Appendices to Social Networks, Cognition and Culture

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Appendix to Social Networks, Cognition and Culture

Social Networks, Cognition and Culture

Appendices to Social Networks, Cognition and Culture, Douglas R. White. 2010. 
Blackwell Companion to Handbook of Cognitive Anthropology. Edited by 
David Kronenfeld, Giovanni Bennardo, Victor De Munch, and Michael Fischer

Methods of Cohesive blocking

Appendix 1: The three-world problem and models of mind, social networks, cognition and culture

Introduction

The three-world problem. In some fields it is beneficial for social scientists to consider philosophical problems that affect research. For networks and cognition, Popper’s (1959) critique of logical positivism is a good place to start. Popper argued “that positive evidence (‘confirmation’) and the inductive method (the search for rules that lead from limited observations to the establishment of valid generalizations) are not at the heart of science” (Schweizer 1998:44). Loosely stated, Popper's view is that science is not defined by method, and that "appropriate methods" do not guarantee results. “Rather, negative evidence (‘falsification’) and deduction are at the core…. In the spirit of critical rationalism, when choosing between rival hypotheses, we should always select the one that has higher information content by being more general and thus more challenging due to its wider range of application. And we should [tentatively] keep the one that has survived serious attempts at falsification and therefore has proven less false than its rival.” My goal here, in proposing a complex heuristic model -- of physical brain, behavior, and material culture in relation to immaterial mind and culture --, is to open up new problems in cognitive anthropology, bringing to the study of culture and cognition potentially new theories and approaches from the field of social networks.

Material and immaterial worlds

The three-world problem, as debated by Popper and Eccles (1977), is one that also confronts cognitive anthropology:

World 1: The physical world (and human brain and behavior in that world).
World 2: Mental activity and human consciousness.
World 3: Objective culture, "which is the creation of World 2 but takes on its own distinct and permanent existence."

My concern in drawing Fig. 1 is with how these three worlds are related. How is it possible for “Objective” culture to take on a distinct and durable existence? Arguments between scientists such as neurophysiologist Damasio (2007) and philosophers like Gluck (2007) are seemingly irreconcilable. Their critiques of one another fail to resolve the problems of Popper and Eccles. My proposal is that we may come to better understand human behavior, cognition, and culture if we separate elements such as action, thought and culture into material components and immaterial patterns, using different aspects of networks of relations, such as relational pattern, abstraction from material to immaterial (patterns of thought, patterns of culture), and material causation.

Figure 1: A heuristic model of the three-world problem

Fig. 1. The three-world problem at two levels: Columns: Individual and Social, Thought and Relations. Upper ovals: Cognition and mental expression; vs. Networks and behavioral expression. Lower ovals: Brain and organismic behavior relative to the environment; vs. Culture, group and role. Arrows: suggest cylcles such as B↔C (reciprocal) and A→B→C→D→A (directed).

Fig. 1 is a heuristic model that expresses how the three-worlds might be related. Key features are that: unlike the brain, immaterial A=mind is not directly causal to observable C=social behavior; and, material B=brain is not causally connected to immaterial D=Culture. Direct causalities do reciprocate between observable C=social behavior and material B=brain. The upper ovals in the figure involve what individuals “do” in terms of thinking (internally) vs. behaving (externally) as persons embedded in social networks. The lower ovals involve B=brain (with its organism-environment interactions) vs. the nonmaterial elements of D=culture. The columns suggest that elements A and B play out at the individual level (mind and brain) while C and D do so at the social level (networks, culture).

Among the heuristically useful features of this framework is that habitual behaviors need not be mediated by mind if coordinated more directly by the brain, while mind-behavior connections require mediation by brain. In my ethnographic examples [main paper], Pul Eliyan sidedness behavior is usually habitual, sometimes strategic, and occasionally requires a conscious computation when encountering strangers from the local area. Chuukese residential choice is a conscious decision, as are Karate Club members' decisions in their dilemma of choosing alternate subgroup membership.

Heuristically, it is useful to observe that -- just as A=mind expresses itself through language -- external C=social behavior expresses itself through gesture, movement, facial expression, and action (White and Johansen 2006). These constructs depend on what ego and alter observe to be the motivators of social behavior: what is assumed social actors' perceptions, memories, and the contexts of interaction. Like material culture, items of C=social behavior have meaning in a network of meanings; external behavior is constructed as social relations. This conception views how we construct intended or observed relations as elements of social networks.

The main driver of human thought and behavior, in this heuristic model, is the A→B→C→D→A cycle among material (B,C) and nonmaterial (A...D) elements that include mind and culture. The network oval C evokes the idea that episodic behaviors are internally (experientially) and externally perceptual and can be represented as network flows with an episodically temporal ordering in behavior that draws on restructured and weakly encoded memory of episodic experience. Solid and dashed ovals encircle material and immaterial elements, respectively, with causality between material items, pattern projections between thought and culture, and abstractions between material/immaterial counterparts: “mind studies brain, behavior models culture.”

