This paper is a summary of an Aspen Institute sponsored in-depth roundtable session, written from the perspective of one informed conference observer (Bollier). The participants are leading thinkers in the many complex areas this paper covers (economics, systems theory, human behavior, human futures, information technology evolution, etc) and are listed on page 57. A selection of their key insights shared in the paper are listed below:

A "push" economy is geared towards mass production, anticipating consumer demand, and routing resources to the right place at the right time, to create standardized and mass produced products. By contrast, a "pull" economy is based on open, flexible production platforms that are used to orchestrate a broad range of resources. Instead of producing standardized products, "pull" model companies are demand-driven, and assemble products in customized ways that serve specialized or local needs, usually using "rapid" or "on the fly" processes.

Several global corporations are moving towards "pull" methods, and away from "push" models; ie., Toyota, Dell, Cisco, Li & Fung. These companies employ different variations of Value Network models, that share information about overall network performance and best practices for serving specialized needs, among hundreds or even thousands of partner companies that make up the network. This creates an intra-network knowledge commons. Some companies also work closely with Open Source Software projects, thereby expanding their "pull" network, and expanding their knowledge commons into a broader Open Commons via Open Source Software project contributions. Thus, "pull" business models also tend to be Network Value-Increasing, and Commons-based business models as well.

"Pull" models can also be platforms for creating "increasing returns dynamics." This is due to "pull" models being based around loose and flexible networks that are already configured to scale as growth occurs. So, growth does not incur the huge overhead costs in administration that "push" models must contend with. Pull platform key characteristics include modular and loosely-coupled networks, open channels that better harness the passion and commitment of innovation communities. "Pull" platforms also will tend to influence public policy with regards to education and innovation, as more companies tend to gravitate towards the "pull" models.

The areas where "push" models tend to succeed in business are in areas where people do not know what they want, and prefer to shop from pre-made selections (Ikea, Home Depot). However, there are even "pull" models to found here, in the form of user-driven innovation, such as mountain biking, extreme skiing, hot rodding, etc. In these pro-amateur niches, customers don't necessarily know what they want, but do want to be a participant in the "pull" network that creates the product.

How do you tax a product that is made in 23 different countries? "Pull" models are going to change the way that governments create policy as more companies gravitate toward them. This will influence laws about intellectual property, education, taxation and more.

"Pull" economies are not just centered around finding creative ways to "outsource/offshore jobs" away from one place and to the places where "labor" is "cheaper". Successful "pull" models have encouraged and aided "insourcing", where more jobs are created, for instance in the United States by "foreign sources (a total of 7 million cited by this paper), than are out sourced (a total of 600,000+ cited by this paper). This is because pull models seek out, not just the "cheapest" labor, but the best ways to add value to the production networks. So, they can scale to many participants around the world, regardless of local labor costs, to find the best participants needed for specific specialized productions.

The social dynamics of "pull" models are highly centered around creating relationships of trust, sharing knowledge, and close cooperation among network participants. In "pull" models, non-market value creation (tacit knowledge, intangible value) is generally steered towards a commons-based model. A commons is used as a "collective governance regime for managing shared resources sustainably and equitably." Many of these commons are made possible by networked information technologies (the internet).

Bollier suggests that "if online commons are going to be useful to business, companies will need to do more work to develop protocols for identity and reputation management". This is because the use of the commons is based around trust. It also due to the need for ways to measure qualitative value in intangible assets beyond money, like knowledge, individual performance and value multiplication, and network wide performance/value multiplication.

Roundtable participants also noted that "pull" models will pose challenges to current education regimes that are centered around training people to participate in "push" economies. One of the participants mentions that " Computers, software tools, and Internet resources make possible some radically new styles of learning. By using pull-based systems, students can function much like businesses in the pull environment: They can access resources they don't control and put themselves into flows of activity, rather than just building inventories of static, objectified "knowledge."

The Institutional Analysis and Development (IAD) framework developed by the Ostroms and their colleagues at Indiana University provides a foundation for studying a multitude of theories, models, and predictions of public choice behaviors in different systems of governance and organization.

Frameworks define the action arena to which it would be applied; the resulting patterns of interactions and outcomes, and the means of evaluating those outcomes.

A framework is a general language about how varying rules, physical and material conditions, and attributes of a community affect the structure of action arenas, the incentives for actors, and resulting outcomes.

Action arenas include an action situation and the actors in that situation.

