anthropologists. Only three KS

ethnographies remain for conversion from paper-based genealogies to e-networks for analysis with Pajek, but others will be added.

2,5 Binford‟s (2001) Constructing Frames of Reference forager database has been spreadsheeted by Boehm and Hill. Non-foragers

from the SCCS will be analyzed separately. Extensive testing of “peer effects” methods have established their validity.

3 Smith and White (1992) have postwar WS commodity flow time series in 5yr intervals; capital and migration flow will be added.

4 Murdock‟s Ethnographic Atlas (EA) in Spss format has been supplemented by newly authored installments 30-31.

5 Murdock and White‟s (1969) Standard Cross-Cultural Sample dataset on 186 societies in Spss and R formats has coded data

contributions from 80+ different authors on 2008+ variables. Citations to SCCS are now 95+/year and growing.

6 109 missing codes for 28 SCCS variables 1006-1115 will be coded for 28 SCCS societies on the world-system impacts variables

partially coded in White and Burton‟s (1985-1988) NSF 8507685 funded research on “World-Systems and Ethnological Theory.”

7 To bring the SCCS societies up to date for post-1965 societies, 30 well described post-1965 ethnographic cases will be added to

an (expanded) eSCCS and coded for EA variables and the CDC Cultural Diversity Codebook of 180 SCCS variables.

8 Given that the SCC Sample was published in 1969, the eSCCS additions to the sample will bring it up to date temporally. This

will allow study of world-system impacts on 37 well-described ethnographic cases in the contemporary post-war period.

These various samples are required by differences in availability of data. Individual cases and regions have

varying ranges and networks of relations with their environments. We previously researched, for some of

our cases, the impacts of globalization or colonization on the lifeways of the people in these societies

(Bradley, Moore, Burton and White 1990). In our societies of type (3), we look at post-war ethnographies

for societies of types (1) and (2) in order to get appropriate measures of world-systems changes and

impacts of contemporary globalization, parallel in a shorter time-frame to what Wolf (1982) did

qualitatively in studying the impacts of European expansion from 1500 forward.

Using a multi-level approach for each subsample, the overall integration of each complex, constructed

database will create new frameworks for analyzing effects of inter-societal interactions and relations

among and within societies. Each of the samples with ethnographic network data includes how people

connect intergenerationally and includes reproductive links and social constructs, such as attested

parentage and ancestry, marriage, types of normative kin behavior, and attributes of social organization

and processes, such as forms of labor, wealth, and exchange. Many cases of types (1) and (2) have kinship

network data with multiple levels, and genealogical networks on which other data are overlaid. The level

of between-family networks gives us significant bases for accounts of organizational principles that can be

compared across societies. Viewing successive generations in a network of contingent processes and

overlays provides foundations for accounts of change in social organization as against economic and other

factors. Measuring influences on structures and changes for a sample of cases will take into account

sociopolitical and geographical/material adaptive contexts and the “peer effects” of other societies.

Organizational aspects of networks include markets and exchange. Here, Bell‟s (2010) NSF proposal is

logically contiguous to this one. He defines productive wealth that can be identified by its capacity to grow

in size or value over generations. Forager societies of type (1) may have productive wealth in the form of

fertility and territory, although those lacking an ability to expand their territory to accommodate increased

population must limit their fertility and consequently fertility fails as a form of productive wealth. For

those of type (2) productive wealth in the form of herd fertility can be effective as an asset that grows in

tandem with the human population, allowing human fertility to retain full expression. Viewed in a dynamic

context, it should be clear that cattle in bridewealth should not be characterized as a “circulating fund”

(Goody 1973), circulating permanently like kula objects. Once we recognize the long term dynamics of

productive wealth, wholly new and exciting findings become possible. Our focus on network data for

kinship-linked populations and Bell‟s foundational conceptions of productive wealth allows us to view

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survival, expansion, or decline in population dynamics vis-à-vis varying specific productive wealth-assets,

constraints, and opportunities for exchange.

RESEARCH OBJECTIVES AND HYPOTHESES. Morton Fried and others talk of networks of networks

as replacing the attempted typologies of societies (bands, tribes, etc.) but this has remained up to now a

metaphor rather than a concrete proposal. Because societies are strongly shaped by the ways they are

connected, externally and internally, the patterns that connect exist on different levels need to be studied

and measured as appropriate to specific contrastive levels. The multilevel network construct allows

network analysis to connect to more global levels, drilling down on specific nodes at more local levels,

each with its own organizational networks.

Objective 1: Data construction and studies of globalizing world-systems impacts on post-WW II

ethnographic cases. Anthropology has made its impacts as an applied science, project by project, but not

often through an expanded lens and in a systematic way, with exceptions such as Wolf (1982), Scudder‟s

(2005) The Future of Large Dams and his (2005, 2010) Global Threats, Global Futures: Living with

declining living standards. The latter focuses on the global scale but also at detailed regional levels and

competes with dominant economic models, including the economics of developmental growth that as a

conception is itself part of the problem of sustainability. Outside anthropology, economics often relies on a

bankrupt theory of price equilibrium as it applies to empirical cases. Among other things we propose to

study the impact of external forces on ethnographically studied societies, and where possible in very

specific detail. PI White is currently working (as he did in NSF Award 8718769 1988-1990 with Ted

Scudder and Elizabeth Colson), but voluntarily, with a new generation of ethnographers, on long-term

qualitative assessment (1956-present) of the major impacts since relocation caused by building of the

Kariba Dam on the Gwembe Tonga, using key informant notebooks and changes in social network data.

We evaluate the effects of internal and external shocks such as the rise and fall of copper and other

commodity prices, and other regional and world economic and political fluctuations, on the unraveling of

traditional Gwembe sustainability. Our study of the Gwembe is a very detailed case study of many aspects

of the approach to the analyses proposed here.

The topic of impacts of globalization in the postwar period for well-described ethnographic cases draws

from two parts of the full project: full network analysis in the sociohistorical contexts of ethnographic case

data already compiled at KinSources for 40+ cases; and use of auxiliary data from an expanded SCCS

including contemporary ethnographic case studies and on-line interactive compilation of ethnographic

codes by contemporary ethnographers in Laura Fortunato‟s EDP website. The expanded databases will be

analyzed using multilevel METHODOLOGIES developed for general anthropological use. Below are initial

elements of a cumulative glossary of databases from which this project draws.

