Notes on the introduction to “Theories of Group Behavior”, Brian Mullen, George R. Goethals (Eds.) (1987). New York: Springer-Verlag

Emile Durkheim – proponent of using the group rather than the individual as the basic unit of analysis. Believed individuals tell us nothing about groups.

Floyd Allport – proponent of using the individual as the basic unit of analysis. The individual is more “real” than the group.

Early social psychologists saw groups as being analogous to an individual in their wants, needs, and behaviors.
LeBon (1895/1960)
Boodin (1913)
McDougall (1920)
Jung (1922) – collective unconscious

Ostrom & Pryor (chapter 9) – saw correspondence bewteen social structures and cognitive structures.
Wagner (chapter 7) – said fundamental memory of group is equal to interpersonal processes.

American research has tended to focus on the individual as the basic unit of analysis, rather than a Gestalt, wholistic approach.

Donald Campbell (1958)
Entativity is the “realness” of a group. This measurement helps decide whether the group or the individual is the proper unit of analysis. High entativity is a result of high proximity, high similarity, common fate, “good form“.

Robinson (1981) was similar to Campbell in judging the realness of a group. He looked for same size, and same activities over time, or covariation.

References

Allport, F.H. (1920). Social Psychology, Boston: Houghton-Mifflin.

Allport, G.W. (1968). The historical background of modern social psychology. In

G. Lindzey & E. Aronson (Eds.), Handbook of social psychology (2nd ed.) Reading, MA: Addison-Wesley.

Boodin, J.E. (1913). The existence of social minds, American Journal of Sociology, 19, 1-47.

Campbell, D.T. (1958). Common fate, similarity and other indices of aggregates of persons as social entities. Behavioral Science, 3, 14-25

Durkheim, E. (1938). The rules of sociological method. Glencoe, IL: Free Press.
Jung, C.G. (1922). Collected papers on analytic psychology (2nd edition). London: Bailliere, Tindall & Cox.

LeBon, G. (1895/1960). The Crowd. New York: Viking.

McDougall, W. (1920). The group mind. Cambridge: Cambridge University Press.

Robinson, M. (1981). The identity of human social groups. Behavioral Science, 26, 114-129.

Wegner, D.M., Giuliano, T., & Hertel, P.T. (1984). Cognitive interdependence in close relationships. In W.J. Ickes (Ed.), Compatible and incompatible relationships. New York: Springer-Verlag.

Notes on the introduction to “Networks of Collective Action”, by Edward O. Laumann and Franz U. Pappi. 1976, Academic Press, New York, NY

p.6
Structural Analysis

Network symmetry vs. asymmetry
This connotes whether a given social relationship is reciprocated or not, i.e. whether or not the directionality of a social link goes both ways.

The absence of a relationship (or link) is as important to the network as the presence of a relationship.

p.7
Relationship-specific structures
Some relationships structures are more fundamental than others. The presence of these structures makes the presence of other types of relationship structures more likely. In other words, some relationship structures have a profounder effect on the overall interconnectedness of a group than others. By relationship structure, we mean any axis along which people are said to be connected.

Distance-generating mechanisms
A distance-generating mechanism is any method of effecting the level of connectedness between two nodes.

Structural crystallization
The formation of strong intractable correlations among some structural relationships. This may lead to structural contradiction if the crystallization of some relationships in the network also leads to the suppression of other structural relationships. The persistent strength of some relationships may mean the perpetual weakness of others.

p.9
Models of Integration

A perfect market is an economist’s ideal of the wants and needs of individuals integrating to achieve an equilibrium price and level of production and consumption of goods. The view of the market is of individual atoms interacting to form the aggregate collective behavior.

p.10
The concept of social choice is similar to the perfect market idea. The differences are:
1) component actors are more intentional in their desire to influence the collective
2) component actors have greater or lesser amounts of influence – not all are equal in their ability to influence the collective.
3) groups of component actors may act in concert to influence the collective – they are not always acting as self-interested individuals.

Coercive and administered models break society into two groups – the elite, and the people. Some groups of individuals make decisions for the collective. These models try to minimize the influence of the average individual actor. For an extreme example, fascism.