An action situation includes:

• Participants
• Positions
• Outcomes
• Action-outcome linkages
• Control that participants exercise
• Information
• Cost and benefits of outcomes

Actors (individual or corporate) involve:

• Resources brought to the situation
• Values assigned to states of the world
• Methods for dealing with knowledge and information
• Selection processes for courses of action

Analysts can make strong predictions in tightly constrained situations of complete information: overuse of resources in an open commons where the actors do not share access to collective choice arenas.

Results are not as clear in situations where actors are embedded in communities with norms of fairness and conservation as well as the ability to communicate with each other.

Evaluation criteria can include a range of values for categories such as the following:

• Economic efficiency
• Fiscal equity among actors
• Redistributional equity (e.g., policies to care for poorer individuals
• Accountability
• Conformance to a general morality
• Adaptability to change

The IAD framework has been applied to various domains to make predictions of resulting behaviors in field settings. Examples of successful application include:

• Police services
• Urban public services in general
• Common-pool resources: these were studied in laboratory as well as field settings. The IAD framework was used to create a theory of behavior. Communication of participants affects behavior: if no communication was permitted, the results approximated that of non-cooperative game theory. Communication led to different, more positive, results.
• The IAD framework was used to develop extensive databases coding common-pool resources and diverse property regimes.

Metcalfe's Law implies that the value of a communications network scales with the square of the number of peers that it connects (N*(N-1)) where N is the number of network access points. Reed's Law states that communications networks that connect groups (as opposed to peers) create value that scales exponentially with network size (based on the number (2^N-N-1) of non-trivial subsets that can be formed from N*(N-1) connected groups. Reed calls these networks Group-Forming Networks or GFNs.

Reed poses the question of what exactly is value in this setting? Value in a network that provides a service to users (e.g., broadcast networks, amazon.com, content providers) is the value of that service to the customer. A communications network connects peers and value is the "value of potential connectivity for transactions". For example, customers in a telecommunications network find value in the possibility of connecting with 911. Thus, potential connectivity provides the option of transacting. GFN's provide the ability to create and join groups and the value that is provided is the ability to affiliate groups. For example, a business with a supply network has the potential of affiliating with other supply networks. Reed concludes that using Sarnoff, Metcalfe, and Reed's law, there are three categories of value that networks can provide: (1) broadcast transactions which are linear value aimed at individual users (i.e., services), (2) peer transactions which is square value from the facilitation of peer transactions, and (3) GFN transactions which are the exponential value from facilitating group affiliation. As the Internet has developed, there has been a scale-driven value shift of value based on content, followed by value based on size of membership, to value based on the best facilitation of group affiliation. Reed does not imply that any of these values replaces another, rather than all are a part of Internet value.

Reed makes a very important point from this analysis. First, in real networks, the total price that is paid for transactions can only grow linearly because it is typically the case that consumers of value have money and attention that scale linearly with N. Reed calls this a saturation process and notes that if affects all types of value which implies that all three types value compete for the same resources. Once N grows sufficiently large, peer transactions will create more value for unit of network than broadcast transactions, and that GFN transactions will create more value per unit of network than either broadcast or peer transactions. Reed concludes that GFN transactions will out-compete the other categories in attention and return on investment.

NASCAR race draft line formations and dissolutions can serve as an example for cooperation and competition in other social domains.

NASCAR drivers form and re-form into draft lines to take advantage of aerodynamic phenomena to gain an edge in competitions with other drivers who have basically equivalent automotive equipment. 'Draft partnerships' are necessary to get ahead; however, they must be abandoned strategically to win. Within a race, at high speeds, there is an ever-shifting pattern of cooperation and competition among rivals. This is a reflection of an important, desirable American trait: how to compete by doing a good job of cooperating. Essential to success in drafting are trust, acquired over time, and an effective communication support structure through networks of representatives (spotters). Complexity theory, social network analysis, and game theory are used to analyze the behaviors. The lessons are applied in other social domains.

Communication via radio with intermediaries acting as agents (i.e., spotters) who negotiate with the intermediaries for other drivers is essential. Negotiations and deals need to be made rapidly. While deals may be cut before the race, most partnering emerges on the fly in consultation with spotters who have a larger picture of what's happening in the race. Interpersonal communication, dealmaking, and diplomatic skills may be as important as driving technique.

Partnerships are formed with trusted collaborators/competitors. Reputations are gained over time. Betrayals are remembered for years.

