John Boik: It’s not often that a scientist gets to use the words love, creativity, and wisdom in a paper, especially when writing about economics. Perhaps that’s because economics, the dismal science, is obsessed with dismal systems — make that abysmal systems, relative to need.

To be clear, I’m not speaking of the specific policies of the US, the EU, China, the World Bank or others. I’m speaking of dominant economic systems as wholes — especially their underlying conceptual models (macro and micro) and the world views upon which they are based.

A human has only so many minutes in life. Time is the bedrock scarcity. If a person isn’t doing something meaningful in a given moment, he’s doing something less than meaningful. He’s wasting at least some of his potential. By meaningful, I don’t mean productive, in an economic sense. I mean important to the person, to her own wellbeing. The Chilean economist Manfred Max-Neef identifies nine categories of human need: subsistence, protection, affection, understanding, participation, leisure, creation, identity, and freedom. Others might make a slightly different list, but the important concept is that meaning stems from addressing real human needs.

It’s not that we should be doing something meaningful with our time, it’s that we want to. We want to express and receive affection, for example, and to fulfill the other eight needs. We want to, that is, unless external pressures so exhaust, distract, distort, or confuse us that we lose touch with who we are.

Current economic systems are dismal-abysmal because they waste our precious time. As a case in point, only 13 percent of workers worldwide are engaged in their jobs. This means, in effect, that 87 percent of workers feel more or less forced to go to work. Short of force, why would someone spend half their waking hours (or more), day after day, doing something that didn’t engage them?

Except for receiving a paycheck, it appears that most workers don’t really care about their jobs. That’s not surprising. Work doesn’t count as a real human need. It’s only a vehicle by which some needs can be (but for most people aren’t) met. Work doesn’t meet our needs because economic systems, as they exist, didn’t evolve to fulfill the real needs of ordinary people. They evolved largely under pressures exerted by powerful people and groups who wanted to maintain and expand their own privileges.

Suppose that we pause to reevaluate. Using insights from psychology, environmental sciences, public health, complex systems science, sociology, and other fields — that is, using as clear and scientifically sound a picture as we can muster of what humans and natural environments actually need in order to thrive — we can ask ourselves the following question: What economic system designs, out of all conceivable ones, might be among the best at helping us meet real needs?

Strange as it might sound, this question is rarely asked in academia, the science and technology sector, or elsewhere. Or if it is asked, the investigation usually lacks imagination. Surely we can move beyond a discussion of capitalism vs. socialism, as if these were the only two possibilities. A wide-open, largely unexplored space of interesting, potentially viable systems exists.

In my recent paper, “Optimality of Social Choice Systems: Complexity, Wisdom, and Wellbeing Centrality,” I call on the academic community, and science and technology sector, to begin a broad exploration in partnership with other segments of society into what optimality means with respect to economic and political system design. I term this nascent program wellbeing centrality, due to the central role that the elevation of wellbeing would play in systems that help us to fulfill real needs.

Viewed abstractly, economic and political systems are problem-solving systems. One could call them technologies of a sort. As such, they are subject to scientific inquiry and engineering innovation aimed at discovering new designs that improve problem-solving capacity. Further, if we seek ideas for new designs, we don’t have to look far. Nature provides a blueprint.

From a complex systems science perspective, the environment is replete with successful problem-solving systems (cells, organisms, immune systems, ecologies, and so on). Although all look different physically, successful systems tend to exhibit similar underlying mathematical properties. That is, nature has hit upon a good problem-solving approach, and repeats it widely. If we wish our problem-solving systems to be successful, to be as good as they can be, we might want to pay close attention to what nature does.

Moreover, we can view the eight needs Max-Neef identifies as gifts of nature, stemming from eons of evolution over countless ancestral species, to help us focus on and solve problems that matter. Our need to express and receive affection, for example, is also responsible, in part, for our tendency to seek cooperation in solving difficult problems.

Engage global, test local, spread viral

In short, “good” economic systems would produce economies of meaning that help us to help one another live meaningful lives — to meet real needs and solve problems that matter.

