The capacity of biological and ecological systems for self-adaptation or self-organization has been a significant theme in the current life-science academic literature. The article is a case study of complex adaptive systems theory, focusing upon the global political system as a part of a biophysical aggregate system in which we are embedded. This paper explains why self-adaptation does not explain the global political system at this time and postulate what conditions must be met if it did. Self-adaptation, if it were achieved and maintained in some proximate form, would constitute a phase transition.

Our species’ cumulative actions on the environment (including those generating global warming, environmental pollution, ozone depletion, and biodiversity loss) are the dominant source of the increasing density of causal connectedness between human and natural systems. Given the dramatic rise in the world’s population, technological growth--that affecting industrial practices, life-style behavior, and global trade and investment and military weaponry—is the underlying factor that has driven rising causal interconnectedness. I conjecture that the current density of connectedness constitutes a complexity phase into which humanity has entered. The rapidity with which the banking and economic collapse in the United States proliferated into a global recession contributes to my conjecture. Entering into the complexity phase is a profound non-genetically based evolutionary event with profound consequences. As a consequence, humanity is perilously close to what theoretical biologist Stuart Kauffman has called a “complexity catastrophe,” sharply limiting the capacity for self-adaptation, and in matters of global governance dramatically increasing the degree of difficulty of effective governance.

This paper focuses on the structural properties of the global political system and argue that its anarchic conditions are maladaptive. The anarchic quality of the strong nation system and the aggressive, self-maximizing behavior of the strong nation it generates serve to diminish the possibility of achieving sustainability. Moreover, the justification for aggressive, self-maximizing behavior within the strong nation system is weakened once in the complexity phase, and such behavior and its justification are no longer viable when a related conflict exists or emerges between global security and national security. The signature characteristic of global governance for global problems is that global security takes precedence over national security when a conflict arises.

The paper maintains that global governance for global problems is a necessary but not sufficient condition for the achievement of sustainable global governance, and is not likely to occur without strong nation advocacy. Sustainable global governance requires, among other things, the development and maintenance of resilience within and between our tightly-coupled human-made and natural systems.

Rising density of casual interconnectedness may generate a heightened concern for global problems creating, in turn, an unprecedented commonality of interests among nations, especially strong nations, and their respective citizens. Should global “localization” occur, it would produce a veritable compression of ideational space characterized by fewer differences in policy prescriptions between (and within) nations. We would expect the emergence of vocal interest groups and revitalized, if not new constituencies, sympathetic to an ethos that extends beyond the narrow self-interest of the strong nation system. Such a compression might parallel the characteristics of the small world network. However, the likelihood is that a struggle for power between those advocating narrow self-interest within the strong nation system and those favoring a wider and global interest, will undoubtedly playout, and no one can predict in advance its outcome.

Economic globalization has contributed to the dense, causal interconnectedness both within and between nations. The management of the global economy in accordance with neo-liberal policies reflect the distribution of power within the strong nation system. These policies with their embrace of market fundamentalism have had destabilizing, global effects, especially upon third world nations.

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."

Strogatz examines the underlying process of creating patterned behavior in situations where there is no obvious conscious control or even intention. These phenomena arise from “coupled oscillation”—that is, the tendency of phenomena at all levels of existence to synchronize their rhythmic features. The classic example: southeast Asian fireflies that flash in synchrony over miles of countryside.

Other natural examples are discussed:

• earthquakes
• forest fires
• mass extinctions
• synchronization of female menarche
• heart attacks

The Mathematics of Sync

The underlying requirement for coupled oscillation or sync to occur is for phenomena to operate in cycles and for the players in the phenomena to be able to influence each other mutually. In addition, a catalyst may sometimes be necessary. One of variables is pulsed communication vs. continuous interaction: continuous interaction creates more complex, subtle sync.

Some observed qualities/principles of sync:

• One-to-one coupling grows to many-to-many coupling.
• Catalysts tend to initially sync some players but desync others further.
• If one oscillator kicks another over a threshold, they will remain synchronized forever.
• Self-organized criticality and cascading behavior (described by Per Bak’s statistics of cascades).
• Frequency pulling (from Norbert Wiener): the tendency of oscillators to pull stray patterns into sync with the group is the universal mechanism of self-organization.
• Influence function (amplitude), sensitivity function (tendency to speed up or slow down), and level of connectivity shape the individual behaviors of players in the system and the time it takes them to self-organize (Winfree).
• Whenever the whole is different from the sum of the parts; whenever cooperation or competition is going on, the governing equations are nonlinear.
• A threshold of similarity is equivalent to a “phase transition.”
• Kuramoto’s rule: oscillators symmetrically adjust by making the minimum adjustment that allows them to communicate.

