System+behaviour

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Communities as networks - Networks of individuals within the community and networks of communities acting regionally or with common purpose are models of resiliance: They maximise the interactions of individuals and the resources available to them thereby maximising individual resilience. Dunbar's number is a limitation of the number of effective relationships that can be maintained by an individual; effective organisation is limited to three levels of hierarchy - the village, the tribe and the body politic - but networks extend these resources exponentially.

Multiple interactions among variables, positive and negative feedback loops, and non-linear system dynamics determine the health and behavior of individuals, populations, and entire ecosystems. System behavior depends on specific circumstances and often fluctuates around a mean. However, exaggerated oscillations or near-threshold conditions can create vulnerability to small perturbations that can propel the entire system into new dynamic operating conditions. Studies that ignore details of system conditions will miss important real-world determinants of health in complex interactive systems.

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all biological systems are guided not by top-down governors or control mechanisms but by feedback from the networks in which they are embedded. This is how nature regulates, preserves, and evolves itself towards greater adaptability. There is no fallible ruler driven to resource over-reach and myopic certainty. There is only the ongoing trial & error of embedded growth tempered by continuous communication between & within organisms. []

John F. Schmit, a military consultant and writer who has been closely associated with Marine Corps doctrine since 1986, gave a lecture at the National Defense University in 1998 titled __ //Command and (Out of) Control: The Military Implications of Complexity Theory// __. He concluded that military success required a shift from the prevalent mechanical world view: > The physical sciences have dominated our world since the days of Newton. Moreover, the physical sciences have provided the mechanistic paradigm that frames our view of the nature of war. While some systems do behave mechanistically, the latest scientific discoveries tell us that most things in our world do not function this way at all. The mechanistic paradigm no longer adequately describes our world—or our wars. Complex systems—including military organizations, military evolutions, and war—most definitely do not behave mechanistically. Enter complexity. > Complexity encourages us to consider war in different terms which in turn point to a different approach to the command and control of military action. It will be an approach that does not expect or pursue certainty or precise control but is able to function despite uncertainty and disorder. If there is a single unifying thread to this discussion, it is the importance of adaptation, both for success on the battlefield and for institutional survival. In any environment characterized by unpredictability, uncertainty, fluid dynamics, and rapid change, the system that can adapt best and most quickly will be the system that prevails. Complexity suggests that the single most important quality of effective command and control for the coming uncertain future will be adaptability.

Author(s) / Editor(s)
[|Powell, Walter W.] > Network forms of organization, with reciprocal patterns of communication and exchange, are alternatives to hierarchically or market based governance structures; they are more suited to describing companies involved in an intricate latticework of collaborative ventures with other firms over extended periods of time. Published in/byResearch In Organizational Behavior, Vol. 12, pages 295-336Date1990

Findings
Network forms of organization, with reciprocal patterns of communication and exchange, are alternatives to hierarchically or market based governance structures; they are more suited to describing companies involved in an intricate latticework of collaborative ventures with other firms over extended periods of time. Hierarchies are suited to transactions that involve uncertainty, recur frequently, and require substantial “transaction-specific investments”. Markets are suited to exchanges that are straightforward, non-repetitive, and require no transaction specific investments. These “alliances” aim at creating indebtedness and reliance over the long haul: your current collaborator will be your competitor in other domains (or in the same domain) over time. In markets, the strategy is to drive the hardest possible bargain in the immediate exchange. Commitment is low. Network organizations are more social than markets and hierarchies, they are dependent on relationships, mutual interests, and reputation. They are less guided by a formal structure of authority. Successful networks involve complementarity and accommodation. Reputation, friendship, interdependence, and altruism are integral. The most useful information comes from people you have dealt with in the past rather than from the formal chain of command. Conflicts are resolved by haggling in markets; administrative fiats in hierarchies; norms of reciprocity and reputational concerns in networks. Markets offer choice, flexibility, and opportunity. Prices determine production and exchange. Hierarchies are well-suited for mass production and distribution. Networks are more flexible than hierarchies. Transactions occur through networks of individuals engages in reciprocal, preferential, mutually supportive actions. Reduction of uncertainty, fast access to information, reliability, and responsiveness are paramount concerns that motivate participants in network organizations. Know-how, the demand for speed, and trust are critical components of successful network organizations. Examples of network forms: Know-how, the demand for speed, and trust are critical components of successful network organizations.
 * Network forms of organization, with reciprocal patterns of communication and exchange, are alternatives to hierarchically or market based governance structures.
 * Network organizations: More social than markets and hierarchies, they are dependent on relationships, mutual interests, and reputation. They are less guided by a formal structure of authority.
 * Successful networks involve complementarity and accommodation. Reputation, friendship, interdependence, and altruism are integral. The most useful information comes from people you have dealt with in the past rather than from the formal chain of command. Taking a long term perspective enhances reciprocity.
 * Reduction of uncertainty, fast access to information, reliability, and responsiveness are paramount concerns that motivate participants in network organizations.
 * Know-how, the demand for speed, and trust are critical components of successful network organizations.
 * Craft industries (construction, publishing, film and recording industries)—facilitated by loyalty to the profession and to project teams. Informal trading of proprietary expertise is common among members of a profession in different organizations.
 * Regional Economies and Industrial Districts (German textiles, Silicon Valley)—Rich array of support services. Pooling resources on basic research through consortia and trade associations. Encouragement by local government. Proximity to centers of higher education.
 * Strategic alliances and Partnerships (Oil and gas, chemical and pharmaceuticals, commercial aircraft)—share risk and expense. Cooperative relationships with suppliers. Gain fast access to new technologies or markets. Needs to deal with anti-trust concerns.
 * Vertical disaggregation. (Downsizing, outsourcing)—Increased flexibility in responding to technological change and commodification of products.
 * Know-how and detailed knowledge of the abilities of others who possess similar or complementary skills thrive in networks. Exchange of competencies is more likely to occur in networks; exchange of tangible resources is more likely to occur in market transactions or among units in a hierarchy.
 * Demand for speed is facilitated by a network’s strengths in offering fast access to information, flexibility, and responsiveness to changing tastes. Information flow through a network is freer and richer than in more tightly controlled markets or hierarchical organizations.
 * Trust develops when there is a high probability of future association. There is a higher probability of cooperation and also a willingness to punish those who do not cooperate.

