Currently, spectrum in wireless networks is allocated using a property based scheme. This solves the problem of interference by providing coordinated access to capacity for users of multiple technologies at the cost of fixing the available capacity for each technology area. Recently, architectures have been proposed that use a cooperative strategy for capacity allocation. These have the advantage of increasing available capacity with the number of users. The author believes that cooperative wireless networks could be created that provide capacity that scales proportional to the number of users. In addition, a cooperative wireless network would have increased options with respect to Metcalfe's Law, the number of pairwise transactions that could occur would grow as N**2, and Reed's Law, the number of groups that could be formed would grow as 2**N.

The author argues that the scaling of capacity available in wireless networks indicates that spectrum does not behave like ordinary property and requires a different commons based allocation and coordination regime one that encourages cooperation among users in order to increase available capacity. No obvious regime exists today. However, the current Internet regime for wired communication was formed from 25 years of innovation in an open and experimental environment. The resulting regime differs significantly from the previous regime that grew under the control of the telecommunications provider. The author calls for the development of a cooperative wireless network regime by starting with an open and experimental regime that encourages cooperation much the same as the starting point for the current Internet regime for wired digital communications.

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."

“Along every conceivable dimension — from the intimate to the institutional — digital media force both individuals and organizations to redefine what kind of relationships create value.” The result of this paradigm shift isn’t about data and information, it’s about the value and priority that people place on the quantity and quality of their relationships.

Significant advances in technology have always altered how we perceive ourselves and our relationships. The automobile had an impact beyond simply moving from point to point B, and TV had an impact beyond delivering images and sound. Both of those wrought real and profound cultural change. Whenever a new medium emerges we have to look beyond the simple mechanics of the medium to the impact the medium has on the community. What’s important to recognize is that these new digital technologies aren’t simply evolutions of preceding technologies, but that these new technologies are now networked with each other. These new networks between networks have resulted in new relationships between networks that, in turn, have created new kinds of relationships between people.

This new phase of networked technologies allows individuals and institutions alike to create new ways of interaction. Intimacy, anonymity, trust, openness, access, passion, negotiation, hierarchy, coordination and collaboration can all be mediated, monitored and managed via networks ostensibly designed to carry bits. The value challenge has shifted from gathering and disseminating information to packaging and bundling it in unexpected ways.

What people crave is the chance to communicate and relate to each other in new ways – not simply to have access to a vast feast of information. The new technologies are directly related to the essence of being human. They challenge and stretch the traditional meaning of concepts like relationship, community and interpersonal expression. People expect more from these new technologies than simple job improvement, they want it to improve their working relationships with their boss, their colleagues, their subordinates and their clients.

Ultimately what is critical to people is value, and it is people, not information, that create value. According to “Netizens” their increased sense of belonging, of being part of a larger community, greatly outweighs the benefits of having a mass of information available. Failing to understand the transformational affects of the digital technology on culture itself will result in missed opportunities.

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.

Chapter 1, The Problem of Cooperation. Why do people (or other actors) cooperate? "The objective of this enterprise is to develop a theory of cooperation that can be used to discover what is necessary for cooperation to emerge." It uses the Prisoner's Dilemma as a framework for testing theories about balancing self-interest and competition.

"In the Prisoners' Dilemma, the strategy that works best depends directly on what strategy the other player is using and, in particular, on whether this strategy leaves room for the development of mutual cooperation."

Chapter 2, TIT FOR TAT. "The iterated Prisoners' Dilemma has become the E. Coli of social psychology," yet people have not paid much attention to how to play the game well. Axelrod organized a computer tournament to which people familiar with PD submitted programs encoding different strategies. The winner was one of the simplest, TIT FOR TAT.

Axelrod then constructed an environment in which different programs competed, and the losing programs were eliminated: this was an ecology that rewarded high scoring programs, and punished others. "This process simulates survival of the fittest. A rule that is successful on average with the current distribution of rules in the population will become an even larger proportion of the environment of the other rules in the next generation. At first, a rule that is successful with all sorts of rules will proliferate, but later as the unsuccessful rules disappear, success requires good performance with other successful rules." In other words, the competition gets tougher.