We will develop coded data on the applied anthropological problems documented for Scudder‟s (2005) 50

relocation studies are summarized in expert ratings, mostly of failure in problems of sustainability or

impoverishment, and for his summaries of 15 failure assessments in 24 ecosystem assessed (2009:77-105)

from the Millennial Ecosystem Assessment (2005, 2006) projects, also affected by regional consequences

of globalization processes. We may try to do so for the Cultural Survival (2010: DB:CS) projects at the

website in Fig. 1.A.

Fig. 1.A. Gmap of Cultural Survival

(2010) 100+ recent trouble spot study

cases: Gmaps extend to networks at the

global level, clicking into cases at the

local level. Live: http://bit.ly/c1funC

Fig. 1.B. This google map tracks cases

of swine flu in 2009, types of cases are

color coded, fatal cases have no dot,

clicking a region gives a more detailed

map of cases within the region.

Similarly, Wolf (1982) drills down at several hundred

ethnographically data points to analyze how commodity

exchange affected indigenous societies in the 1500-1980

period of overseas conquest and modern world-systems.

Interactive maps provide drilling down from a network

at one level (network spread of disease not shown here)

by clicking a node to see a more detailed map or a

network within that node. The upper level nodes can be

societies with organizations networks reached by a click

of a given node. Appendix 1 has a case study example.

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Google/Gmaps, GE/Kmz, Bing, and ArcGis.com maps are systems in terms of Harary‟s multilevel graph

approach if a node at one level may contain a network of relations at the next level, reached when clicking

nodes drills down in the map to a more detailed level. This is partly exemplified in the interactive maps in

Fig. 1: they are clickable, drilling down to text or images but the networks of interactions among the nodes

are missing, like how swine flu spreads from case to case (in 1.B), region to region, or how one CS trouble

case connects to another through time. Project maps use Kmz or ArcGIS.com, a free fast online zoomable

ESRI mashup and presentation browser with clickable nodes. They provide interactive data or network

display, and multilevel graphics, but we equip each level with networks that show distance, language

family connections, or interactions between the nodes. We can drill from a global level whose network

dynamics are known to samples of societies within countries or local levels to model “peer effects.”

Our research hypotheses involve long-term projects where data analysis using new software is underway.

(Hypotheses for Objective 1: Is there evidence that the effects of global economy largely promote

unsustainability for local ethnographically studied communities and their regional ecologies?) At the

global level, Smith and White (1992) and Reichardt and White (2007) developed means of identifying

network positions in the global economy using time-series country data from 1965-2005. They showed a

one-dimensional core-to-periphery hierarchy in which network positions of individual countries are

measured as they change through time due to changing patterns of commodity exchanges. World-system

zones change their character and geographic regions may differentiate over time, while the rise and fall of

individual countries in the hierarchy is accurately measured. For the international economy, we will add to

our World Bank data on commodity flows two other major inter-country time-series databases:

movement of capital and migration of people. These three time series provide a framework to analyze

problems of cultural survival, adaptation, and dynamics making use of more local case studies embedded

in the changing global system (e.g., Bradley, Moore, Burton and White 1990), for which Scudder (2005,

2010) provides a general framing of issues. A network system construction allows us to drill down from

countries as nodes, to our samples of within-country ethnographic data. We test models of “peer effects” of

the higher order network (see METHODOLOGY) on changes in the local networks: i.e., of changes for

countries affected by others and by structural changes in the world economy.

The main part of Objective 1) is to achieve the kind of specific qualitative analysis done by Wolf (1982),

but for the recent era, and with greater replicability and quantitative precision by mapping network and

ethnographic data on local communities. This allows testing effects of changes in the world-systems

network variables (international flows of commodities, capital, and migration) on the kinds of conflicts

and disasters recorded in the world-system SCCS codes for individual societies and on regional

ecosystems. Along with quantitative results, maps like those in Fig. 1 will show network processes with

significant effects, and the nodes will be color coded for benign, neutral and harmful effects. Clicking the

nodes will show networks or analytic results for ethnographic cases, more closely approximating the kinds

of results in Wolf (1982). To do so requires completion of the coding of SCCS codes 1006-1115 partially coded

for White and Burton’s (1985-88) NSF 8507685 funded research on “World-Systems and Ethnological Theory:

Standard Sample Codes and Hypotheses.” This will include (1) 28 SCCS societies for 1945-1965 ethnographies

not already coded for effects of World-systems on SCCS societies and (2) another eSCCS sample for coding, of

30 best-ethnographies published between 1966-2010 for SCCS world regions lacking cases already coded for

1945-1965. Many of these 30 will have data from KinSources.

Objective 2: Intensive ethnographic network studies samples of types 1) and 2. Combined with the

levels of study listed under (Hypotheses for Objective 1), which take us from a global country-by-country

network to the local level of individual societies and their problems of sustainability, Objective 2 adds,

where available from Kinsources, a local level inter-family network with two levels, the second being the

individual families within communities. A major objective here is to map data for local ethnographic cases

onto multilevel family networks social organization (social class, clans, etc.) and attempt to predict

individual family demographics (e.g., family size or lineage growth or decline) from aspects of the inter-

family or inter-lineage networks. The mathematics of graph theory here traces back to André Weil (1949),

and is decisive: data are mapped onto an ordinary graph of person-to-person relations but the network

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structures of kinship are computed by Weil-Harary-White p-graphs of parental links between units that

contain both families and the marriages that produce them. The 80+ anthropological datasets in our new

KinSources 2010 repository represents an intellectual movement among European and American social

anthropologists to publish their data in KinSources and to use the new software provided to do so (White

and Jorion 1992, White 2010, Hamberger, Houseman and White 2010, as reviewed in Leaf 2009:167-73), all of which was accomplished by our networks research teams over the past 18 years. These networks

make it simple to compute structural endogamy or cohesion among family units compared to the

complexities of measuring structural cohesion from standard genealogical diagrams (Hamberger et al.