Disturbances to an equilibrium system grow as a function of the similarity of the individual players; if the players are nearly identical, the disturbances grow exponentially.

Link between biology and physics: “mutual syncronization is analogous to a phase transition, like the freezing of water into ice. The main difference is that when oscillators freeze into sync, they line up in time, not space.”

Frequency pulling tends to produce a pattern distribution that is unlike the familiar bell curve; instead it has a tall, narrow central peak and two weak peaks on either side—this is a possibly a description of a “standard” distribution to a synchronized or self-organized system.

“Virtually all major unsolved problems in science today have this intricate character…a complex, self-organizing system where everyone changes the state of everyone else.” Examples cited: biochemical cell reactions that lead to cancer; stock market booms and crashes; emergence of consciousness from firings of brain neurons; origin of life in the chemical reactions of the primordial soup.

Kuramoto’s rule in more detail: the amount of adjustment between pairs of oscillators is given by the sine function of the ange between them, multiplied by a number called the “coupling strength,” which determines the maximum possible adjustment. Breakthrough in this idea was the symmetrical relationship between oscillators, compared to Winfree’s concepts of frequency pull and sensitivity.

Kuramoto continued: all systems will migrate toward a state in which the order parameter and speed of the pack are constants. There are ultimately only two such states: an order parameter of 0, in which the system will never display synchrony; a “partially synchronized” state consisting of three groups: a synchronized pack of average speed, a slower desynchronized swarm of dawdlers, and a faster desynchronized swarm of sprinters. This latter case is possible only up to a certain threshold of diversity. You can predict how ordered the pack will be as a function of with width of the bell curve.

David Welsch & Steve Reppert (Mass Gen Hospital): “the brain contains a population of oscillators with distributed natural frequencies which pull one another into synchrony and make a more accurate oscillator en masse than individually. Wiener anticipated all that, but he missed an important detail: Instead of cycling 10 times per second, these cells cycle about a million times slower. These are the cells of the circadian pacemaker, the internal chronometer that keeps us in sync with the world around us.”

Strogatz’s breakthrough idea was to view oscillators as fluids.

Sync and cooperation:

“Reproductive sync has benefits for all if the females in the group are cooperative…It could be that women unconsciously strive to ovulate and conceive in step with their friends (to allow them to share child-rearing and breast-feeding duties) and to keep out of step with their enemies (to avoid competing with them for scarce resources)….Female rats in a synchronized group produce larger and healthier offspring than those reared by a solo mother.”

Cooperation in the context of oscillators means ability to sense one another’s rhythms and react to stay in step. [implications for growth of sensors?]

“When the system was self-synchronizing, Winfree found that no oscillator was indispensable. There was no boss. Any oscillator could be removed and the process would still work. Furthermore, the pack did not necessarily run at the speed of its fastest member. Depending on the choice of influence and sensitivity functions, the group could run at a pace nearer the average speed of those in the pack, or it could go faster or slower than any of its members. It was all wonderfully counterintuitive. Group synchronization was not hierarchical, but it wasn’t always purely democratic either.” (p. 52-53).

Human problems that sync can help explain, solve, interpret:

What causes fads, crowd behavior, and mob psychology?

While much of sync theory focuses on rhythmic phenomena, repeating the same cycles, human behavior is more complex. Thresholds are a focus here. Relevant research comes from:

• Thomas Schelling: discoverer of the “tipping point.”
• Mark Granovetter: rioting behavior (interesting that it is hard to distinguish a person who will readily join a riot from one who will join only after someone else starts rioting; i.e., normal sociological and psychological segmentations won’t catch the difference); also the threshold is quite low.
• Duncan Watts: innovation modeling by network analysis reveals two tipping points: 1) islands link together creating global cascades; 2) a dilution effect when cascading vanishes entirely because a node has too many neighbors. Also the concept of a “vulnerable cluster” which is equivalent to “early adopters.”

Traffic congestion

A basic chaos theory problem; key research comes from:

• Dirk Helbing and Bernardo Huberman: auto traffic synchronizes at a certain density, with large trucks setting the gating speed.
• Boris Kerner and Hubert Rehborn: traffic slows to crawl as on-ramp increases density, but stays in that sync for two hours after the heavy on-flow, “trapped” in a stable, but suboptimal sync; they have to be “defibrillated” in order to regain free flow.