Veterans rarely want to partner with 'rookies'. Newcomers need to earn the confidence of the more experienced competitors.

Social science theories can be used to analyze the draft line behaviors:

• Complexity theory: racers self-organize into structures that oscillate between order and chaos.
• Social network analysis: draft lines are networks whose organization depends on drivers' social (interpersonal and relational properties) and human (personal properties) capital.
• Game theory: drafting is a 'prisoner's dilemma' problem with incentives for cooperation and betrayal.

NASCAR drafting may be used as a metaphor in other domains. Examples cited include:

• Career advancement in corporate circles: executives take along selected staff and lower-ranking executives as he or she advances, but defections from these draft lines are also common at strategic times.
• International diplomatic and military alliances.
• The Internet, in both its technical and social structures. Communication techniques allow the formation and re-formation of cooperating coalitions.

Technology, history, and social impacts of technology are most often framed in terms of hardware, software, and finance, but communication technologies have the potential to change the way people think, communicate, and organize social groups. These impacts are sometimes framed by Moore's law (microprocessors and chips grow more powerful and less expensive over time), Metcalfe's law (the value of a technical network grows as the square of the number of nodes grows) and Reed's Law (when technical networks enable people to form social groups, the value of the network grows as two raised to the power of the number of nodes - much faster than just the rate of growth of technical networks). The group-formation enabled by the Internet makes it possible for people who don't know each other and who are located in different parts of the world to connect with each other in regard to shared interests - economic, social, cultural, and political. When communication technology enables people to organize collective action in these spheres, civilizations change. Now that the power of computing and communication has untethered from the desktop and leaped into billions of pockets, the forms of collective action are erupting in places and spheres of life where computation and communication had never reached before.

At the point where billions of people have access to personal communications and the instant information that the Internet provides, the aspects of cooperation and collective action discussed by Axelrod, Ostrom, and others comes into play - the capabilities of the emerging mobile mediasphere enable forms of collective action that were not possible before.

Moore's law means that the quantitative capabilities of chip-based devices grow so quickly that they translate into qualitative changes over periods of decades; today, billions of people carry devices that are thousands of times more powerful than the first personal computers, and cost a fraction of the price. At the same time, the users of these devices discover and exploit communication capabilities, social potential, political leverage, economic opportunities that were not dreamed of by those who designed, manufactured and sold the technologies. The technologies that make smart mobs possible are in the earliest stages of development, similar to the state of the personal computer in 1980 and the Internet in 1990. Yet the political demonstrations and electoral leverage that manifested in the Philippines, Korea, Spain, the USA and elsewhere - deposing governments and electing others - show the potentially disruptive power of smart mobs, even in their earliest stages.

At the same time, primitive ad-hoc computation collectives such as SETI@home and folding@home indicate new forms of computing emerging from the collective, voluntary efforts of millions of computer users. And GPS chips add the power of location-based services to the mix: people are mobilizing social networks and information in the immediate time and space.

Economically, the ability to gain profit by sharing with others, rather than only by competing - as manifested by Amazon, Google, eBay, open source software and other enterprises - is making a new kind of economic enterprise possible. Commerce is ancient, markets are as old as the crossroads, but capitalism is only about 500 years old, enabled by technologies such as joint stock ownership companies, shared liability insurance organizations, double entry bookkeeping. Now, the peer production methods exhibited by open source communities and other enterprises hint that humans have not stopped inventing new forms of economic collective action.

Six Degrees begins in the beginning. Stanley Milgram's initial small world studies are analyzed. His findings in seeing if a group of people in Nebraska can get a letter to someone in Massachusetts are scrutinized. Milgram left a puzzle. Mathematically, six degrees of separation can be shown and intuitively it is appealing. But do social networks actually work that way?

Initially, Watts steps into the world of pure mathematic theory. Graph theory and random graphs are employed to build potential worlds in which connections can be made. These tools are detailed and their histories explained.

Watts and his colleagues then take the science to new levels, by introducing sociology, epidemiology, economics, and business models into this new multi-disciplinary science. Immediately, each new field of study brings with it new insights into network dynamics.

This convergence of disciplines reveals the social, transportation and technological networks that make up our world. These networks are, ultimately, made up of individuals. Individuals in turn relate back to the networks and define how they operate.

Socially, people relate to their network by clustering. Clusters are logical organizations of network elements. In a social context, we might cluster in terms of a religion, a favorite author, a school we are attending or an affinity for a type of food. Some of these have very close physical distance, while others have a social distance with members spread out over a large area.