2010). Adding a relatively complete kinship network with genealogical depth to the ethnographic data on a

field site adds a qualitative temporal order among generations that roughly matches historical time and

historical events. Fig. 2 shows an example. It bears repeating that Weil, in his appendix to Lévi-Strauss

(1949), already saw as a system of kinship the links between families created by marriages, which produce

newly connected inter-family ties. He surmised that kinship is structured at two levels in that exchanges

between families are ordered by rules or strategies and that roles within nuclear or extended families may

extend to inter-family roles. Recall that a network in which a node at one level may represent a network of

relations at the next level is what graph theorists Harary and Batell (1981) called a ''system''. The two level

system-view of Weil and Harary and White (1997) allows core network insights of Lévi-Strauss (see

White and Johansen 2005: Chapter 4) about the circulation of marriage exchange and cohesion that takes

measurable form in relation to empirical hypotheses.

A simple bi-component of the network, among families whose marriages form overlapping circles, defines

''structural endogamy'' (Brudner and White 1997) which can be analyzed relative to different overlaps –

migration, political office, economic exchanges, moral/religious attributes, dyadic normative role

behaviors, etc. The kinship network data set up a scaffolding for mapping specific dyadic coding (e.g., Br-

Si, Fa-So and 88 other kinds of the 10*9 directed dyads) for social behavior (DB:KB) and marriage

exchange rules (DB:MGP) that may involve family pairs in a higher level network. Overlays are done in

the conventional format of individuals as nodes at the lower network level and between families at a higher

network level. Computer programs insert dyadic labels at appropriate levels in the multilevel kinship

network datasets. Network structures for each case are analyzed as labeled multilevel graphs in the

network program Pajek (White et al 1999c). (See full set of database DB:explanations in Appendix 2.)

DB:MGPR Marriage gift and payment rules: These are 10 dimensions devised by Duran Bell to code

societies (e.g., DB:eSCCS, eEA, KinSources) that specify additional analytical “dyadic labels” for the

kinds of gift exchange and payments between members of the families of the bride and the groom. E.g.:

“Bridewealth as a negotiated or predetermined commitment to timely transfer of wealth-assets from the

husband’s family to the bride’s family as a necessary condition to the formation of a new conjugal unit.” (When

present, the inter-family directed links accompanying marriages are ones of wealth-transfers but rights over

offspring of the bride, if present, are given by a labeled link in the network in the opposite direction).

Fig. 2: The structurally endogamous kinship network core of a

Turkish nomad clan (White and Johansen 2005: 379; 76-79).

Family-node marriages are colored by generation, solid/dotted

lines descend from parents to families of sons/daughters,

respectively. The cohesive community core in Fig. 2 contrasts

with noncore members: all nodes are deleted that do not have

the bi-component property of two or more disjoint paths with

all the other nodes in the network, which leaves structurally

endogamous (cohesively overlapping) marriage cycles. Names

are given for early lineage founders, with 8 successive

generations. The nodes in these graphs can be clickable to

show family member names. Networks data for this case are at DB:KinSources:http://kinsource.net/kinsrc/bin/view/KinSources

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DB:KB KinBehavior: This data collection has three succinct pages for each of 250 EA societies (coded by

Murdock for his 1949 book on Social Structure) that provide normative kinship behavior codes for

each relative in a 10x10 role matrix of 100 dyadic kin relations for an extended family and their

affines. These are norms using the categories (-)Avoidance, (^)Respect, (+)Informality, ( )Joking and

(>)License recorded by many ethnographers following Radcliffe-Brown. The symbols can be attached

as dyadic labels (with various mathematical interpretations and analyses) for the closest relation within

the 100 dyads, if any, for the entire network. The PI has given access to the DB:KB data for Omaha

systems in the sample of kin-terminologies to anthropologists at the Museum of Natural History, NYC,

along with the Omaha kinship network data coded under one of the PI‟s earlier NSF-funded projects

from the Smithsonian collection (1880) left by Dorsey.

This project will accomplish the integration of these three types of network datasets for ethnographic

Kinsources datasets, complemented by other coded data at the societal level. These provide a framework

for a new kind of comparative ethnographic analysis that in the reconsideration of various anthropological

theories. For example, Network structures of societies with (DB:MGPR) kinship alliance claims on

potential spouses are meaningful for testing of hypotheses such as “marriage payments occur in exchange

for wealth assets” and “marriage gifts occur in exchange for intergroup alliance” (Bell 1998, 2010). Bell

(1998) showed evidence that marital exchange systems should and do involve marriage alliance gifts or

payments exchanged for wealth assets. If so, these are important to understanding multilevel networks.

Bell‟s codes (DB:MGPR) attach both to dyads – like joking, avoidance and other kinship roles in

conventional egocentric networks – and to relations between families or households.

Nadel (1957:16, 24) argues that Avoidance, Joking and License (DB:KB) have a form of orderliness

that guides behavior formulated as rules with sanctions, somewhat different from the more normative

kinship roles today of Respect or Informality. Martin (2009:7fn9) excoriates Nadel for this view but the

evidence is on Nadel‟s side for the 154 nomadic foragers where the ubiquity of sanctions is studied by

Boehm (1993, 1998, 2000, 2008a, 2008b). Avoidance and joking behaviors involve the pan-human

distributions of orderly behavior shown in the inclusion lattice of Fig. 3. Here, based on a sample of 250

societies, drilling downward in the lattice on any node with a kin dyad label entails all the types of

avoidances above it (e.g., Br/Si avoidance entails WiBrWi, HuMo, WiFa, HuFa, WiMo); societies with

each type of avoidances are listed at and below each node. This distributional spread of kinship role

behavior was identified by statistical entailment analysis (White 1999b) in order to identify how avoidance

in any one specific dyad among the 90 possible DB:KB dyads for each of the 250 cases entails avoidance

in one or more other specific dyads. Downward intersections of two types of avoidances mark their co-

occurrence. As a four-dimension partial order of inclusions this lattice has a 100% fit to all available data

(because of this, many cases of missing data values can be estimated by the ordered implications).

Fig. 3: An exact world ethnographic

lattice of kin avoidances has a four-

dimensional partial ordering of

distributions: 1) parents of Hu, Wi

(opp/same sex, within circles), 2)

siblings and siblings-in-law of Hu

and Wife (opp/same sex, in

parallelograms), 3) opposite sex

siblings & parents siblings &

parallel cousins (White 1995).

Lower types of avoidances entail

upper ones features in perfect

inclusion relations, found by

statistical entailment analysis

(White 1999b). Of the 250 societies,

names attached to each node show

each subset of avoidance relations.

are.