"The analysis of the tournament results indicate that there is a lot to be learned about coping in an environment of mutual power. Even expert strategists from political science, sociology, economics, psychology, and mathematics made the systematic errors of being too competitive for their own good, not being forgiving enough, and being too pessimistic about the responsiveness of the other side."

The tournaments reveal that "there is a single property which distinguishes the relatively high-scoring entries from the relatively low-scoring entries. This is the property of being nice, which is to say never being the first to defect."

TIT FOR TAT's rules for success:

• Be nice. Don't be the first to go on the attack. This demonstrates good will, and avoids provoking others.
• Retaliate. If others attack, retaliate. Not doing so encourages bad behavior and gives niceness a bad reputation.
• Be forgiving. If others defect but then go back to cooperating, accept the opportunity to move back to a cooperative mode.
• Be clear. Others can predict what you'll do, be certain that their moves will have definite outcomes. "There is an important contrast between a zero-sum game like chess and a non-zero-sum game like the iterated PD. In chess, it is useful to keep the other player guessing about your intentions. The more the other player is in doubt, the less efficient will be his or her strategy. But in a non-zero-sum setting it does not always pay to be so clever. In the iterate PD, you benefit from the other player's cooperation."

Chapter 4, Trench Warfare. During World War I, "live and let live" arrangements emerged spontaneously between opposing units on the Western Front. Cooperation could take hold because "the same small units faced each other in immobile sectors for extended periods of time." Consequently, they had a more sustained relationship than in mobile warfare, and could develop commonly-understood rules, reciprocity and restraint in attacks, displays of strength (e.g., snipers shooting at hard targets)as well as ethics (recognition that there was an arrangement and violating it was immoral) and rituals (e.g., regular artillery firing).

"Cooperation first emerged spontaneously in a variety of contexts, such as restraint in attacking the distribution of enemy rations, a pause during the first Christmas in the trenches, and a slow resumption of fighting after bad weather made sustained combat almost impossible. These restraints quickly evolved into clear patterns of mutually understood behavior, such as two-for-one or three-for-one retaliation for actions that were taken to be unacceptable."

Chapter 6, How to Choose Effectively. Four suggestions about how to do well in PD:

• Don't be envious. In a PD, "envy is self-destructive. Asking how well you are doing compared to how well the other player is doing is not a good standard unless your goal is to destroy the other player." However, in an iterated prisoner's dilemma, you can't do better than the other player, unless they're always suckers. "In a non-zero-sum world you do not have to do better than the other player to do well for yourself. The other's success is virtually a prerequisite of your doing well for yourself."
• Don't be the first to defect (be nice). "It pays to cooperate as long as the other player is cooperating." In a short game, defection can make sense; but in a relationship, taking advantage of the other person is self-defeating.
• Reciprocate both cooperation and defection. TIT FOR TAT "does not destroy the basis of its own success. On the contrary, it thrives on interactions with other successful rules." However, the right level of forgiveness depends on the context, and the other players' strategies.
• Don't be too clever. "In a zero-sum game, such as chess it pays for us to be as sophisticated and as complex in our analysis as we can. Non-zero-sum games are not like this. The other player can respond to your own choices. And unlike the chess opponent, the other player in a PD should not be regarded as someone who is out to defeat you." "There is an important contrast between a zero-sum game like chess and a non-zero-sum game like the iterated PD. In chess, it is useful to keep the other player guessing about your intentions. The more the other player is in doubt, the less efficient will be his or her strategy. But in a non-zero-sum setting it does not always pay to be so clever. In the iterate PD, you benefit from the other player's cooperation."

Chapter 7, How to Promote Cooperation. Promoting cooperation can be thought of as an exercise in tinkering with the variables in a PD. "As long as the interaction is not iterated, cooperation is very difficult. That is why an important way to promote cooperation is to arrange that the same two individuals will meet each other again, be able to recognize each other from the past, and to recall how the other has behaved until now."