Social choice offers the widest variety of combinations of elite and collective behavior.

p.19
Relationships among nodes can be either self-reported or empirically observed. These techniques are liable to produce different results.

p.20
When grouping a collection of individuals into a single node on a network (in order to talk about higher-order properties), relationships between these sub-groups can be deduced from the relationships between the individuals within the sub-groups.

Network Analysis attempts to explain he behavior of nodes and of the whole system by appealing to specific features of interconnectedness among nodes. The more-connected nodes are more influential to the whole system than nodes with fewer connections.

p.21
Graph Theory is the mathematical study of network behavior resulting from its interconnectedness. Mostly analyzes one type of relationship at a time.

p.22
Blockmodeling blocks groups of nodes based on structural equivalence. This de-emphasizes interconnectedness compared to graph theory. Also, unlike graph theory, it encourages views of multiple types of relationships in one analysis.

Data Reduction Problems
Given the abundance of data, there are two general strategies for representing the data simply.

Cluster analysis forms sets of nodes by clustering them together.

Multidimensional analysis uses spactial solutions. It maps interconnectedness using proximity in space, usually Euclidean space. Easier to visualize than cluster analysis.

p.23
The number of possible pairwise relationships in a network is given by:
n(n-1) for asymmetrical networks
n(n-1)/2 for symmetrical networks

Overview/Thesis Statement:
Originally intended to connect people separated by large distances, telecommunications technology today is finding compelling new uses connecting people already in close physical proximity. From the Flash Mob phenomenon to Pac-Manhattan, groups of people are subverting the intended uses of technology to their own, often performative, ends.

I will demonstrate that current trends in popular culture and commercial technology have created a new medium for artistic expression: people. Like paint, human behavior can be applied in broad strokes using contemporary theories of information, social networks, group psychology, and distributed computation. My project will turn people into pixels, and turn a moving crowd into a moving image.

Rationale:
This project comes at a time when physical space is renegotiating its relationship to information space. Semioticians, and postmodernists have long bemoaned the separation of an object and its meaning. It has become almost clich頴o mention it in civilized society. But never has this gap been as explicit as with today?s combination of mobile devices and their hypertext capabilities: location-specific information is accessible from cell-phones and PDA?s at the push of a button.

New media artists have begun to exploit this in the creation of new meanings by overlaying physical space with a layer of geographically-positioned information space, whether factual or fantastical. This can be most obviously seen in projects such as Pac-Manhattan, where a video game board is superimposed onto the streets of New York City, and in Blinkenlights, where the fa硤e of an office building was converted into the world?s largest computer display. The separation of information and object can further be seen in museum tours, where handheld devices provide detailed and dynamically updatable information about exhibit pieces directly in front of the viewer. There is something magical in the often-skewed overlap between the information and physical spaces.

My involvement as one of the creators of Pac-Manhattan exposed me to the immense interest, both among cultural institutions, as well as among ordinary people across the globe, in this juxtaposition between information and physical space. Although this thesis, as an idea, pre-dates Pac-Manhattan, and certainly doesn?t have the same pop appeal, it has nevertheless been heavily influenced and altered by this experience.

There seems to be a vested interest on the part of the general public to be performers. This is reflected in the current fascination with ?reality television?, blogs, and might even be argued to be a factor in the massive success of ?first-person shooter? video games in comparison to games with other vantage points.

One particularly foul rainy day in 1999, while living in Seattle, I took a bus from work to my neighborhood, walked a few blocks towards my apartment, and noticed an eerie quiet on the streets. I suddenly realized, through the fog, that I was caught exactly half-way between a line of fully-armored riot police and a crowd of protesters participating in the Seattle riots. Needless to say, I was summarily attacked with many canisters of tear gas and compression bombs, and found myself instinctively running to the side of the protestors, with whom I had never had much sympathy.

As I joined the group of protesters, my belief in their fundamental ignorance on the issues at hand was somewhat confirmed, but quickly became irrelevant. The power of the group, and the level of each individual?s emotional involvement (including my own), was undeniable. Protests are a form of performance, as are other particularly urban types of entertainment, such as Flash Mobs, bike parades, and the like. The organizers of the protests and other group activities are often very well organized and informed, and the individuals who participate must feel interested and engaged in what they are doing in order for the performance to be successful.