We don’t have much time to make a transition from current systems to better ones. Mass extinction and other global catastrophes loom on the horizon. We face the unthinkable, not so much because a few CEOs, companies, or politicians have acted greedily (some have), but rather because today’s problem-solving systems didn’t evolve to help us meet real needs. They waste our precious time, as mentioned, rather than focusing our talents and natural drives on things that do matter, such as caring for others and the planet.

But how do we get from here to there? No matter how promising the design of a new system might be, it would be unreasonable to expect that a nation would abruptly drop an existing system in favor of a new one. Nevertheless, a viable, even attractive strategy exists by which new systems could be successfully researched, developed, tested, and implemented. I call it engage global, test local, spread viral.

Engage global means to engage the global academic community and technical sector, in partnership with other segments of society, in a well-defined R&D program aimed at computer simulation and scientific field testing of new systems and benchmarking of results. In this way, the most profound insights of science can be brought into play.

Test local means to scientifically test new designs at the local (e.g., city or community) level, using volunteers (individuals, businesses, non-profits, etc.) organized as civic clubs. This approach allows testing by relatively small teams, at relatively low cost and risk, in coexistence with existing systems, and without legislative action.

Spread viral means that if a system shows clear benefits in one location (elimination of poverty, for example, more meaningful jobs, or less crime) it would likely spread horizontally, even virally, to other local areas. This approach would create a global network of communities and cities that cooperate in trade, education, the setup of new systems, and other matters. Over time, its impact on all segments of society would grow.

Cities, big and small, are the legs upon which all national systems rest. Already cities and their communities are hubs for innovation. With some further encouragement and support, and the right tools and programs, they could become more resilient and robust, and bigger heroes in the coming great transition.

By John Boik, PhD. To learn more about the wellbeing centrality R&D program, the LEDDA economic democracy framework, or to download (free) Economic Direct Democracy: A Framework to End Poverty and Maximize Well-Being (2014), visit http://www.PrincipledSocietiesProject.org.

Please share and republish. Originally published at www.principledsocietiesproject.org.

Photo by unconventional_paint

The post An Economy of Meaning, or Bust appeared first on P2P Foundation.

This article looks at three crucial insights for the future of economics:

• Complex adaptive systems
• How technologies of cooperation enable commons-based peer-to-peer networks
• Why we need complex adaptive systems to understand new economies

Complex Adaptive Systems

The Edge of Chaos

Complex adaptive systems has enjoyed considerable attention in recent decades. Chaos theory reveals that out of turbulence and nonlinear dynamics, complex systems emerge: order from chaos.

We learned that complex systems are poised on the “edge of chaos” and generate “order for free” (Stuart Kauffman). They are composed of many parts connected into a flexible network. As matter and energy flow through, they spontaneously self-organize into increasingly complex structures. These systems, continuously in flux, operate “far from equilibrium” (Ilya Prigogine). Beyond critical thresholds, differences in degree become differences in kind. “More is different.” (Phil Anderson)

Complexity science reveals the difference between prediction and attraction. We can know that a marble in a bowl will reach the bottom even though we cannot predict its exact path because of sensitivity to initial conditions. Deterministic chaos means path dependence, where future states are highly influenced by small changes in previous states. A typical economic example is the lock-in of the now-standard “QWERTY” keyboard.

Networks

We see network effects: adding another node to a network increases the value of all other nodes exponentially, because many new connections are possible, economically “increasing returns to scale” (Brian Arthur). Reed’s Law goes even farther, because new groups can be formed, exhibiting a much greater geometric growth. We know about “small-world,” or “scale-free,” networks, so called because there is no statistic at any scale that is representative of the network as a whole, e.g. no bell-curve average, but instead a “long tail,” mathematically a logarithmic “power law.” Some networks are robust to random failures but vulnerable to selective damage, i.e. network attacks that target nodes with a higher centrality. Furthermore, “centrality” means different things inside different network topologies. Network structure affects the frequency and magnitude of cascades. Like avalanches in sand piles, power laws create “self-organized criticality” (Per Bak).

