main narrative: networks and multilevel anthropology nsf
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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
8 (eSCCS)
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
14
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.
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