How the individual?s involvement and the organizer?s master schemes are mediated by telecommunications technology is a subject relevant not only to protests. Reality television, business organizations, economics, and art could all benefit from manipulating the way people are connected on the ground.

Goals:
My goal is not to solve all the group activity challenges of the 21st century. Rather, I would like to explore the potential of telecommunications technology to mediate the carefully crafted interactions between individuals. Specifically, I would like to use the voluntary participation of individual people as pixels in an image formed by the self-organization of the individuals into a crowd. The interpersonal interactions and the technology?s ability to mediate them, will presumably lead to dynamically adjustable aggregate behavior of the group. The audience for this work will be the participants themselves.

Core Features, Media, Technology:
Participants will hold a portable device which emits bright colored light. These devices will communicate via radio transceivers. The details of what the participants do with the devices are yet to be worked out.

Success Measures:
The pixels, which are held in the hands, or placed on the hats of the participants, must be properly arranged to form a moving picture. Success of this project will be easily gauged by the fidelity of this picture and by subjective measures of the participants? involvement.

This project necessitates that people participate. This means, that people must feel actively engaged and involved in the creative process. Furthermore, they must feel a collective responsibility to form the image correctly. Participants will be interviewed after the event and their feelings and impressions will be collected in an informal manner.

Of course, one aspect of group activity is that people sometimes leave the group, whether in frustration, or as a result of some other reason. Although for this thesis, I will be attempting to maintain everyone?s interest, the frustration of members of the group could, in a future experiment, be seen as a successful result.

Concept Diagram:
Coming soon?..

Man?s behavior has always been mediated by tools. Fossils found in Oldowan, Tanzania show that at least 2.5 million years ago, Homo habilis, ?the skillful man?, used stone tools for food preparation1. From this we can conclude that the ability to crush food had definite consequences on his hunting and gathering behaviors. Since hunting and gathering were of prime importance to his survival, we can further conclude that this tool significantly affected habilis? social behaviors.

It is reasonable to assume that were our tools and technologies to be dug out of the ground two million years from now, future archaeologists would similarly accept our tools as evidence of the inner workings of our social life and culture. Presumably, they would attempt to recreate, and perhaps re-enact, our behaviors and social mores based on the capabilities and limitations surrounding the surviving tools, just as we have done with habilis.

So how do modern tools and technologies mediate behavior? Do tools extend our mental capabilities2 or are they merely an externalization of innate mental proclivities ? a sort of physical appendage, performing the brain?s outsourced tasks in order to lighten the cognitive load within our overstuffed craniums3? The scope of these questions is obviously far too broad to be answered with any semblance of certainty in any reasonable amount of time. But it is clear that an understanding of human behavior is inextricably linked to an appreciation of man?s tools.

Whether or not the tool makes the man, or the man makes the tool, there is nonetheless a tool-man interdependency at the most fundamental level of human behavior4. Today this connection, between man and tool, is ripe for exploration as an artistic medium.

Whereas once upon a time, the relationship between any Homo habilis and his simple stone tool was a private matter just between the two of them, today, modern telecommunications technology, as a set of tools, has effectively ripped apart the sanctity of that intimate bond and opened it up to a potentially infinite number of relationships with other tools and with other men5.

These networked devices, and the people networked through them, can be connected in any of a vast array of network topologies6. The old-fashioned one-to-one communication relationship, as found in a telephone conversation, has been relegated to its proper place alongside its neglected siblings, one-to-many, and many-to-many. In addition to these ?cardinalities?, the overall shape of a network helps define its topology, which in turn defines its set of possible behaviors.

For example, in a star-shaped topology, a central node acts as a sort of hub for the rest, serving as the connecting link between any two other nodes on the network. Behavior-wise, all messages must pass through this hub, which must then pass each message on to its destination node. This can lead, in turn, to a very small but standardized set of behaviors.