Information Landscapes

Complex systems constitute “fitness landscapes,” exhibit cycles of growth and decline, are punctuated by explosions of diversity and periods of stasis, and show waves of ebb and flow, seen in traffic patterns. On fitness landscapes, algorithms that pursue merely maximization, without the ability to observe remote information from the landscape, freeze in local optima. Without system diversity, there is no improvement. Swarms escape because they not only read information from the landscape but also write to it, creating shared information environments.

Landscapes and occupants impart selection pressures on each other. Good employees and good jobs both outperform bad ones. Agents and strategies evolve. Adaptation can become maladaptation when selection pressures change.

Dynamics and Time

When we study the spread of disease through a forest we see a slow progression of infected trees.However, when we study the spread of fire, we see the same pattern enacted much faster.

Complex systems and their dynamics are not new. What is new is that human systems have accelerated to the point where political, economic, and social changes now occur rapidly enough to appear within the threshold of human perception. We change from slow social movement to an era of “smart mobs.” Consequently, while it may be true that we did not need the tools of complex systems in the past, because economic change was slow and did not require a dynamical viewpoint, the current speed of economic change demands this new lens.

The Emergence of Commons-Based Peer-to-Peer Networks

A crucial global economic phenomenon is the rise of commons-based peer-to-peer networks. “Technologies of cooperation” (Howard Rheingold) enable people to self-organize in productive ways. Open-source software was one first clue to powerful new ways of organizing labor and capital. “Commons-based peer-production” is radically cost-effective (Yochai Benkler). By “governing the commons” (Elinor Ostrom), shared resources managed by communities with polycentric horizontal rules, without reliance on either the state or the market, escape the “tragedy of the commons.” Our thinking about production, property, and even the state, must evolve to reflect the growing participatory economy of global stewardship and collectively-driven “platform cooperatives” (Michel Bauwens). New commons include food, energy, “making,” health, education, news, and even currency.

The rise of 3D printing and the Internet of Things combined with participatory practices yields new forms of value production, paralleling new forms of value accounting and exchange. We witness a “Cambrian explosion” of new currency species, like BitCoin, and innovative trust technologies to support them: the blockchain and distributed ledgers. Just as 20th century electrical infrastructure remained fragmented until standards enabled a connected network (Thomas Hughes), new infrastructure matures when separate solutions merge and the parts reinforce the stability of the whole.

The Future Fate of Economics

Economics as a discipline can only remain relevant as long as it can provide deep engagement with contemporary reality. Overly-simplified models and problematic axioms cannot guide us forward. The world is an interwoven, heterogeneous, adaptive “panarchy.”

Harnessing complexity requires understanding the frequency, intensity, and “sync” of global connectivity. Analyzing many futures demands better tools. To analyze “big data,” first we need data. Complexity science utilizes multi-agent simulations to investigate many outcomes, sweep parameters, and identify thresholds, attractors, and system dynamics. Complexity methods provide unique metrics and representations, animated visuals rather than static graphs.

This is not just big data; it’s dynamic data. With distributed systems, it becomes peer-to-peer data: shared infrastructure. Just as ants leave trails for others, shared infrastructure bolsters interoperability through a knowledge commons. Restricting connectivity and innovation, e.g. with intellectual property rights, carries extreme costs now. Fitness impedes uncooperative agents and strategies. Fortunately new commons have novel “copyleft” licenses already, promoting fairness and equity.

Complexity science shows us not only what to do, but also how to do it:  build shared infrastructure, improve information flow, enable rapid innovation, encourage participation, support diversity and citizen empowerment.

This article was originally published in cooperation with the Organization for Economic Cooperation and Development (OECD) at The Future of Economics: From Complexity to Commons and on OECD Medium at The Future of Economics: From Complexity to Commons

To engage with the original please go to The Future of Economics: From Complexity to Commons by Paul B. Hartzog

The post The Future of Economics: From Complexity to Commons appeared first on P2P Foundation.

Complex Systems and the Energy Commons

In this article, we look at the future of the Energy Commons, and how using a complex adaptive systems lens can lead to effective solutions. As an example, I’m going to demonstrate how this method can lead us to a solution I call “swarm micro-solar.”