• Enlarge the shadow of the future. For cooperation to emerge, players must be in a continuing relationship, with the expectation that it will continue in the future. "Mutual cooperation can be stable if the future is sufficiently important relative to the past." "There are two basic ways of doing this: by making the interactions more durable, and by making them more frequent. [P]rolonged interaction allows patterns of cooperation which are based on reciprocity to be worth trying and allows them to become established," Making interactions more frequent makes "the next interaction occur sooner, and hence the next move looms larger than it otherwise would." You might do this by enforcing isolation, or constructing hierarchies or organizations, which are "especially effective at concentrating the interactions between specific individuals."
• Change the payoffs. Make defection less attractive, by enforcing laws, or growing the value of long-term incentives.
• Teach people to care about each other.
• Teach reciprocity. Reciprocity "actually helps not only oneself, but others as well. It helps others by making it hard for exploitative strategies to survive."
• Improve recognition abilities. "The ability to recognize the other player from past interactions, and to remember the relevant features of those interactions, is necessary to sustain cooperation. Without these abilities, a player could not use any form of reciprocity and hence could not encourage the other to cooperate."

Chapter 8, The Social Structure of Cooperation.
The social structure of cooperation involves labels, reputation, regulation, and territoriality.

• Labels are fixed characteristics of an agent that are observable by other agents. Labels affect reciprocity and retaliation via assumptions of group similarity and stereotypes.
• Reputation is others' belief about the strategies an agent will employ. Reputation may be based on past behavior or on rumours, i.e. reputation can be accurate or merely believed. Reputation affects whether or not other agents will cooperate or defect with you.
• Regulation involves setting the stringency of a standard of behavior "high enough to get most of the social benefits of regulation, and not so high as to prevent the evolution of a stable pattern of voluntary compliance from almost all of the companies" (or regulated agents).
• Territoriality refers to both physical and conceptual spaces that can be 'invaded' by agents of differing strategies. Territoriality establishes boundaries within which behaviors will be reinforced or retaliated against depending on prevailing norms. Also, the boundary provides an 'inside' for agents that comply with the norms, and an 'outside' to which they can be expelled if they do not comply.

Chapter 9, The Robustness of Reciprocity.

• Cooperation can get started by even a small cluster of individuals who are willing to reciprocate cooperation, even in a world where no one else will cooperate.
• Once cooperation is establish, it protects itself from invasion by non-cooperative strategies.
• The foundation of cooperation is the durability of the relationship, which allows agents to learn about each other in order to cooperate.

Dan Bricklin examines ways to induce a pool of users to contribute to a commons without extra effort, using the architecture of the commons (as in Napster's default to sharing in the way download directories are available) and leveraging user's self-interest. The key to understanding the success of Napster and other file-sharing technologies resides not in their 'peer-to-peer' nature but in the fact that they provide users with access to a database of desirable things and enable people to create a public good in the process of seeking their own interests.

Bricklin identifies three ways to fill a database: organized manual, organized mechanical, and volunteer manual.

CDDB succeeded at motivating volunteer manual data entry because it leveraged the desire for users to have their data in the database so that CDDB-aware programs could access it, for example when a user would insert a CD into their computer.

Bricklin calls this "harnessing the power of individual selfishness."

Napster cleverly avoided manual data entry by automatically indexing anything in the user's 'Shared Music' directory. Thus "storing the copy in the shared music directory [was] a natural by-product of the user's work with the songs."

Sharing is the default. This results in users "adding to the value of the database without doing any extra work."

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.

“The study of social dilemmas is the study of the tension between individual and collective rationality. In a social dilemma, individually reasonable behavior leads to a situation in which everyone is worse off. The first part of this review is a discussion of categories of social dilemmas and how they are modeled.” The Prisoner’s Dilemma, the problem of providing public goods, and Hardin’s Tragedy of the Commons are three powerful metaphors that facilitated and structured research but also served as blinders since their limitations are often not recognized.

Models:

Kollock’s analysis divides dilemmas into two-person and N-person dilemmas. The key two-person dilemmas are the Prisoner’s Dilemma, the Assurance Game, and the Chicken Game. Each of these models is defined by the ordering of four possible outcomes: mutual cooperation, mutual defection, and either first or second person’s unilateral defection. Each of these outcomes generates an individual benefit for each person and is ordered by the benefit for the first person.

The Prisoner’s Dilemma models unsecured transactions, e.g. buying and selling over the Internet. The best outcome of a Prisoner’s Dilemma is unilateral defection of the first person, followed by mutual cooperation, mutual defection, and the worst outcome is the first person’s unilateral cooperation. Since defection has the highest potential benefit and cooperation the highest potential risk, the equilibrium of the Prisoner’s Dilemma is mutual defection. This equilibrium is deficient because the best outcome for both players is mutual cooperation.