Conversely, in a mesh network, all nodes communicate through a tangled web of connections. This leads to varied and often-complex behavior where messages must sometimes hop around across many nodes before eventually finding their proper destination. In a fully-meshed network, each node is directly connected to all other nodes, leading to very computationally-expensive, redundant, but reliable systems of communication. In an ad-hoc mesh network, the connections are usually indirect, unreliable, and short-lived. Thus the overall behavior of the ad-hoc network must be able to rapidly adapt to, or gloss over, any sudden changes in topology7. This has obvious consequences for the quality and value of any information shared in this way.

The study of the properties inherent in the various incarnations of networks has traditionally been approached from the vantage point of only one of the many related fields. Mathematicians, economists, and sociologists have all independently developed tools for network analysis. Mathematicians speak in terms of ?graph theory? to quantify the computations inherent in different networks8. Economists, often influenced by Thomas Schelling?s famous ?tipping model?9, base their models on interactions between networks of consumers and producers, each with independent, ?greedy?, behaviors. Sociologists tend to perform analyses of the average strengths of social bonds between two people or two groups, and qualify the emergent properties that result10.

More recently, with the growing popularity of the science of emergence, and the work on Complex Adaptive Systems, done in the 1990?s at the Santa Fe Institute11, scientists, economists, and social scientists have begun to study networks as abstractions and only then to apply the resultant disembodied theories to the details of their respective fields. The result has been some progress towards the development of a unified theory of networks12.

The fact that networks of people have different behaviors than individuals was not lost on early social psychologists, including Freud. In his ?Group Psychology and The Analysis of the Ego?, Freud critiqued contemporary theories of group behavior, and laid out the case for the psychoanalytic perspective, noting along the way the self-similarity between the group and the individual, the structural differences between crowds and organizations, and the emergent properties of groups as opposed to individuals13. All of these are classic properties of a complex adaptive system.

The organizational structure of groups, or the lack thereof, and its relationship to the ability of a group to steer itself, or be steered, towards a desired objective is the intensely scrutinized subjects of sociological, operational, and marketing research studies14. The effects of the message inherent in the contemporary media15, technology, and telecommunications network topologies upon the organizational structure and objectives of groups are starting to be reflected in the groups? behaviors.

In American culture, the spontaneous organization of people and the organization of group behavior mediated by technology have begun to be seen as a sort of populist entertainment17. This is nothing new: America, as land of the dispossessed, has traditionally favored entertainment for the common people over the traditional notion of high art and culture. After all, within the speed of only a few generations, we have transformed ourselves from being smug apostates of European high culture into the proponents of a new land of Reality Television and insta-celebrity stalking.

But, no longer are groups of people required to be in close physical proximity or close temporal intervals in order to be organized, and to retain the behavioral properties that this organization entails16. With the advent of modern telecommunications technology and mass media, the behavior of groups can be steered and micromanaged towards objectives through the careful control of the flow of information between people.

Creating new and unexpected connections among such organized groups is enjoyed online on services like Friendster, or MySpace18, but with the increasingly blurry division between life onscreen and off, the entertainment in the ad-hoc wiring and re-wiring of these mesh networks is enjoyed in the physical world as well as in the virtual19.

For example, pieces such as Golan Levin?s, ?Telesymphony?20, in which he created an orchestra ensemble out of an audience?s cell phone ringers; and the Blinkenlights project in Germany21, where the facade of an office building was transformed into the world?s largest computer display; exhibit the ability of modern telecommunications technology to map virtual networks onto physical space and to transform online simulations of life into second-order simulations transduced back in the real world22.

However, these works use a static physical structure ? be it a building or a stationary seated audience ? and map onto it a dynamic mesh networked piece of music or video. What remains to be seen, and what seems like the next logical step, is to use a spontaneously organized ad-hoc group of people, organizationally mediated through ad-hoc networking technology, whose artistic value at any given moment is a stylized reflection of its own organizational structure. In other words, turning a moving crowd into a moving image: this would be the ultimate in modern simulation science!