N=250 societies

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Consistent with the association between avoidance and joking behaviors and societies with kinship

alliances, the PI‟s prior research shows that avoidance occurs for in-laws in the context of competing

claims over whether a husband may take a wife‟s sister from the wife‟s family; or, where classificatory kin

are divided into kin and affines. Fig. 3 has corresponding ordinal dimensions that apply: 1-2) to in-laws

(dimensions 1-2, within circles, and sloping to the lower right, more common with domesticated plants and

animals and complex preindustrial economies); or 3), to cross-sex uncles, cousins, and opposite-sex

siblings (dim 3, sloping to lower left, common with foragers). The lower and rarer types of avoidance on

dim. 2 intersect (for fewer cases) with all those of dim. 3. Joking occurs between subordinates to multiple

supervisory authorities in conflict over sexuality and marriage alliance claims. A mapping of kin behavior

identifiers onto actual rather than idealized kinship networks, a first in ethnographic research, allows

analyses of the organizational structures of diverse kinship networks. Pertinent to Fig. 3, White (1999c)

also has an entailment analysis of the gender division of labor across subsistence tasks.

(Hypotheses for Objective 2: The positive effects of structural endogamy and network cohesion for

sustainability in regional and local level ethnographic network studies) If we move to ethnohistorical

time series for the co-evolution of cultivators and complex foragers and their ecosystems, with

domesticates, we can drill down from regions and language groups to ethnographic cases with complex

kinship and exchange networks. Brudner and White (1997) and used the Weil-Harary-White concept of

kinship as a two-level system to show how a farmer village in Austria is organized by a network of inter-

house nodes and their network relations, building on idea of the circulation of goods, language, and

persons. Intrafamily relations split the house-family between heirs of productive property of the family

farm and their non-heir siblings. The heirs and non-heirs division splits the village into two economic

classes, the property-owning farmers who inherit land, and those lacking wealth-producing property who

become craft or tradespersons or emigrate from the village. Weber‟s theory of conditions for the fit

between social class and economic classes is realized here using a novel network cohesion measure:

namely, the strong mutually predictive relation between a structurally endogamous core of the village

(heirs marrying endogamously with other heirs or siblings of heirs), forming an abundance of interwoven

marriage circles, contrasting with the siblings who are not or do not marry heirs and marry instead outside

the structurally endogamous circles. Here, predictions are made about emergent network organizational

positions (group or role). Emergent effects of inter-family cohesion of heirs versus non-heirs who are not

structurally endogamous are measurable with a multi-level conception of network as system. Cohesion also

predicts villagers‟ bilingual language choice.

White‟s (1997) measurable construct of structural endogamy, used by Brudner and White (1997), also

gave rise to the PI‟s (1999-2003) NSF funded research that focused on developing the measurable network

concept of structural cohesion. Developed in formal mathematical form by White and Harary (2001), this

measure is based on the minimum number of links that have to be severed in order to disconnect any

particular node, anywhere in the network (perhaps including its neighbors), from the rest of the network.

White and Harary successfully tested the time-series prediction, for a karate club that dissolves into two

factions, that the order in which ties are broken occurs with less structurally cohesive links and longer

network distance through other ties to one of the two leaders. The algorithm for network analysis of

structural cohesion was programmed and its predictiveness tested again by Moody and White (2003). To

satisfy disbelieving AJS reviewers, they replicated the finding, over and again in ten independent studies

of student friendships in American high schools, that only structural cohesion consistently predicted

student responses to a questionnaire designed to measure attachment to school. This article, in the second-

ranked sociology journal, won the ASA Outstanding Article Publication Award for 2004. Powell, White,

Koput and Owen-Smith‟s (2005) article in the first-ranked AJS sociology journal won the ASA‟s

Distinguished Scholarship Award in Economic Sociology for showing that structural cohesion predicted,

in time series, the choice of inter-interorganizational collaborations in the world biotech industry.

The example that concludes this section is the result of the PI assisting John Padgett (Padgett and

Ansell 1993) in a 10-year longitudinal study of Renaissance Florence (1282-1493) where we reconstructed

from 98,000 Florentine family tax records and other documents not only the family-named patrilineages

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but the growth or decline of family sizes and family wealth in relation to changes in the structural cohesion

of each family in the marriage ties between families. Development of the network measure of structural

cohesion comes out of White‟s (1993-1995, 1999-2002) prior work on NSF Awards 9310033 and 9978282

(1997-2002) for “Network Analysis of Kinship, Social Transmission and Exchange” and “Longitudinal

Network Studies and Predictive Cohesion Theory.”) Recall that Harary and White (1997) define a kinship

system as a genealogical network in which nodes are families and arcs converge on families of

descendants, so that a node at one level (a family) may represent a network of relations at the next level,

i.e., among the members of a lineage or individual family (White 2010). In this case, however, we moved

from marriage networks among households (as in Brudner and White 2007) and the concept of structural

endogamy (marriage bi-components) to the level of family lineages and the multiconnectivity of their ties.

Padgett (2010:374-375) used the PI‟s measure of structural cohesion: “It is not straightforward to measure

the prestige, or status value, of a lineage in the Florentine marriage market, and hence its change over time.

But social network methods can be used to assess the degree to which a family with a last name was in the

core or on the periphery of the Florentine intermarriage network. In particular, the statistical concept of

structural cohesion” defines the network core (my italics) “as the set of nodes in such deeply integrated

and hard-to-remove network positions.… Social mobility in this marriage context means movement of a

family, through its pattern of marriages with other families, into or out of the core of the city‟s marriage

network…. Movement into or out of the core can be interpreted as movement of families into or out of the

Florentine marriage elite.” Movement into or out of the weaker structurally endogamous bi-component,

notes Padgett, represents “movement of families into or out of the Florentine marriage market itself.”

The analysis of these data changes the understanding of Florentine social, economic, and political

history. For Padgett (2010:391): “What difference did these mobility and marriage dynamics make for the

demographic success and failure of Florentine families? … The overall picture is that three factors drove

the demographic growth and decline of Florentine families during the Renaissance: wealth, political

factional success, and being within the marriage-network core” defined by White‟s “structural cohesion”

(Moody and White (2003).3

Fig. 4: Growth or decline in Florentine family size during the Renaissance, 1282-1317 (period 1) through 1458–80

(period 7) is predicted by structural cohesion (White and Harary 2001). Graphs at the left show predictive regression

coefficients, and, at the right, the statistical significance of the predictions (N=1,697 distinct families). The Black

Guelf faction, after winning the struggle in 1301 (X) between Blacks and Whites, were endogamous for a century, and

the anti-Chiompi faction, after putting down the Chiompi revolt in 1378 (X) between Blacks and Whites, were also

endogamous for a century. Average household wealth predicts a small fraction of the variance in family growth or

decline, compared to marriage-network structural cohesion between families. The negative effect of family size

(larger families, lower growth in size) has less effect than household wealth, except for period 4 (1379-1403).

