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 authors distinguish between emotions that operate primarily in dyadic relationships and emotions that operate in a significant manner in collective contexts. The authors examine the evolutionary role each emotion and cite research about ways these emotions might contribute to the creation and maintenance of cooperative behaviors: "This chapter is premised on the claim that human cooperation is profoundly shaped by, and perhaps only possible because of, emotions. We will examine the manner in which different emotions shape behavior in cooperative contexts…Although framed within an evolutionary psychological perspective, our goal is not to present definitive evidence of the validity of this particular approach, but rather to spur future investigations of the role of emotions in cooperation. Toward that end, on an emotion-by-emotion basis we will both briefly describe a variety of existing findings and present a number of hypotheses, specifying discrete, testable predictions whenever possible."

Emotions that are primarily dyadic include romantic love, gratitude, anger, envy, jealousy, guilt righteousness and contempt. Romantic love is seen as a means of overcoming a barrier to the kind of cooperation we see in parenting -– the temptation to defect in the short term on a relationship that requires a long-term investment. "A number of investigators have suggested that some emotions can be understood as mechanisms design to commit people to behavior that yields long-term payoffs, thus overcoming the temptation for short-term defection. Romantic love, a universal human emotion that underpins pair bonding, appears to be such a mechanism."

Where romantic love is about how one feels about another person, gratitude addresses how one feels about somebody's behavior, and can be an emotional currency that binds one to reciprocity. "Gratitude focuses both attention and a positive, affiliative orientation on a party who has supplied the actor with a substantial benefit. In the context of its initial elicitation, gratitude seems to prompt the actor to recognize a valuable interaction partner and subsequently signal a willingness to reciprocate."

Why do people get so angry when someone cuts ahead of them in a queue or in traffic? This is clue to the evolutionary advantage of anger as a means of protecting ones own interests, but when it comes to the thus-far unexplained human propensity to punish cheaters, even at a cost to themselves, anger might be instrumental in conferring advantage to a group that requires monitoring and sanction of free riders in order to maintain a public good or create an institution for collective action: "If gratitude is elicited by receipt of a benefit, its opposite is anger, elicited by actual ar attempted exploitation or harm. More formally, anger is the response to the infliction of a cost. In addition to showing an "irrational" willingness to reward generosity, subjects in behavioral economics experiments also show an eagerness to punish uncooperative partners…Together, these results clearly demonstrate that even within the confines of finite anonymous games, angry individuals often place paramount importance on harming the transgressor, and are willing to incur substantial costs in order to do so."

The commonsense view of social evolution as the product of choices made in the marketplace of cultural possibilities resulting in the continuous betterment of the human condition is flawed.

The rise of civilization, paradoxically, reduced the natural limits separating societies. In such a situation, Schmookler's Parable of the Tribes describes how, in a situation in which two or more actors desire to exploit a limited resource, power becomes important and a contaminant of the possibility of peaceful co-existence:

All of a group of tribes living within reach of each other choose peace. However, if all but one choose peace, there are four possibilities for the threatened neighbors:

• Destruction.
• Absorption and enslavement.
• Withdrawal to a less desirable place.
• Imitation of the aggressive behavior.

Technological innovation and “improvement,” far from making things inevitably better, can extend the reach of aggressors throughout the world.

Cultural homogenization and the diminishment of diversity happens both through benign, commonsense choice (i.e., innovations as improvements) as well as through compulsion by dominant aggressors.

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.

Humans have taken the cooperative arrangements that benefited organisms and species at the biological level to the cognitive and social levels: the capacity to play cooperative social games that benefit all was a driver of the evolution of human intellectual capacity; increased intellectual capacity manifested in both the concrete sphere of tool-making and the abstract sphere of social relationships. Once enhanced cognitive capabilities made complex social arrangements like status, reputation, gossip, persuasion, punishment, alliance possible, human social capacities became a tool for ratcheting up cooperative game-playing capacity.