The Assurance Game is similar to the Prisoner’s Dilemma except it models situations where mutual cooperation is more benefical for each player than unilateral defection, e.g. a project that requires collaboration. This extra motivation to mutually cooperate creates two equilibria, one optimal, which is mutual cooperation, and one deficient, which is mutual defection. The optimal equilibrium requires trust between the two persons sufficient to assure each other that the other will cooperate. Insufficient trust leads to the deficient equilibrium.

The Chicken Game is again similar to the Prisoner's Dilemma except mutual defection is the worst outcome, worse than unilateral cooperation. This replaces the Prisoner’s Dilemma’s mutual defection equilibrium by two equilibria, unilateral defection and unilateral cooperation because of the strong motivation to not mutually defect. The Chicken Game is a model for situations that require volunteer effort to avoid the worst outcome but where duplicate effort is less desirable.

Kollock divides N-person dilemmas into two types based on cost and benefit for each individual. The first type is known as the social fence,s where an individual is presented with an immediate cost that generates a benefit shared by all. The individual wants to avoid the cost but if all do, everyone is worse off. A common metaphor of the social fence is the provisioning of public goods, which are (to a varying degree) non-excludable and nonrival. The key characteristic of a public good dilemma is the production function which defines the relationship between the level of resources contributed and the level of public good provided. Production functions are classified into decelarating, linear, accelerating, and step functions. Various production functions can produce N-person versions of any of the 2-person dilemmas.

The second type is know as social trap where the “individual is tempted by an immediate benefit that produces a cost to all. If all succumb to the temptation, the outcome is a collective disaster.” The usual metaphor of the social trap is the tragedy of the commons. A key feature of commons dilemmas is that the benefits are non-excludable (or difficult to make excludable) and subtractable. The key characteristic of commons dilemmas is the carrying capacity of the commons which depends on the replenishment rate of the subtractable joint resource.

Important (but not inevitable) features that affect N-person dilemma dynamics and contrast them to two-person dilemmas are anonymity, diffusion of defection cost, and little or no direct control on others. Some of these features are also found in two-person dilemmas, e.g. blaming defection on out-of-control circumstances is a form of anonymity in two-person games.

Solutions:

“The second part of [Kollock’s paper] is an extended treatment of possible solutions for social dilemmas. These solutions are organized into three broad categories based on whether the solutions assume egoistic actors and whether the structure of the situation can be changed: Motivational solutions assume actors are not completely egoistic and so give some weight to the outcomes of their partners. Strategic solutions assume egoistic actors, and neither of these categories of solutions involve changing the fundamental structure of the situation. Solutions that do involve changing the rules of the game are [called] structural solutions.”

The motivation of not completely egoistic actors to cooperate is influenced by social value orientation, communication, and group identity. The social value orientation of a person seems to be acquired from the person’s social environment and is some linear combination of a cooperator who tries to maximize joint outcome, a competitor who tries to maximize own outcome relative to partner, and an individualist who tries to maximize own outcome. Kollock does not find any conclusive results in how to influence social value orientation but does find evidence that it varies between different countries.

The presence of communication positively affects cooperation rates. Communication enables a person to find out about others’ choices, to make explicit commitments, to appeal to what is the moral thing to do, and most importantly, to create or reinforce a sense of group identity. The effect of group identity is in fact so strong that it can affect cooperation rates even in the absence of communication. In-group behavior of individuals frequently includes personal restraint and treating Prisoner’s Dilemma situations as Assurance Games. However, in-group behavior implies out-group behavior with the potential to cause severe social costs due to intergroup conflicts.

“[Strategic solutions] rely on the ability of [egoistic] actors to shape to shape the outcomes and hence behavior of other actors. For this reason, many of these strategic solutions are limited to repeated two-person dilemmas.” Axelrod (see The Evolution of Cooperation) identifies three requirements for strategic solutions: ongoing relationships between actors (i.e. all expect shared dilemmas in their future), ability to identify each other, and ability to keep track of the other’s past behavior.