The Florentine structural cohesion, wealth, and winning political faction predictions of growth/decline in

family size reach as high as R2 =0.42 and R

2 =0.29 in periods 4 and 6 in Fig. 4, but these graphs show high

regression coefficients and significance for inter-family cohesion in periods 4-6 prior to the Medici

takeover of the state and as the power of winning factions declines.

These and related network analyses (Leaf 2009) recently spurred a cottage industry among

anthropologists who as a result of new formalisms (White and Jorion 1992, White 2010, Hamberger,

Houseman, and White 2010) prepared and posted their own network datasets at the KinSources data

3 The proof presented in White and Harary (2001) is that structural cohesion also measures the number of disjoint paths between every pair of

nodes in well-defined subsets of the networks, called structurally cohesive blocks.

X X

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repository and coded and posted as well the published data of earlier anthropologists as networks of

genealogical relations. This growing collection (now beyond 80 datasets) offers a grounded approach to

the study of social organization, not only in the study of emergent structures of inter-family kinship

networks, but in the fact that other types of ethnographic data – economic, political, religious, etc. – can be

embedded in these networks. This takes us well beyond Martin‟s (2009:2) recitation of Simmel‟s

(1950:10) conception of how networks simply “crystallize” into structures: “The large systems and super-

individual organizations that customarily come to mind when we think of society are nothing but

immediate interactions that occur among men constantly, every minute, but that have become crystallized

as permanent fields, as autonomous phenomena. As they crystallize, they attain their own existence and

their own laws, and may even confront spontaneous interaction itself.” Crystallization is a good metaphor,

but for the Austrian farmers and Florentine elites, among the mechanisms of class crystallization are

structural endogamy and structural cohesion as measurable causal effects, i.e., from metaphor to

measurement. Not only class but a great variety of other forms of social organizations are crystallized in

and by cohesive components. Aydιnlι nomad structural endogamy, for example, “crystallizes” clan

members who travel together versus those who immigrate to towns.

Objective 3: intensive ethnographic network studies in the special case of type 2, samples of foragers

without domesticates. These samples also have many Kinsources datasets so we may also focus on

problems of endogamy and cohesion but we are also equipped with datasets constructed for foragers.

(Hypotheses for Objective 3: The relative effects of structural endogamy and network cohesion for

sustainability in local level ethnographic network studies of foragers) A key finding from the PI‟s

dissertation (1969) on foragers and agriculturalists in Indian North America came from the hypothesis that

pre-state societies with complex cooperative technology had cross-cutting organizational cohesion while

those with simple individualistic technology had segmentary or spanning tree organizational networks (low

structural cohesion). Foragers will not have marriage payments for wealth transfers as defined above by

Bell, but may have what he defines as marriage alliances and gift-giving as a means of kinship integration,

expected with more complex food-collection technologies. European-induced fur trade, however, induces

extremely segmentary-individualist spanning tree organization lacking structural cohesion.

Binford (2001:316), armed with a massive forager database, is wary: he sees working with

undimensionalized data as so complex that it leads analysts to mirror their own cognitive organization.

Instead, after assembling the raw data for comparative or time-series study, he advocates a theoretical

choice of major dimensions and appropriate variables to do principal components analysis that

dimensionalizes the data (2001:45-47). In spite of major efforts, the results are very slim (Hill 2002) with

Binford‟s approach. He might have better success with the discrete structure analyses of Fig. 2 and Fig. 3

that are error-free unique representations of actual data (Galois or concept lattices and networks).

For the simpler forager project here we use Binford‟s and other datasets but rather than follow

Binford‟s methods, we adopt analytical strategies with a higher success rate in prior studies. We use cross-

case analysis of “peer effects” of common history and inferences drawn from intraregional similarities

(e.g., diffusion or borrowing), along with discrete structure analysis (White et al. 1986) with zero error, to

free the analyses of relationships among variables from Galton‟s problem of historical non-independence.

A special subsample focuses also on cases with extensive Kinsources data and is able to analyze kinship

and organizational network variables, testing the structural cohesion hypotheses as well.

For the evolution of polities structural cohesion hypotheses would posit that chiefdoms do not evolve

without linkage through exchange systems between cohesive lineages with internal ranking and that states

do not evolve without larger cross-cutting cohesive building-blocks for officeholding or subterritorial

structure. Studies such as Wright‟s (2009) archaeological dataset on chiefdoms and early states may help.

RESOLVING THE PROBLEMATICS OF NETWORKS AND CAUSAL PATHWAYS. The central

methodology of this project addresses issues of how network variables such as cohesion affect change and

the resilience, and sustainability of human populations, viewed through comparisons of ethnographic field

data and sociohistorical studies. Galton posed the network problem of “peer effects” – e.g., of interactions

with proximal societies or of sharing common history – as one that requires network studies. Such effects

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can operate from higher multi-level networks such as those of globalization or from more direct peer

effects of societies on one another. Measuring these effects requires capturing of network “peer effects”

(Evans, et al. 1992, Manski 1995, 2007) through instrumental variables (IVs) measured as separate

independent variables in a regression. Adding these as estimates of peer effects to a regression equation

(called “peer effects” OLS) for a dependent variable solves the problem posed by Galton in that the effects

of other independent variables –attributes of each case– can now be validly estimated. Eff and Dow (2009)

provide an open source [R] “peer effects” OLS program with two matrices for potential network effects,

one for the potential effects between cases of spatial proximity and one for potential effects of language

phylogeny similarities. Our graduate researchers, advisors, and 45 UCI undergraduates tested and

demonstrated the validity of their method. Sample results for intra-society combine with network “peer

effects” are combined in Fig. 5 from thirteen student studies put together in a causal graph (Pearl 2000),

although effects of network cohesion and other network variables can be added to such graphs only as a

result of the research proposed herein. In Fig. 5, for demonstration purposes, the labeled green, yellow and

red nodes indicate layers of the dependent variables, and arrows to those nodes from others labeled for the

independent (predictor) variables from “peer effects” OLS. Sizes of nodes for the dependent variables

reflect the extent to which neighbors‟ characteristics (e.g., for societies with evil eye) are predictive by

spatial proximity as an IV (instrumental variable: the larger nodes have distance IVs significant at p