Author(s) / Editor(s)
[|Wright, Robert] > Wright applied to the history of civilization the same game theory that Axelrod had used to explain biological and social phenomena, concluding (controversially), that humans throughout history have learned to play progressively more complex non-zero-sum games with the help of technologies like steam engines and algorithms and metatechnologies like money and constitutions Published in/byPantheonDate1999

Findings
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."
 * Social complexity evolves because it brings benefits to those who participate, and one of those benefits is the capacity for increasing social complexity
 * Humans have built societies of increasing power and complexity by creating technologies, institutions, and social contracts that enable us to cooperate in new ways, on larger scales, to produce greater benefits to more people: zero-sum games. The evolution of human capacities for inventing, elaborating, diffusing nonzero-sum games is a lens for looking at a powerful driver of history.
 * Technologies, from plows to alphabets, have produced both physical power and new opportunities for complex collective action.
 * Metatechnologies such as capital markets, constitutions, and science have created both concentrations and decentralizations of wealth and power – zero-sum games don't make zero-sum competition go away. The two modes co-evolve.
 * Nonzero-sum games influence the environment to become more conducive to nonzero-sum games.
 * Nonzero-sum games are tools for overcoming obstacles to collective action.
 * Innovation, exploration, investment, persuasion, politics are tools for initiating, maintaining, increasing cooperative game-playing.
 * The evolutionary advantages of reciprocal altruism on the biological level are potentiated when they drive the development of human mental capacities such as remembering who owes you and who is a friend; increases in the mental capacity for social complexity enables the elaboration of more complex forms of social cooperation: tit-for-tat plus emotion plus mental capacity equals alliances, friendships, societies.
 * Emotions like friendship, love, and envy; traits such as trust, cheating, and punishment; and concepts such as justice and fairness can be seen as the mythic narratives humans tell ourselves to explain mechanisms we've invented for inventing, elaborating, and maintaining cooperative arrangements.
 * Just as other biologically-originated traits, such as evolution itself, have become the objects of reason, knowledge, nonzero-sum games have moved from unconscious to reasoned and planned. Understanding technologies and metatechnologies of cooperation makes it possible to design more powerful forms.