Cognition might emerge partly from a distillation of C=behavioral practices mediated by brain and memory, not necessarily consistent but also from D=culture from roles and cohesively organized groups that can be cognized. The mind draws out patterns and conclusions by abstraction, a process of synthesis by mind that draws on both physical and symbolic substrates. There is a reflexive cognitive D-A link for thinking about culture (but no directed A-D link since culture does not “think”).

See: A faulty model of mind

Conclusions

Two of the most basic concepts relevant to social sciences have been those of group and role. In this paper I have tried to move the status of these concepts up from the descriptive level (or middle range theoretical constructs) to a level of measurement in networks of interactions where more formal and thus measurable theoretical concepts can be tested at a causal level, exemplified by how cultural emergence can be explained and predicted as consensus at the level of cohesive group emergent out of interaction, and predictive consequences of levels of structural cohesion in groups and role structures.

References

  • Popper, Karl. 1959. The Logic of Scientific Discovery. New York: Basic Books.
  • Schweizer, Thomas. 1998. Epistemology: The Nature and Validation of Anthropological Knowledge. In Handbook of Methods in Cultural Anthropology, H.R. Bernard, Ed., Walnut Creek: Altamira Press. Pp. 39-84.

Appendix 2: Activating the cohesive.blocks algorithm to produce Fig. 2

The following R programs need to be installed

library(igraph)
library(digest)
library(RSQLite)
#Paste into R for execution (data for Fig. 2 are on-line and copy/paste are all that’s needed).

Producing Fig. 2 (b), same as Fig. 3

#GNU cohesive.blocks() (c) Written by Peter McMahan 2007 posted in Cohesive blocking.
require(igraph)
require(digest)
require(RSQLite)
source("http://www.charting1968.net/CohesiveBlocks.R")
source("http://intersci.ss.uci.edu/wiki/Vlado/random20nodes36edges.R") #- 20 node graph with 38 random edges, and 3 nonrandom edges - just click, copy and paste into R.
g <- read.graph(file="http://intersci.ss.uci.edu/wiki/Vlado/random20nodes36edges.net",format="pajek")
gBlocks <- cohesive.blocks(g,verbose=TRUE,cutsetHeuristic=TRUE) #option for these data
V(gBlocks)$label <- V(gBlocks)$id #labels for vertices
max.cohesion(gBlocks) #print maximum cohesion values for each vertex
lapply(gBlocks$blocks,function(i){V(gBlocks)[i]$id}) #labels
#plot.bgraph(gBlocks,layout=layout.spring,vertex.size=14) #spring embedding
plot.bgraph(gBlocks,layout=layout.kamada.kawai,vertex.size=14) #kamada.kawai
write.pajek.bgraph(gBlocks,file="gBlocks")
Right
Output: 
[1] 2 3 3 3 3 2 3 3 2 2 2 3 2 2 3 3 2 1 3 2
1 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12"
     [13] "v13" "v14" "v15" "v16" "v17" "v18" "v19" "v20"
2 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12"
     [13] "v13" "v14" "v15" "v16" "v17" "v19" "v20"
3 [1] "v2" "v3" "v4" "v7" "v15" "v16" "v19"
4 [1] "v3" "v5" "v8" "v12"

Producing Fig. 2 (a)

require(igraph)
require(digest)
require(RSQLite)
source("http://www.charting1968.net/CohesiveBlocks.R")
source("http://intersci.ss.uci.edu/wiki//Vlado/20nodes38randomedges.net.R") # - 20 node graph with 38 random edges - just click, copy and paste into R. 
#GNU cohesive.blocks() (c) Written by Peter McMahan 2007 posted in October.
require(igraph)
require(digest)
require(RSQLite)		
source("http://www.charting1968.net/CohesiveBlocks.R")
g <- read.graph(file="http://intersci.ss.uci.edu/wiki/Vlado/20nodes38randomedges.net", format="pajek")
gBlocks  <- cohesive.blocks(g,verbose=TRUE,cutsetHeuristic=TRUE)  ##gBlocks <- cohesive.blocks(g)                           
V(gBlocks)$label <- V(gBlocks)$id
max.cohesion <- igraph:::maxcohesion
max.cohesion(gBlocks) 
lapply(gBlocks$blocks,function(i){V(gBlocks)[i]$id})
#plot.bgraph(gBlocks,layout=layout.spring,vertex.size=14) 
plot.bgraph(gBlocks,layout=layout.kamada.kawai,vertex.size=14) 
write.pajek.bgraph(gBlocks,file="gBlocks")
Right
Output:
[1] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 3 3 3
1 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14"
[15] "v15" "v16" "v17" "v18" "v19" "v20"
2 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14"
[15] "v15" "v16" "v18" "v19" "v20"
3 [1] "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14" "v15"
[15] "v18" "v19" "v20"

San Juan Sur

Social Nets Cog-May2010 29pp aFIg5.png

Preparation to run in R

Install R and run
Load required package: igraph
Load required package: digest
Load required package: RSQLite
Load required package: DBI