<0.001). I.e., multilevel network embeddings are included in these results. Because the dependent

variables were chosen for this diagram to produce a graph that is connected, we can see that many

independent variables predict several dependent variables. There is only one causal triple, however, where

variable A (here: milking) predicts B (money) and C (evil eye) and B predicts C as well. In this case both

the A→C and B→C regression slopes predicting C have significant positive coefficients while the A→ (-)B

regression slope is significantly negative. (In Fig. 5, red dotted lines show negative effects and solid black

lines show positive influences) The IVs (instrumental variables) for the first two regression slopes are very

significant for regional clusters (i.e., for pastoral societies versus monetized societies) while the significant

IV language effects for milking animals and money are negative, which reflects the fact that these two

types of economies come from significantly different language families.

Fig. 5: A triangular regression effect within a causal graph. Two mutually exclusive causes of “Evil eye” (pastoral

milked animals v245 vs. monetized economies v155) are found, significant at p<0.0003 for their language network

dissimilarity. Solid black lines show positive and dotted red lines negative influences. For societal attributes,

regression slopes predicting Evil eye are positive for Money (0.25) and for Milked animals (1.01); both are sources of

wealth, with significant pvalues. Spatial transmission effects for Evil eye are significant at pvalue < 0. 0.00000015.

The slope for Milked animals predicting Money is -0.42 (i.e., negative, with pvalue < .06).

METHODOLOGIES: Resolving questions of multiple causal relations given network effects.

General problems solved with “peer effects” methods. This project uses ethnographic survey data in

relation to network data but discussion of comparative research must begin the need to disregard previous

cross-cultural results as unreliable when they ignore multiple pathways of connection across societies.

Anthropologists are well aware of the fallacy of statistical inferences from correlations that do not

incorporate “peer effects.” Simple first-order correlations or “HRAF-type” cross-cultural studies produce

A

B

C Causal triple: A C, B C, A (-)B

Cau

Is the dispersion of “peer effect”

causalities of positive value for

sustainabilities? Or a vulnerability

in a globalized market that would

suggest the need for regulative

policies?

Research questions:

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highly unreliable results. Ignoring all concern with how societies are historically and empirically

connected had the effect of inflating of significant tests, which produces type I "false positive” errors in

drawing conclusions and type II "false negative” errors in testing whether correlations replicate for

different samples of cases. “Peer effects” methods, however, the approach followed here, incorporate

network effects (of proximal interactions, network cohesion, patterns of exchange networks, etc.) and in

doing so, avoid these problems.

Specific problems solved within a peer-effect “causal graphs” approach. Solution of triangular (the

example in Fig. 5) and similar regression results illustrate the usefulness of Pearl‟s (2009) causal analysis.

For example, because both milked animals and money affect evil eye (an expression of envy in witchcraft

beliefs) but the milked animals → money regression coefficient is also known, the indirect effect of milked

animals on evil eye can be estimated and combined with the direct effect to explain how and why the first-

order correlation of milked animals → evil eye is weaker than it might be otherwise. Thus the

direct+indirect effect is coefAC + (coefAB * coefBC) = 1.01 + (-0.42 - 0.25) = 1.01 – 0.17 = 0.86, i.e., a

weaker coefficient for milked animals → evil eye than given in the direct coefficient. Many complex

causal models can be estimated but not when some of the predictors have a confounding rather than

additive effect on the others.

Solving a new problem with the ordinal regression estimates. One defect of the Eff and Dow (2009)

software, which we have corrected in our regression estimation procedures, is that it uses OLS (linear)

regression for ordinal variables. Dow (2008) provides complicated [R] software for regression with ordinal

dependent variables. Our reprogramming (White and White 2010a, 2010b) of Eff and Dow is much

simpler: we allow changing numbers of ordinal ranks (1, 2, 3, etc.) to interval measures that re-express the

frequencies as best-approximations to normal bell-shaped distributions, a standard solution (Mosteller and

Tukey 1977) for regression models with ordinal data (i.e., approximating “probit” ordinal regression)

Significant problems solved with the “multiple imputation” of missing data. Studies that use multiple

regression suffer from serious errors when the data that are missing on cases for different pairs of

independent-dependent variables. Most analyses rely on “pairwise” deletion of cases, where one of a pair

of variables has missing data, or on “listwise” deletion of entire cases where data are missing on any of the

independent variables, which may reduce sample size considerably. Eff and Dow‟s (2009) software, which

provides a foundation for solving the “peer effects” problem, solves the missing data problem by

probabilistic multiple imputation. This involves making N copies of the data, including missing data; then

filling in missing data independently from expected probabilities of possible values using regressions from

a separate set of completely coded high-quality variables; then using those imputed values independently

for each copy of the full original data to analyze regression results from the each imputed dataset; and

finally combining these independent sets of statistical results with the widely-used efficient and unbiased

Rubin‟s (1987) formuli.

Consistency of methods. For each of our ethnographic samples (world-system efffects, complex societies,

foragers) and types of multiple networks (inter-family/individual family networks, exchange networks, and

labeled dyadic behavior-type networks), causal graphs like Fig. 3 can be drawn for network “peer effects”

and effects of variables coded for individual societies, taking “peer effects” into account. Language and

distance matrices are used to help solve for multilevel networks effects (“solving Galton‟s problem”) for

all three types of samples, but additional IVs are given for the post WW II sample (i.e., “peer effects” of

globalization networks), and for the samples that include KinSources network data for many of the

ethnographic cases. All of the methods used in this project work together, as an ensemble, to study effects

between levels and between variables when: 1) variables are sorted into network “peer effects” estimated

as IVs (instrumental variables) and properties of individual ethnographic cases, and 2) regression models

are restricted so as not to violate counterfactuals: e.g., “evil eye” beliefs cannot cause the existence of

money but money can influence the occurrence of “evil eye,” operating through envy and amulets.

ArcGIS.com. Our project uses a new (2010) free fast online zoomable ESRI mashup and presentation

browser with clickable nodes (drilling down to local networks or zooming the map to local scales) which

provides an interactive data display and choice of multilevel graphics along with data models in a DBMS.