Networks of this type are, to various extents, “scale-free” networks. If graphed these networks roughly follow a classic power law trend where the level of connectivity between two nodes in a network increases dramatically as more nodes are connected. Real-world scale-free networks tend to have highly connected hubs which rapidly, purposely, and efficiently transmit pertinent or pervasive content from one location to another. In social circles, these are networkers. In the airline network these are hub airports. In traffic they would be freeway interchanges.

Due to this architecture, the Internet and modern air transport have combined to greatly decrease the role of proximity in our social networks. This has had great impacts on commerce, tourism, cultural sensitivity and other social factors. However, it has also led to great risks in the transmission of diseases, sensitivity to distant economic fluctuations, and rapid spread of misinformation.

These dynamics create a type of network that Duncan calls simultaneously robust and vulnerable. Their strength and weakness is that, with rapid transmission from cluster to cluster, anything can move quickly from one location or group to another. He uses the example of Toyota, whose network of suppliers was organized in such a way as to quickly compensate for and recover from a potential economic catastrophe.

Stable scale-free networks do not rely on a rigid hierarchy to provide direction in times of crisis. Rather, the structure of the network itself can rapidly respond to an unforeseen situation.

Their network was arranged in such a way as to foster and reward communication. This communication helped cope with ambiguous or unplanned situations. Rather than paralyzing Toyota while people waited for a decision from a rigid hierarchy, the contractors in the network were able to analyze the calamity and provide a rapid response to it.

As mentioned above, this robustness also rapidly transmits malicious content as well. The Melissa Virus, SARS and Ebola are analyzed to show why the network did or did not transmit them and, when it did, how they eventually died out.

Watts ends this book by summarizing that the multidimensional nature of social distance is sometimes counterintuitive and subjective. People can feel close in a network sense to people they are physically distant from and, conversely, socially distant from people physically nearby.

He continues by warning that social and physical distances have shrunk. People can quickly travel from place to place and economies are highly interdependent. The sheer number of dependencies in the modern world may yield surprising results from seemingly insignificant actions.

He finishes by showing the stability of our networks with the example of how New York adapted to the 9-11 attacks. The City bounced back to semi-normal operations within a week. During the disaster, the best laid plans of emergency operations staff were scuttled by the utter unavailability of facilities and services designed to copy with disasters. The network will provide.

Recent years have seen a series of questions asking the applicability and usefulness of traditional collective action theory to certain contemporary phenomena. To name an example, Olson's (1965) proposition that small groups are more successful than larger ones in his account of collective action theory can now be widely contested with evidence from contemporary networks such as the highly successful Indymedia (a large network of journalists, writers, and everyday people organised around participatory media principles).

The paper first examines traditional collective action theory in relation to two central elements: the problem of free-riding and the importance of formal organisation as one important way to overcome it. The challenges presented by new uses of information and communication technologies address specifically to these fundamental elements.

A number of examples are presented, to drive the point that collective action theory has evolved or departed from its traditional concept especially with respect to free-riding (do I contribute or free-ride) and the role of, and dependence on organisation. Some examples are:

1. “Battle in Seattle”, in which a far-flung network of groups «used e-mail, the Web, and chat rooms to engage in a largely self-organising protest against the policies of the World Trade Organisation» (pp 370).
2. The International Campaign to Ban Landmines (ICBLM): «a core strength of the campaign, which still seems to be ill understood by many, has always been its loose structure» (pp. 370).
3. «participating in various groups and public forums in which people's useful contributions emerge from an interactive process rather than the explicit pursuit of a goal» (pp 371).
4. «posting information on a web page or weblog, contributing to discussion on an electronic bulletin board» (pp 372).
5. Open source projects (pp 375).
6. Spontaneously organised smart mobs (Rheingold, 2002) aimed at public goods (pp 376).

These examples effectively illustrate how the nature of free-riding, organisations, and organising have changed in the contemporary media environment. In the case of the problem of free-riding, the binary decision of whether one contributes or free-ride is no longer apparent. Instead, the individual frequently go back and forth through a process of interaction and negotiation for collective action. In many of these scenarios, decisions to free-ride or contribute can also no longer be easily discerned.

The rise of new technological and participatory media have also made communication methods that used to be exclusive to formal organisations, now available for individuals. Changing structures of organisation that are made possible by communication technologies have also resulted in the ability of social movements and groups to take on certain functions of formal organisations — even surpassing the possibilities of formal organisations. Again, the boundaries are blurred, «between traditional hierarchical forms and flexible network structures».