Run in R by copy and paste

Copy an paste into R workspace:

source("http://intersci.ss.uci.edu/wiki/Vlado/MW_SanJuanSurNet.R")

This community network has 75 nodes and an average density of 4 to 5. It takes less than a minute to run. This version of the *.R code uses cutsetHeuristic=FALSE to avoid an algorithm error. The routine calls the data from http://intersci.ss.uci.edu/wiki/Vlado/SanJuanSur.net

If the cohesion of each node does not print, repeat the analysis as follows.

cb <- cohesive.blocks(g)
max.cohesion(cb)
 [1] 2 2 2 2 2 2 3 2 3 2 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
[39] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 1 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

Output

In addition to the SanJuanSur.net graph, the program prints the cohesion tree as follows:

Branch 0: 1-cohesive; 75(74) vertices; 1 sub-branches; 
Branch 1: 2-cohesive; 74(60) vertices; 2 sub-branches;
Branch 2: 2-cohesive; 6(0) vertices; 0 sub-branches;
Branch 3: 2-cohesive; 56(55) vertices; 1 sub-branches;
Branch 4: 3-cohesive; 55(0) vertices; 0 sub-branches;

and prints the max cohesion of each vertex:

 [1] 2 2 2 2 2 2 3 2 3 2 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
[39] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 1 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

References

Output

Click to enlarge
 [1] 2 3 3 3 3 2 3 3 2 2 2 3 2 2 3 3 2 1 3 2
1 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12"
  "v13" "v14" "v15" "v16" "v17" "v18" "v19" "v20"
2 [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12"
  "v13" "v14" "v15" "v16" "v17" "v19" "v20"
3 [1] "v2" "v3" "v4" "v7" "v15" "v16" "v19"
4 [1] "v3" "v5" "v8" "v12"

Footnotes

1

Because these patterns are constructed in the mind by interactions of neural networks, our mind has a perception of durability and continuity in our experiences, chunks of which will persist in various aspects of memory and mental schemata even as our attention is intermittently shifting from one experience to another.

2

Because their collaborations contribute value to reputation, for example, biotech organizations (Powell et al. 2005) self-report their new collaborative contracts annually in their trade journal; Ayd?n? nomads proudly report their marriages and ancestors to ethnographers (White and Johansen 2006); network surveys may constrain and limit responses but also ask respondents to report on personal experience as well as experiential observations. Dyadic self-other reporting may provide estimates of the reliability of such reports.

3

Other network predictors of cultural consensus include common ancestry, common history, common educational experience or exposure to the same media sources such as specific TV and radio sources. These are “vertical” rather than the “horizontal” influences of structurally cohesive groups. There are also “oblique transmission” influences such as effects of common types of prestigious figures that inspire learned agreement.

4

These ties show an extended family structure in SJS with a common "consensual role" pattern in the visiting behaviors for kin. Removing symmetric ties for visiting among kin gives 46 remaining asymmetric visiting ties that form a connected but partially ordered visiting hierarchy differing significantly from random rearrangements of ties (p=.00000000000003). This is evidence of the salience of a P-graph structure (individual members of couples and their siblings linked to parental couples) for the kinship network (see following section).

5

Fig. 3 has 54 red nodes and 20 green-blue nodes (one node is obscured) and has nine green-blue nodes with social class ratings below 46 on the scale 0-66 in Figure 2 of Loomis and McKinney (1956: 407).

6

The cohesive blocks in the biotech industry were unnamed, and it is doubtful that the friendship groups were named because they cut across grade levels and partitioned groups within grade levels.

7

Cohen (1969, Cohen et al. 1968) showed evidence of modes of reasoning using relational reasoning rather than analytical categories of nonverbal tests but such evidence has been largely ignored.

8

For SJS the predictions from one variable (cohesion) to many independent variables(multiple aspects of consensus, among judges of middle class position, for upper to middle vs. lower-middle and lower class ranking of individuals, and for leadership roles, etc.) is more likely causal than the multiple regression prediction (many predictors, one dependent variable) .

9

The concept of role models with overlaps of alters is that every occupant of a role X interacting with role Y has some overlap with co-occupants and common alters and thus a partially shared perceptual environment. Reichardt and White (2007) give an example of a role-overlap model for the 2000 global economy. A dynamical model of overlapping roles computes changes in role-overlap structure in successive time periods.

10

It may make more sense for the study of culture to ground the notion of systemic cohesion not by "Institutions" but by concepts for more concretely cohesive entities such as "Organizations." This specifies more concrete linkages, objectives, and adaptive redesign (Leaf 2008). Then in the domain of adaptive cognition (Posner 2001) and language there are two concrete adaptive levels for conceptual networks with concrete linkages that are either tighter through logical construction or looser through Ashby’s principle of adaptive variability, where collaborative cognition occurs through the natural and constructed environment, artifacts and observables (Hutchins 1991).