Author(s) / Editor(s)
[|Watts, Duncan] > Healthy social, technical, biological and professional networks are built on cooperative frameworks that enable them to quickly spread information and phenomena regardless of beneficial or malicious intent; this appears to be a deep structural characteristic of "small-world" or "scale-free" networks that have a relatively small number of hubs that enable extensive interconnectivity across large numbers of nodes. >

Author(s) / Editor(s)
[|Reed, David] >> Reed's Law states that communications networks that connect groups (as opposed to peers) create value that scales exponentially with network size. >> >> 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. >> From this Reed concludes that because Group Forming Networks create more value (for users) than either broadcast or peer networks, they will out perform other forms of network connectivity both in ability to gain attention and in return on investment for businesses >> >> >> == Summary of: The Wisdom of Crowds == >> == Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations == >> === Author(s) / Editor(s) === >> [|Surowiecki, James] >>> James Surowiecki argues that with the proper structure and characteristics, large groups of ordinary people can outperform small groups of experts in making decisions and predictions. >> Published in/byRandom HouseDate2004 >> >> === Findings === >> James Surowiecki argues that with the proper structure and characteristics, large groups of ordinary people can outperfom small groups of experts in making decisions and predictions. Through numerous examples (Iowa’s electronic prediction market, the Hollywood Stock Exchange, The Bay of Pigs decision, NASA’s Columbia disaster, football strategy, corporate decision making, and others) Surowiecki discusses the weaknesses of traditional decision making and shows how collective wisdom can be aggregated from a large, diverse group of people who don’t necessarily possess expert knowledge traditionally associated with effective problem solving. His view is contrary to popular, and corporate, assumptions that specialized experts in small deliberative groups are better able to make effective decisions. He proposes that narrow expertise is not fungible to other decision domains or contexts, and that expertise in “decision-making” is a poorly conceived notion. Indeed, large groups can be wiser than small cadres of experts even if they are not well informed or very rational. He proposes four key attributes that are necessary for effective large group collective wisdom: diversity of the group, independence of opinion and conclusions that is free of manipulative and corrupting influence, decentralization of the group, and bottom up processes that aggregate information. Surowiecki uses these attributes to show how collective decision making are effective is solving three distinct types of problems: cognitive, coordination, and cooperation. >> >> ==== Conditions for creating collective wisdom ==== >> >> ==== Types of problems best solved through aggregating collective wisdom ==== >> **Cognitive:** These are factual questions with definitive solutions in the present or in the future. Who will win the US Presidential election? How much does this hog weigh? Will an invasion of Cuba be successful or not? Which technology platform will succeed? Challenges to solving cognitive problems include group think, or herding, when members of a group receive and use undue influence on each other that prevents incorporating new, deviating, or controversial information into the decision making process. Information cascades occur when members of a group make decisions in sequence rather than simultaneously and undermines independent opinion and judgement. >> **Coordination:** These are problems or challenges that involve structuring individual actions in way that they take a shared course of action. Individual actions are interdependent; what one person does depends and affects what everyone else will do. Coercion and authority are two ways of solving these problems but Surowiecki suggests that in liberal societies bottom up methods are more amenable to social norms. Examples include finding a common place to meet in a busy city (an example of a focal point or Schelling point), first come, first serve seating or cues, and flocking. Solutions to these problems resemble what Frederick Hayek called, “spontaneous order.” >> **Cooperation:** These problems involve organizing individuals’ self-interested action in a way that creates mutual advantage. Examples of these problems include paying taxes and curbing pollution. A key to solving cooperation problems involves establishing and communicating trust. As Surowiecki states, to solve cooperation problems, a group or society needs to “ be able to trust those around them, because in the absence of trust the pursuit of myopic self-interest is the only strategy that makes sense.” Thus cooperation problems require groups to do more than in coordination problems. >> >> == Summary of: When Push comes To Pull: The New Economy and Culture of Networking Technology == >> === Author(s) / Editor(s) === [|Bollier, David] >>> Information and communication technology innovation have begun to transform commercial business and social institutions from a "push" technology approach (hierarchical "center out"), to a "pull" technology approach (networked -based and decentralized). This poses new challenges to social, political, and educational systems that are largely designed to support "push" economies. >> . >>
 * Specialized expertise tends to be over valued. In fact, large groups, structured properly, can be smarter than the smartest member of a group. On average, the wisdom of crowds will come up with a better answer than any individual could provide.
 * Local knowledge is often critical for solving cognitive problems. Mechanisms of aggregating distributed, independent local knowledge can provide important insight for solving these problems.
 * Major corporate decisions should be informed by decision markets, not made by them. But when decisions are made, power should not be concentrated in the hands of one person. The more important the decision, the more important it is that it not be left in the hands of a single person.
 * A group’s intelligence depends on a balance of independent information that each member holds and common information that everyone in the group shares. The combination of independent information, some right and some wrong, helps to keep the group smart.
 * 1) diversity of opinion (each person should have some private information, even if it's just an eccentric interpretation of the known facts).
 * 2) independence (people's opinions are not determined by the opinions of those around them).
 * 3) decentralization (people are able to specialize and draw on local knowledge).
 * 4) aggregation (some mechanism exists for turning private judgments into collective decision).