Certain technologies push human societies to reorganize at a higher level of cooperation. As an example, Wright offered the Shoshone, a Native American tribe that lived in a territory with no big game to hunt but an abundance of jackrabbits at certain times of year. Because of their stark environment, the Shoshone normally existed at a simple level of social organization, with every extended family foraging for itself. When the rabbits were running, however, the families banded together into a larger, closely coordinated group, to wield a tool too large for any one family to handle or maintain — a huge net. Working together with the net, the entire Shoshone hunting group can capture more protein per person than they could working apart. Wright declared that "The invention of such technologies — technologies that facilitate or encourage non-zero-sum interaction — is a reliable feature of cultural evolution everywhere. New technologies create new chances for positive sums, And people maneuver to seize those sums, and social structure changes as a result."

Wright noted that people who interact with each other in mutually profitable ways are not always aware that they are cooperating; he cited evolutionary psychologists to assert that unconscious underpinnings of cooperation — like affection and indignation — are rooted in genetic traits:

"… natural selection, via the evolution of 'reciprocal altruism' has built into us various impulses which, however warm and mushy they may feel, are designed for the cool, practical purpose of bringing beneficial exchange."

"Among these impulses: generosity (if selective and sometimes wary); gratitude, and an attendant sense of obligation; a growing empathy for, and trust of, those who prove reliable reciprocators (also known as "friends"). These feelings, and the behaviors they fruitfully sponsor, are found in all cultures. And the reason, it appears, is that natural selection "recognized" non-zero-sum logic before people recognized it…Some degree of social structure is thus built into our genes."

"In the intimate context of hunter-gatherer life, moral indignation works well as an anti-cheating technology. It leads you to withhold generosity from past nonreciprocators, thus insulating yourself from future exploitation; and all the grumbling you and others do about these cheaters leads people in general to give them the cold shoulder, so chronic cheating becomes a tough way to make a living. But as societies grow more complex, so that people exchange goods and services with people they don't see on a regular basis (if at all), this sort of mano-a-mano indignation won't suffice; new anti-cheating technologies are needed. And, as we'll see, they have materialized again and again — via cultural, not genetic, evolution."

The cultural innovations that reorganize social interaction in light of new technologies are "social algorithms governing the uses of technology." Wright called these social methodologies "metatechnologies.". In the Middle Ages, the metatechnologies of capitalism — currency, banking, finance, insurance — pushed the hierarchical machinery of feudal society to transform into a new way of organizing social activity, the market. "The metatechnology of capitalism then combined currency and writing to unleash unprecedented social power." Wright claimed that the emerging merchant class pushed for democratic means of governance, not out of pure altruism, but in order to be free to buy and sell and make contracts. Throughout this process, powerful people always seek to protect and extend their power, but new technologies always create opportunities for power shifts, and at each stage from writing to Internet, more and more power decentralizes: "I mean that new information technologies in general — not just money and writing — very often decentralize power, and this fact is not graciously conceded by the powers that be. Hence a certain amount of history's turbulence, including some in the current era."

The theory that ancestral hominids were hunters has achieved considerable popularity. The authors note that there is considerable evidence that ancestral hominids were more likely a prey species. There are three major lines of evidence:

• Modern humans are occasionally prey species for certain predators, especially for tigers in parts of modern India and Bangladesh … people wear masks on the backs of their heads in order to appear alert to potential stalkers.
• Modern apes and monkeys are frequent prey species for a wide variety of carnivores, a fact which does not appear in publications about the animals themselves but which is widely noted in works covering the diet of the predators themselves.
• Ancestral hominid fossils show the marks of various predators – tooth, claw, and talon marks – that are consistent with predation.

The authors demonstrate the prey-nature of early hominid ancestors. Their speculations as to what this means in evolutionary and behavioral terms, however, are weaker. This is because it is a long way (in both time and, potentially, place) from Australopithicus afarensis to H. sapiens … the use of tools and fire, for instance, may well have modified behavior inasmuch as the immediate ancestors of H. sapiens became a more formidable prey species.

The question as to what behaviors are derived from those ancestors which were commonly prey animals remains open for speculation and future work. That said, the authors present a challenge to those who maintain the view that humankind’s ancestors were hunters from the earliest times.