Intentional collective action:

Examples of this are dance, singing, “waves” at football games, audience applause (in Europe); and on the dark side: totalitarianism: Nietszsche: “In individual, insanity is rare, but in groups, parties, nations, and epochs it is the rule.”

How the brain gives rise to the mind:

Acts of cognition are linked to brief surges of neural synchrony.

• Christoph von der Malsburg (USC) asked what physical process binds all the chaos of sensations to allow the perception of a single object. Hypothesis: separate banks of neurons throughout the brain all oscillate in sync for a fraction of second.
• Charles Gray and Wolf Singer: tests hypothesis with cats.
• Jurgen Fell (U. of Bonn) tests human subjects (epileptics with implanted electrodes), found link between sync activity on first viewing words to be memorized and ability to recall later.
• Francisco Varela: “mooney face” experiments reveal a flurry of “gamma oscillations” that mark the moment of perception, recognition occurs when discharges in multiple regions of the brain sync, followed by “active desynchronization.”
• Christof Koch and Francis Crick: Consciousness may be the subjective experience of these states of synchrony passing through our brains.

Insect studies on emergent intelligence in swarms of unintelligent actors has practical relevance to distributed computing, robotics, and other applications; for example, foraging insects use pheromone trails to select the shortest paths to food, a strategy that has been used to solve the famous "traveling salesman problem" in computer science. Systems with distributed collective intelligence are more robust because they can adapt quickly to a variety of situations.

Foraging ants select the shortest paths to food. They are so efficient that ant models have been used to solve the famous “traveling salesmen problem,” a classic in computer science, which concerns finding the shortest route that will take a salesman through a group of cities. Successive iterations over path networks (paths that have been discovered) results in the shortest routes getting reinforced and the longest ones getting abandoned. The outcome is an optimal path length for ant foraging.

Also, artificial ants provide the best solution to the classic quadratic assignment problem, in which the manufacture of a number of goods must be assigned to different factories so as to minimize the total distance over which the items need to be transported between facilities. There exist many such “optimization problems”, such as telephone routing. Also, individual robots have been programmed to push a box to a destination without communicating.

In another project, a model that was initially introduced to explain how ants cluster their dead and sort their larvae has become the basis of a new approach for analyzing financial data. “The ant-based approach enables the data to be visualized easily, and it boasts one intriguing feature: the number of clusters emerges automatically from the data, whereas conventional methods usually assume a predefined number of groups into which the data are then fit. Thus, antlike sorting has been effective in discovering interesting commonalties that might otherwise have remained hidden.”

Again using a biological system as a model, scientists have devised a technique for scheduling paint booths in a truck factory. The method optimizes variables like paint usage and time spent, as well as implementing load-sharing between paint booths in the case of breakdowns.

“Indeed, the potential of swarm intelligence is enormous. It offers an alternative way of designing systems that have traditionally required centralized control and extensive preprogramming. It instead boasts autonomy and self-sufficiency, relying on direct or indirect interactions among simple individual agents. Such operations could lead to systems that can adapt quickly to rapidly fluctuating conditions.”

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.

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."

Analytical results from modeling the fitness consequences of two decision-making mechanisms, despotism and democracy, shows that generally despotic models leads to higher costs than democratic models because despotism produces more extreme decisions than democracy. "Even when the despot is the most experienced group member, it only pays other members to accept its decision when group size is small and the despot's average error is lower than the average median error of all other group members."

Research has largely assumed despotism because social structures among animals are commonly hierarchical and the ability to vote and to count votes is not obvious. However, there is mounting empirical evidence of voting in the animal world by body postures, ritualized movements, and vocalizations, as well as vote counting in the form of summing up votes, integration of votes up to an intensity threshold, and averaging of votes.

An important context in which social animals have to make group decisions is activity synchronization, e.g. red deer herds have to decide when to end rumination and move on. The model assumes that (1) synchronization costs increase linearly with the difference between when an individual would have preferred to stop and when the group actually stops, and (2) costs of stopping to early or too late are symmetrical. Even when relaxing assumption (1) costs are still higher for despotic than for demographic groups in most cases. When relaxing assumption (2) a democratic majority rule different from simple majority that reflects the asymmetry between "too early" and "too late" costs is least costly.

These results are fairly robust with respect to group heterogeneity, energy needed for enforcement, and individuals having incomplete information about their own optimal activity duration. The model predicts that democracy gives groups a competitive advantage and due to natural selection should be quite common in social groups of animals.