Complex adaptive systems as a lens tells us that the dynamic energy solution of the future should:

• be made up of many small components
• be fluid and flexible, utilizing component diversity
• be aware of and respond to its environment, by being mobile

From a complex systems perspective, we can predict the properties of a future solution even though we do not yet know the details. We can know that a marble in a bowl will settle at the bottom even when we cannot predict the specific path it will take. We can know that a percentage of a population will become adults even when we cannot know which ones will survive.

Similarly, we can understand that “small pieces loosely joined” should be more efficient even though we do not yet know precisely how. The reason this works is because all complex systems exhibit instances of deeper patterns found in the universe.

Disaggregate the Solar Panel

Our first understanding above was:

1. be made up of many small components

So, take, for instance, the solar panel. A single solar panel converts solar energy into usable electricity but suffers from the loss of some of that energy as heat. Solar panels don’t work when they exceed their tolerance thresholds for heat buildup.

Heat radiation occurs on the edges of the solar panel, so more edge length means more radiation and a cooler system. It just so happens that an array of smaller panels:

• covers nearly the same area, and so generates about the same amount of energy, and
• has significantly more edge length and so radiates heat more effectively and can run for longer.

In addition, these smaller pieces can be individually enabled not only to produce energy but also to store it, using individual mechanisms (such as batteries). Energy could be “uploaded” into larger storage networks when the individual units are in range of an upstream connection to the Energy Commons. Since the swarm components are connected horizontally, only one component would have to be in range in order for the entire system to communicate upstream.

Decenter the Solar Array

Our second understanding above was:

2. be fluid and flexible, utilizing component diversity

So the next step would be to detach the entire solar array from it’s “center” and instead connect the parts directly to each other. There are two ways to operationalize this:

• connect them together physically into a “mesh” or “net”
• connect them together virtually into an information network

Physically connecting them could be advantageous if you needed them to exist as a single unit for some reason. More useful however would be to connect them digitally into a “swarm.” A swarm of panels could communicate information about the sunlight they are converting, local conditions, etc. Moreover, you could even have the units send energy to one another to balance the energy storage. In other words, a unit that has more storage available could store energy for one that has less storage available.

The effect of horizontal connectivity is to make the entire system function like a brain. The swarm could essentially “rewire” itself by monitoring inequalities in the system and balancing its members’ behavior accordingly.

Detach the Swarm

Our third understanding was:

3. be aware of and respond to its environment, by being mobile

Solar panels need sunlight. The earth rotates. The complex adaptive systems lens suggests that the system should be able to perceive its environment and adjust its collective behavior accordingly. For example, slime molds exist as individual cells, but when changes in resource conditions demand, those cells come together to form a multi-cellular organism, which is mobile, and can move elsewhere to a better resource environment.

So, too, can our solar array. If it is a flying drone array, then it could be positioned in the sky as an actual swarm.

• It could move away from clouds or other obstacles, and even orbit the planet in order to avoid ever being on the dark side. A swarm of swarms, all autonomous but capable of cooperation and communication, could effectively perceive their environment and adjust accordingly to target better environments.
• Also, the diversity of the units would enable them to behave differently as individuals. Each unit could angle itself into the sun, or adjust to wind conditions, etc. Because every component adjusts its own behavior in response to every other component, individual behaviors would create systemic effects. Just as a swarm can fly around obstacles without a leader, so, too, could a micro-solar swarm dynamically adjust to changes in its environment.

D-words and Micro-Solar Swarms

This article has demonstrated how using complex adaptive systems as a lens can lead to an innovative solution in the Energy Commons. We focused on a language of:

1. Disaggregate
2. Decenter
3. Detach

There are many other facets to a fully-developed and organically evolving Energy Commons. There are other solutions, and there are also other commons (food, things, etc.).

We gain a significant advantage when we realize that solutions across these commons exhibit the patterns seen in complex adaptive systems, and when we focus on a “pattern language” for those future solutions.

I hope this article contributes to that effort in some small way.