Works Cited

1. http://lithiccastinglab.com/gallery-pages/oldowanstonetools.htm
2. and http://anthro.palomar.edu/homo/homo_3.htm
3. Ong, Walter J. (1988). ?Orality and Literacy?. New York, NY: Methuen.
4. Clark, Andy (2003). ?Natural Born Cyborgs?. New York, NY: Oxford University Press.
5. Norman, D. A. (1993). ?Things that make us smart?. Reading, MA: Addision-Wesley.
6. http://www.webopedia.com/quick_ref/topologies.asp
7. http://english.ttu.edu/kairos/1.2/coverweb/Cogdill/many.html
8. Hartsfield, N. (1990). ?Pearls in Graph Theory?. San Diego, CA: Academic Press.
9. Schelling, Thomas C. (1978). ?Micromotives and Macrobehavior?. New York, NY: W.W. Norton & Company.
10. Laumann, Edward O. (1976). ?Networks of Collective Action?. New York, NY: Academic Press.
11. Waldrop, M. (1992). ?Complexity: the Emerging Science at the Edge of Ordered Chaos?. Carmichael, CA: Touchstone Books.
12. Watts, Duncan J. (1999). ?Small Worlds: The Dynamics of Networks Between Order and Randomness?. Princeton, NJ: Princeton University Press
13. Freud, Sigmund. (1956). ?Group Psychology and the Analysis of the Ego?. New York, NY: W. W. Norton & Co.
14. Barrett, John H. (1970). ?Individual Goals and Organizational Objectives?. Ann Arbor, MI: Braun-Brumfield
15. McLuhan, Eric (ed.) (1995). ?Essential McLuhan?. New York, NY: Basic Books.
16. Rheingold, Howard (2002). ?Smart Mobs?. Cambridge, MA: Basic Books.
17. http://www.wired.com/news/culture/0,1284,59518,00.html
18. http://www.friendster.com and http://www.myspace.com
19. Gabler, Neal (1998). ?Life: The Movie?. New York, NY: Vintage Books.
20. http://www.flong.com/telesymphony/
21. http://www.blinkenlights.de/
22. Baudrillard, Jean (1995). ?Simulacra and Simulation?. Ann Arbor, MI: University of Michigan Press
23.

Websites of interest:

http://www.southcoasttoday.com/daily/03-03/03-04-03/c03sp089.htm
Guinness world record for largest human logo in Portugal

http://www.typotheque.com/articles/pixel_people.html
Scott Givens Olympic Stadium Stunts

http://newyorkmetro.com/nymetro/arts/art/reviews/4485/
Review of artist Andreas Gurskey

http://www.worldchanging.com/archives/001450.html
Natalie Jeremijenko interview about protest technology

http://www.osa.ceu.hu/galeria/spartakiad/online/
Bodies in Formation online gallery

http://www.ingentaconnect.com/content/sage/bod/2003/00000009/00000002/art00001
Politics of Gymnastics: Mass Gymnastics Displays Under Communism in Central and Eastern Europe

Thesis Context/Research Outline

Concept Description
Mobile Moving Mass
New form of visual media
Group dynamics affect visual image
Mass
Density
Interconnectedness
Flow of information

Historical Events
Situationist International
Exploration of space
Derive ? wandering
American celebrity culture
Uncultured frontier mentality
Shun high art, focus on common man
Ordinary people as celebrities
Reality television
Relationships as entertainment
Life: the movie
Science of Emergence
New metaphor for life
Interactive
Object-oriented
Cultural change in many fields
Physics
Biology
Ecology
Computer science
Social sciences
Economics
Simulation
Agent-based interactions
Bringing simulation off the screen

Related Projects
Flash Mobs
Stadium Entertainment
Korean Mass Gymnastics
Pac-Manhattan
Blinkenlights

Technology
Global Positioning System
Self-organizing networks
Ad-hoc Location Routing
Portable Personal Technology
Bluetooth cell phones

People as Pixels

http://www.typotheque.com/articles/pixel_people.html
- very specific article on examples of design with “People as Pixels”

Media Theory

http://homepage.newschool.edu/~wilder/MediaElision.html
“Media Elision”, an article by Carol Wilder of the New School on the blurring of news and entertainment. Mentions Neil Postman, McLuhan, Beaudrillard – the usual.

