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."

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.

The self-regarding and outcome oriented picture of human behavior presented in traditional economics does not explain why humans care so much about each other and about how social interaction is carried out, not just the end goals. The Ultimatum Game, designed by Werner Guth, is just one illustration of how real people will not always follow the dictates of self-interested rationality. Two subjects are given a sum of money, one is given the power to divide the sum, and the other can either accept or reject (in which case neither get any money). Research from conducting hundreds of trials of the game with thousands of students in Europe, Japan and the USA has shown that the responders frequently reject low offers and proposers frequently propose near equal divisions, even though it is to their monetary disadvantage. While early experiments on undergraduates seemed to suggest that there was a universal sense of fairness, extended research in different cultures (hunter-gatherers, slash-and-burn agriculturists, nomadic pastoralists) has exposed much cultural variation in responses, indicating that local cultural conditions play an important role in how people approach cooperation.

While mean proposals for university students from all over the world was usually between 42 and 48 percent, mean proposals from this cross-cultural study varied from 25 to 57 percent. Rejection rates, the action of the responders, also varied considerable between groups. Individual-level economic and demographic variables did not explain behavior as well as group-level behavior, and game play often could be connected to the people's common patterns of interaction. For example, the Orma recognized that one of the experiment's games was similar to the harambee, a local institution of giving to public goods like roads and schools. They began calling it 'the harambee game' and displayed highly prosocial behavior. In other groups, like the Au and Gnau, frequent rejection of generous offers can be explained by a cultural association with gift-giving: accumulating gifts, even if unsolicited, can imply a lowered status and force the receiver into future obligations or political alliance. The cross-cultural study showed that, in the case of groups at the extremes of behavior, "contrasting behaviors seem to reflect their differing patterns of everyday life, not any underlying logic of hunter-gatherer life ways."

The effect of market integration on cooperation to obtain a monetary reward can be explained easily: individuals from market-oriented societies when put in the context of one of the games are able to seek analogues in their daily activities of using and trading money with strangers. "Those who do not customarily deal with strangers in mutually advantageous ways may be more likely to treat anonymous interactions as hostile or threatening, or as occasions for the opportunistic pursuit of self-interest."

Social psychological research has a long tradition of interest in cooperative behavior and commons dilemmas, beginning with Von Neumann and Morgenstern's 1944 book, Theory of Games and Economic Behavior. It is convenient to summarize the way psychological studies have attacked the commons problem through nine variables (social motives, gender, payoff structure, uncertainty, power, status, group size, communication, causes, frames) divided into three groups (individual differences, situational factors of the task structure, and the perceived effects of situational factors.) Although behavior elicited in a controlled lab environment is never the same as that observed in field research, lab research is an indispensable tool for teasing out causal relations from the larger number of interacting influences. Attention to the findings of psychological research relevant to the specific instance of a commons dilemma can make the difference in generating positive collective action.

Studies on social motives have found correlations between motives and choice behavior and interpretation of others' behavior. Liebrand et al. (1986) demonstrated that "people with individualist social motives tend to interpret behavior along the might dimension (what works), whereas cooperators tend to view cooperation and competition as varying on the moral dimension (what is good or bad)." Less intuitive findings have come out of research into social rewards for cooperative behavior. Gachter and Fehr (1999) conducted a study around public goods dilemmas with an anonymous group, a group that met beforehand to establish a group identity, a group that had a chance to interact after playing, and a group that met before and after. They found that neither the second nor the third groups had significant improvements in cooperation, but that the fourth option resulted in "significantly higher levels of contribution."

Uncertainty of resource size may play a detrimental role in commons dilemmas for multiple reasons. Any factor which threatens to bring an end to the resource will decrease interest in one's reputation and therefore with the relations that support the resource's maintenance. Uncertainty also helps diffuse personal responsibility, since overusers can try to justify their actions through their ignorance of the current resource size.

Experimenters have also uncovered interesting effects from varying groups size on self-efficacy, an individual's sense of their own competence in taking effective action. Smaller groups were found by Kerr (1989) to have higher averages when testing "collective" efficacy, the sense that the group could carry out effective actions to achieve a desired outcome. In groups with a relatively low provision point, the percentage of cooperating members necessary to support the public good, small group size was associated with a sense of "collective" efficacy.

Voting can significantly improve efficiencies in commons dilemmas by acting as a form of communication. Collective learning of general information "extends to subsequent situations and enables people to coordinate their activities even in rounds when no proposals are made."