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“This approach is very powerful because it means that this type of observational data can be intermingled with

all the other GIS layers and analytics applied to it so we can build new kinds of applications. This Web 2.0

concept will support many applications (i.e. citizen science, community feedback and crowd sourcing, etc.).

Fundamentally, all three of these changes are enabled because of Moore's law. Computing is getting faster, pipes

are getting bigger, storage is getting cheaper, etc. It is just the systematic and steady application of Moore's law. I

have seen it through my entire career” (Dangermond 2010, founder and President of ESRI, for GIS science).

ESRI‟s ArcGIS.com is time-aware and zooms into resolutions of 5 meters when appropriate maps are

available. Free satellite data are available back to 1985 to fill in ecological terrain and vegetation mappings

for our regions of study. Thus we can zoom from world maps to networks at any level to detailed maps and

diagrams from field site publications.

SUBSTANTIVE CONCLUSIONS. John Levi Martin‟s (2009) chapter "The Escape from Comparability

and the Genesis of Influence Structures" emphasizes the way network structures become crucial building

blocks for other structures (like our multilevel networks), so that "inequality of choice" to use his phrase

shapes the very vertical and horizontal organizations that will differ or be the same. He makes the point

that there is not now and will never be an understanding of networks without a larger view of structure and

a range of other phenomena both at the local and larger levels. Both endogenous and exogenous factors

need to be considered in explaining similarities and differences in and across societies. Anthropology

needs interactive, embedded models of causality and various levels, including a bird's eye view of

networks both within and across societies, in order to appropriately use and explicate our findings by in

effect placing ethnographic materials in larger historical, geographical (e.g., with time-aware ArcGIS.com

geospatial embeddings) and environmental frames.

Inter-family network structure, marital exchange relations on the network of individuals and families (Bell

1998, 2010), and role behavior as distributed on that network, have not been systematically compiled on

any ethnographic case study except in a qualitative or narrative sense. We anticipate that the studies of this

project will provide major breakthroughs in anthropology. The difference between the network approach

taken here and Wolf‟s (1982) magisterial work is that instead of stopping at a work of textual scholarship,

we also put together the actual networks involved in the kinds of influence processes that Wolf and many

others study (e.g., Appadurai 1997, Wengrow 2008, Leaf 2009). Post-1965 ethnographic works of textual

scholarship reviewed for this project, however, show compatibility with multiple level organizational

network analysis at roughly the same proportion as those that employ „classical‟ ethnographic approaches.

When this project is finished anthropology will have a whole new set of network organizational data for

well described ethnographic cases and a set of provisional causal variables in one or more causal variable

networks and “peer effects” for each tentative relationship. The causalities will be directed toward

organizational and multilevel network features of human societies for three different samples: (1) foragers

lacking neolithic domesticates, (2) societies with neolithic domesticates, and (3) societies of either type

where post-WW II ethnographic data shows causal effects from contemporary globalization variables. All

these relationships will be open to further research by other investigators, who will have access to the full

databases and the appropriate softwares. All these relationships will be viewable in multilevel websites

that show “peer effect” networks among cases (societies in samples 1 and 2 and, additionally, countries in

sample 3): spatial distances rescales for their “peer effects” on different classes of variables, similarly for

the phylogenetic trees of common language ancestries, and the time-dependent global commodity trading,

capital flows, and human migration networks that have differential effects on local societies in different

parts and positional niches in the world, ecologically, politically, religiously, and economically.

WORK PLANS. ASSEMBLY OR PURCHASE OF NEW CASE STUDY REFERENCES. All sources are available

for the 186 societies SCCS at UC Irvine, UCSD, and the SAR in Santa Fe, facilitating coding work at the

Santa Fe Institute and our Southern California UC campuses. The 30 new ethnographies for the SCCS

ethnographies are already purchased for coding in year one. For 30 Kinsources datasets and 30 or more are

needed and similarly for the appropriate post-1965 eSCCS ethnographies. DATA AND CODING (a)

Additional Ethnographic Samples and Codes. PI White in year one will select for inclusion in the eSCCS

Extended SCCS 30 cases from among the most descriptive ethnographies from 1966 forward for world

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regions lacking cases already coded in the SCCS. He and Co-PI Duran Bell and the Graduate GSR, over

the next two years, will (a1) read and code these ethnographies for the SCCS Cultural Diversity (CDiv)

codebook (CDC) of 180 variables, the EA codebook of 72 variables and the World-system SCCS codes

1006-1115; (a2) they will also code the 28 SCCS ethnographic cases for the 1945-1965 period that were

not previously coded for the SCCS World-system codes; (a3) in year two they will code the 30 Kinsources

societies already in the EA for the CDiv and World-system codes. (b) Network analysis of the 30

KinSources databases will be done by White, initially helping the Graduate GSR in year one to perform

more of these analyses in later years and to detect or measure in year two such network variables as (b1)

generational levels, male and female descent lines, demographics of males, females, married/unmarried,

and family sizes by time periods; (b2) structurally cohesive cores of male and female descent lines,

structurally endogamous components for the marriage network, and compositions of the cohesive units, by

consanguineal or nonconsanguineal marriages; and (b3) frequencies of marriage types and overlaps in

consanguinity of marriages. (c) Global economy codes, 1950-2010. Archival retrieval in five year

increments will be done in year two by the Graduate GSR for inter-country (c1) capital flows, and (c2)

international migration. (d) Cultural Survival codes, 1973-2010. The Graduate GSR and undergraduates

will study the Scudder (2005), Millennial Ecosystem Assessment (2005), and 100+ Cultural Survival

applied anthropology projects, coordinated out of Harvard, and code the kinds of sustainability problems,

time, and geocoordinates of each. SOFTWARE DEVELOPMENT. Since the last NSF 2010 submittal, the [R]

programmer has done the programming for the SCCS R package ready for use this fall and winter in UCI

undergraduate classes. The proposed funding will pay for a PeerEffects R package generalization in year

one of the previous package for any rectangular database of variables and spatially located sites, with

additional network interaction matrices optional. This also involves computerized indices of variables and

coding labels for individual variables. A student or researcher will be able to choose a dependent variables

and select a large number of potential variables limited by eliminating counterfactuals and variables with

too few cases or too little variation for study. MATHEMATICAL DEVELOPMENT OF ALGORITHMS FOR