http://proxy.arts.uci.edu/~nideffer/Tvc/section3/11.Tvc.v9.sect3.Grindstaff.html
“Trashy or Transgressive: Reality TV and the Politics of Social Control” – gives a good overview of media theory surrounding infotainment and reality tv. Mentions Adorno, Horkheimer, Postman, etc.

http://vv.arts.ucla.edu/teaching/classes/401_s02/connections/Saras_connections.htm
Sara Diamond’s Connections. Contains links to participatory media, designs, spectacle, enabling technologies, and all that.

http://darkwing.uoregon.edu/~jlesage/Juliafolder/nonfictionTV/nonfictiontvbibliog.htm
Bibliography of useful books on Nonfiction TV.

Prior Art

http://newyorkmetro.com/nymetro/arts/art/reviews/4485/
- review of Andreas Gursky’s minimalist photos of buildings and crowds as “Pixel Visionary”

http://www.flong.com/telesymphony/
- Golan Levin’s “Telesymphony”. He used the audience’s cell phones as instruments in a ring-tone orchestra.

http://www.halfbakery.com/idea/World_20record_20display_20panel
- another waste of an idea on halfbakery.com. They suggest making the worlds largest display panel made of people holding adjustable CMY color cards

*Autoimage*
Autoimage is a self-organizing self-portrait.

Based on a model of how the brain learns to recognize faces, each pixel in an Autoimage portrait learns where it belongs in the image as a whole based on the activity of the pixels around it.

Autoimage brings the age-old medium of the portrait into the era of the network. Self-organizing portraits respond to the modern metaphor of life as the sum of complex interactions among many small independent agents.

*Description*
Background: This project has its heritage in the study of networks, graph theory, complex adaptive systems, and place of art in the age of mass reproduction.

Complex adaptive systems (CAS) is a relatively new field of study (early 80’s) that attempts to unite findings in science with observations in nature. It appears that many chemical, biological, social, economic, and physical processes have remarkable similarities. CAS is the study of those likenesses.

One of the major revolutions of thought to come from CAS are the inter-related ideas of agent-based interaction and emergence. Many complex systems (e.g., the brain) can be thought of as independently acting agents (e.g. neurons) whose collective behavior (e.g. the mind) is somehow greater than the sum of the individual parts.

Autoimage is an artistic representation of this new network metaphor for life that is spreading from physics and computer science, where it originated, to most other disciplines.

*Audience*
People interested in networks, the philosophy of science, emergence, cultural perspectives, network theory, new metaphors for life, and of course art.

*User Scenario*
User is instructed to use camera phone to take a picture of themselves and send it in an email to the Autoimage engine. Autoimage then assembles a self-organizing portrait of the user on the plasma screen. Afterwards, the user can go to the Autoimage website and see their self-organizing image linked for viewing at their leisure.

*Technical System Description*
Camera phone takes snapshot and sends it to Autoimage email address. Perl script extracts the photo from the email, posts a link on the website, and launches the Java applet which displays the self-organizing autoimage on the plasma screen.

Click on the updated schematic to view the connection of the input and output layers – two matrices – into the PIC. There is a sensing layer, consisting of Hall-effect sensors, and an output layer, comprised of LEDs indicating the latest moves on the board.

Input comes in from the board via the matrix of Hall effect sensors. These are multiplexed in order to minimize power consumption. Multiplexing is made possible by the 3 to 8 decoder. The PIC is programmed to have the decoder innervate one row of sensors at a time. Once a row is innervated, the shift register relays any high signals in that row (signifying the placement of a magnet above the sensor) to the Pic. The Pic then decodes this message to update the position of the board in its memory, and loops through the remaining rows.

Output happens in much the same way. The Pic is programmed to have the decoder innervate one row of LEDs at a time. Once the row is innervated, the shift register is loaded with that rows data in order to sink current on the lines connected to those LEDs that should be on. The decoder/shift register system loops through the remaining rows.

Not included in this diagram is the connection to the Cobox Micro. The chip displayed here, the 16F84 has run out of i/o pins, and it’s necessary to move to a giant like the 18f452 – more on that next time.