By studying these phenomena, collective action theory is now reframed using the principle of boundary-crossing between private and public. In this context, when an individual cross a boundary between private and public realms, and when this boundary is crossed by two or more people in conjunction with a public good, collective action is said to have occurred. This is a rich frame by which several scenarios in the current contemporary media environment can be accommodated:

• the ease of transforming private discourse to public discourse, without any specific dependence on central organisation (e.g. private responses to an e-mail discussion which eventually becomes public)
• The absence of a central organisation prompting people to share their email lists (transforming private domains into a public domain of collective action)
• The Web as a vehicle for crossing boundaries (information that are privately created may one day become useful publicly)

The facilitation of private-public boundaries results in exchanges that could arguably advance collective action. Technologies that help to identify, for example, private interests, experiences, and acquaintance once identified as shared between people can prompt collective action. Other than permitting the constitution of pubic spheres around commons interests, this focus would also accommodate the continuum by which individuals and groups can easily move back and forth between private and public realms.

Further thoughts:

The notion of using the private-public boundary crossing as the principle to explain contemporary types of collective action is a very interesting one, especially in relation to the commons paradigm in the media environment. Such reconceptualization of collective action is also necessary, in light of the various types of convergence that the world is witnessing today. The convergence of technologies and growing interdependence between people and their uses of technologies, converging communities and organisations, and convergence in media as they continuously evolve over time.

Having said this, there is also a number of theories and constructs which I think would be very useful to study along with the work raised by this paper. For example, borrowing the lens of structuration theory (Giddens, 1986) to look at how the nature of technologies in use reflect the structural and agency properties of the private and public realms would enhance understandings around the social processes of these technologies (how technologies influence and are influenced by people). The theoretical constructs of the commons, such as the Prisoner's dilemma and the tragedy as conceived by Hardin (1968) would also be relevant to study with respect to the free-riding problem and the role of organisations raised by traditional collective action theory. And along with this paper, it may also be worthwhile to reframe the commons concept in light of the contemporary scenarios of the commons.

References

Bimber, B., Flanagin, A. J. and Stohl, C. (2005) Reconceptualizing Collective Action in the Contemporary Media Environment. Communication Theory, 15 (4), 365-388.

Giddens, A. (1986) The constitution of society: outline of the theory of structuration, University of California Press, Berkeley.

Hardin, G. (1968) The Tragedy of the Commons. Science, 62, 1243-1248

Rheingold, H. (2002) Smart mobs: the next social revolution, Perseus Books Group, Cambridge.

The competitive advantage that social networks create is called social capital. Empirical evidence shows that brokerage between interdependent groups that specialize on different things creates more social capital than simply a high number of relationships among individuals (i.e. network closure). However, brokers depend on trust, and trust is frequently viewed to require network closure. The problem with this view is that with increased network closure the value of brokers diminishes which in turn creates less social capital. Part of solving this problem is to figure out whether network closure really does produce the kind of trust that increases social capital. Burt shows that trust created by network closure might be ill-founded.

The relationship strength between ego and alter correlates with the amount of trust between ego and alter. In a social context ego also receives gossip about alter, i.e. information about alter via third parties. The bandwidth hypothesis states that gossip nework closure increases information flow reinforcing and fine-tuning trust relationships beneficial to social capital. The echo hypothesis states that gossip network closure does not so much increase information flow but reinforces dispositions. This is due to a commonly observed etiquette in informal conversations where third parties only reveal information about alter to ego that concur with ego's opinion of alter. The motivation for this etiquette are civility, efficiency, and the important role gossip plays in creating and maintaining relationships.

Analysis of survey network data of three study populations consisting of senior managers in a leading manufacturer of electronic components and computer equipment, of staff officers in two financial companies, and a bankers in the investment banking division of a large financial company shows that trust can develop within negative third-party ties ("an enemy of my friend is my enemy" or "a friend of my enemy is my enemy"), and distrust can develop within positive third-party ties ("a friend of my friend is my friend" or "an enemy of my enemy is my enemy") which is consistent with the echo hypothesis but not with the bandwidth hypothesis.

"Strong connection through third parties increases the probability of social reinforcement such that network closure creates echo, not accuracy. [...] Therefore, network closure does not facilitate trust so much as it amplifies dispositions, people cannot learn of what they do not already know" which negatively impacts social capital.