CAUSAL GRAPHS for the PeerEffects R package. The [R] programmer will work in year two with the

leading expert on the mathematical algorithms for solvable estimates of causal graph structures from

regression analyses of multiple dependent variables with network “peer effects.” These algorithms also

detect regression coefficient networks that differ from those like Fig. 5 in that they are unsolvable. In year

two and three these algorithms will be incorporated into the PeerEffects R package. LINKAGE BETWEEN

THE ETHNOGRAPHIC CASES WITH COMPUTERIZED KINSHIP NETWORKS AND THE DYADIC LINK

LABELING FOR KINSHIP BEHAVIOR AND MARRIAGE ALLIANCE ROLES will be a collaboration in year two

between the PI and the programmer, with help from Co-PI Bell. FULL DATA ANALYSIS will be done where

possible in year two and continued in year three. This will include free time-aware ArcGIS MAPPING,

ArcGIS.com, ArcGIS desktop and server software through a UCI license and a campus GIS expert to help

with and advise on the work. EXTENSIVE Causal Graph MAPPING will be done in year three by creating

algorithms that automate “peer effects” causal graph results for any number of dependent variables and

then build composite causal graph networks far beyond the complexity of Fig. 5. PUBLICATION OF

RESULTS AS ARTICLES begins in year two (we have several underway) and PUBLICATION OF RESULTS IN

BOOKS continued in year three, as collaborations between the PI, graduate GSR, and the programmer. The

causal graph expert will have publication rights in appropriate areas of contribution.

PROJECT SCIENTISTS AND THEIR ROLES. Individually, and as a diverse group of researchers, our

team members are interested in the integration of anthropology‟s resources on-line and integrative tests of

theory, including top-down and bottom-up effects in nested networks of interacting societies and variables

in the three multilevel datasets discussed. The team includes expertise in each of the types of societies and

problems investigated, and consists of outstanding researchers in each aspect relevant to the project. GSR

T. Oztan is an incoming Mathematical Behavioral Science grad student with 800 GREs in math with PI

White as his advisor. White was a cofounder of the NSF-funded CCCCC at Pittsburgh that created the

SCCS and founded two electronic journals, including World Cultures. He is the SCCS co-author, and has

published extensively in each of the fields covered in the proposal, notably in anthropological and

sociological network analysis and formal modeling as well as comparative and historical research. He was

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awarded the Distinguished Senior Social Scientist Alexander von Humboldt two-year prize where he

collaborated with University of Cologne anthropologists with research interests coordinate with this

project. His network research won top publication prizes in sociology in Economic Sociology (Powell and

White… 2005) and mathematical sociology (Moody and White 2003) and was featured in complex

systems journals (Reichardt and White 2007). His paper on structural endogamy (Brudner and White

1997) was nominated for a special reprint issue of the journal Theory and Society and remains highly

influential. Co-PI Bell is both an anthropologist and economist and a distinguished contributor to the

anthropological study of systems of wealth, exchange, and marriage. He is experienced in coding SCCS

societies and creating new codebooks. Our programmer S. White is an ABD at UC Irvine, has published

seven articles in leading computer science and complexity venues and since 1997 has had eight continuous

leading computer system jobs in industry, including senior scientist. Our econometrician K. Chalak is

providing the mathematical and algorithmic modeling for causal graph analysis. Unpaid consultants A. Eff

and M. Dow provided our initial open source [R] for “peer effects” regression software, published the core

prototype of our “peer effects” multilevel software in anthropology journals and publish extensively in

mathematical anthropology. Informal advisor J. Pearl is considered the father of machine learning and the

father of causal graphs. Advisor H. White is distinguished in “peer effects” multilevel regression in

econometrics and computer science. Other experts in anthropological aspects of other problem areas

(archaeology, historical time series, ecology, comparative ethnographic study of foragers, etc.) are all

outstanding and highly published in their fields. UC London and Santa Fe Institute Post-doc L. Fortunato,

developed one of the first ethnographic on-line codebook-database for contemporary field studies and

specializes in evolutionary and biological anthropology and the study of mating. Elsewhere, Argonne

National Labs simulations programmer M. Altaweel has contributed network simulation routines within

the widely used Repast (2010) package that duplicate White (1999): “the best general approach to

describing networks [against random baseline] simulation” (Leaf 2009:167).

PUBLIC CONTRIBUTIONS. CONTRIBUTIONS TO EDUCATION AND OPEN ACCESS

EDUCATIONAL RESOURCES. Anthropology is badly in need of new resources for on-line qualitative,

network, and spreadsheet methods, classroom or home instruction, and techniques that facilitate field

research. The PI is experienced in this area and was involved in a number of precursors to the present

project. PUBLICATIONS will focus on a series of substantive and methodological articles followed by a

book on Peer effects and causal analysis of ethnographically studied societies in global context.

CONTRIBUTIONS IN TRAINING GRADUATE AND UNDERGRADUATE STUDENTS. The incoming

UCI graduate student supported through TAships with the PI and Santa Fe Institute summer funding has an

800 in math GREs and an excellent background in mathematical and socio-historical anthropology and

sociology. He is capable of learning large project research management and of publication in virtually any

and every area of this project, and is seen as long-term collaborator successor to the PI in the broad areas

of research in the project. Two cohorts of undergraduates are now trained in the approaches of this

research, both in network analysis and “peer effects” multilevel modeling of ethnographic problems,

contributing to the kinds of results in Fig. 5. Two more cohorts will be trained in academic years 2010-

2011-2012. REU awards will be sought for two UCI undergraduates both to participate in the research and

to visit for three summer weeks, with the PI and the Graduate GSR. The PI continues to teach at the

graduate level in the Mathematical Behavioral Science program and the Social Networks Programs where

he continues to recruit and produce PhDs in these fields and in the anthropological sciences. REPOSITORY

CONTRIBUTIONS. The project will produce eLorak, where the full collection of datasets, codebooks,

software, coursework and instructional systems for anthropology described in this project are housed. Our

SCCS R package is now mostly complete, and when generalized for other datasets, we will produce the

PeerEffects R package. The PI will host eLorak@UCI: The eLearning Open Repository for

Anthropological Knowledge for open access software “peer effects” analyses of databases combining

networks-and-spreadsheet data, and for network construction and analysis, graphical methods, student use,

and instructions for research. Initial databases will include KinSources, eEA, eSCCS, CFR, and ACS.