The most successful strategy in iterative Prisoner’s Dilemma tournaments (everyone against everyone) that meet these requirements is Tit-for-Tat which starts out with cooperation and then matches the partner’s previous behavior. This strategy transforms a repeated Prisoner’s Dilemma into a repeated Assurance Game since the only long-term outcome of this strategy is either mutual cooperation or mutual defection (the two equilibria of the Assurance Game). Key aspects of successful strategies in repeated Prisoner’s Dilemma tournaments are (1) to realize that it is not a zero-sum game hence does not benefit from a competitive social orientation (“don’t be envious”), (2) to not defect first, (3) to reciprocate both cooperation and defection, and (4) to be predictable so that the partner clearly understands one's strategy. One important caveat is that repeated Prisoner’s Dilemma tournaments assume perfect communication. In real life where communication is often imperfect more generous or forgiving strategies can avoid accidental cycles of recrimination.

Recent evidence suggests that the strategy of choosing partners is more important than the strategy used within a dilemma. In a modified version of iterative Prisoner’s Dilemma tournament actors can exit current relationships and choose alternative partners. A very successful strategy in this environment is Out-for-Tat which exits a relationship as soon as the partner defects. A more forgiving version that gives a defecting partner a second chance is even more successful.

Strategies for N-person dilemmas involve grim triggers, social learning, and group reciprocity. In a “grim trigger” strategy an individual only cooperates if all other group members cooperate and defects as soon as one other group member defects. Social learning is the basis of a cognitively less taxing class of strategies that involves imitating other group members and look for thresholds in public good provisioning instead of calculating marginal rates of return or figuring out dominating strategies. Group identity increases cooperation rates because group members follow strategies that assume that all members share a strong expectation of group reciprocity (reciprocity within the group).

Structural solutions change the rules of the dilemma thereby changing or eliminating it. One approach is to reinforce prerequisites for strategic solutions by introducing long-term accountability (shadow of the future) that influences individual reputations. However, accountability and reputation are not sufficient to escape the Prisoner’s Dilemma’s equilibrium of mutual defection (in two- or N-person version) if the means to encourage cooperation are too weak (e.g. production function for public good too flat or too much effort required to reach provisioning point).

Many people seem to positively weigh others’ outcomes since cooperation increases significantly as the benefits to others from one’s cooperation increase. Cooperation levels are also higher if group members are asked to contribute to a non-divisible public good that only benefits the whole group, probably due to an increased sense of group identity (see group reciprocity).

Cooperation in N-person dilemmas increases if individual contributions have (or are perceived to have) a discernable effect, i.e. make an efficacious contribution. For public goods with step-level production function one can create a minimal subgroup that requires every member to contribute in order to reach the provisioning point or let two groups compete for contributions, turning an N-person Prisoner’s Dilemma into an N-person Chicken Game. Another example are "matching grants" or "adopting" an individual from a large group of benefactors.

Increasing group size makes defection more anonymous and increases the cost of organizing. However, research results on cooperation depending on group size alone are inconclusive. In the case of highly non-rival goods with a threshold production function a larger group is more likely to contain a "critical mass" of cooperating individuals. Diversity of group members' interests and resources encourages formation of critical mass.

A common structural strategy for N-person dilemmas is the creation of boundaries in an attempt to make public goods or commons more excludable. There are three main approaches: The first one is to institute an external authority or trusted leader to govern access to commons. This approach appears to be less preferable if other structural changes are possible. Establishing an external authority can raise severe problems of justice, enforcement, corruption, and scalability. The second approach is to break up commons into private parcels assuming that individuals will take better care of own property than common property. However, privatization does not work for non-divisible goods, raises the social question of who gets to own commons, does not prevent owners to routinely destroy their own property (“tragedy of enclosure”), and requires institutional support to enforce private property rights. A third approach is to locally regulate “access to and use of common property by those who actually use and have local knowledge of the resource.” One key characteristic of successful and long-lasting local regulations is clearly defined boundaries.

Sanctions are a structural method to encourage cooperation where the outcomes themselves of N-person dilemmas are too weak of a motivator. However, the implementation of sanctions can be very expensive. Local monitoring and sanctioning systems are more practical and less costly. Another way to reduce cost is to use a graduated system of sanctions with low-cost conflict resolution. A sanctioning system is itself a public good and therefore poses a second-order dilemma. Communities with a high level of trust readily cooperate in a first-order dilemma but cooperate less in a second-order dilemma hence are less willing to support a sanctioning system. The opposite is true for communities with a high level of distrust.

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.