Read More:
If you would like to learn more about robot swarms, take a look at:
https://www.weforum.org/agenda/2016/06/the-bees-of-the-future-that-can-pollinate-and-save-disaster-victims

To engage with the original please go to Solving the Energy Commons with Micro-Solar Swarms by Paul B. Hartzog

The post Solving the Energy Commons with Micro-Solar Swarms appeared first on P2P Foundation.

This article seeks to respond to an important issue that arises a lot in the conversations and spaces in which I participate. Moreover, I think it is timely and important in relation to the divisiveness made apparent by the recent election of Donald Trump to the presidency of the United States.

There is a general usage in our language (which doesn’t necessarily indicate a cognitive consensus) that cooperation and competition are opposites or mutually exclusive. More importantly, there is a conviction that competition and cooperation are somehow ontologically “real,” which is to say that they exist, i.e. that they are a property of the system being observed, rather than a property of the observer.

An alternative viewpoint, however, and one that I find crucial, is that the presence of cooperation or competition is in the eye of the beholder.

We will look at three examples:

1. Predator/Prey interactions
2. Sports
3. The Nation-State system

Predator/Prey

An example from complex systems is illustrative. Take an ecology of predators and prey with complex systems dynamics between, say, wolves, sheep, and grass. There are several competitions happening here.

• sheep compete for grass
• wolves compete to eat sheep
• sheep compete to not be eaten by wolves
• grass competes to not be eaten by sheep

However, out of this complex system we get Lotka-Volterra cycles of the rise and fall of populations. An increase in grass can feed an increase in sheep which, in turn, can feed an increase in wolves. An increase in wolves results in less sheep, which takes pressure off of the grass, but subsequently puts more pressure on the wolf population as food becomes scarce. Populations rise and fall over time, a dance across time. These dynamics have been extended to any system containing resources and consumers of those resources, such as economics. The parts of a systems are always cooperating to maintain the system as a whole in the midst of larger systems and dynamics.

Sports

Another useful example is the dynamic between sports teams in competitive sports. Certainly we are all familiar with the arena in which one sports team competes against another in a match where there is only one winner and one loser. Beneath the surface however there are other complex dynamics occurring.

The resources for both teams are not infinite: financial resources, time, attention, etc. Many resources are in scarce supply. The ecology of sports teams and individual players seeks to maintain its popularity and importance inside larger systems. Sports desires our attention; it requires our resources, and it takes actions in order to achieve those goals, e.g. to keep sponsorships alive, and to keep salaries high. Even when competing, sports teams strive to bolster and sustain the network. Even a simple chess game between friends, while seeming competitive, may serve broader goals of companionship and time spent. When we zoom out from a limited viewpoint, we can see that competitions serve cooperative ends.

The Nation-State System

Another place where competition and cooperation occur simultaneously is in the nation-state system, i.e the realm of international politics. This does not refer to competition and cooperation between states, however. Instead we are talking about a level of understanding that shows that even when states are apparently competing (even when they are at war), their activity, seen through another lens, is fundamentally one of cooperation.

A quote from Hedley Bull is instructive:

“[States’] goal [is] the preservation of the system and society of states itself. Whatever the divisions among them, modern states have been united in the belief that they are the principal actors in world politics and the chief bearers of rights and duties within it. The society of states has sought to ensure that it will remain the prevailing form of universal political organisation, in fact and in right.”

— Hedley Bull, “The Anarchical Society,” 1977, p. 16

For some scholars, this is demonstrably evident with regard to the 1936 anarchist revolution in Spain. Foreign powers, both capitalists and communists, many of whom were already in direct conflict, cooperated to eliminate the success of Spanish anarchism because it was not merely a threat to individual states themselves but, more importantly, a threat to the entire nation-state system’s validity as the dominant means of managing peoples (internally) and international order (externally).

Competition IS Cooperation: Seeing Differently

The crucial consequence of the perspective that I have attempted to illustrate above is this.

Even when we are in conflict with an opponent, there is some cooperative dynamic that is occurring by our acting in relation to that opponent.

For example, in society and politics, when social groups oppose each other with hatred and violence, there are those who benefit. The media and the arms industry supply us with both the pens AND the swords for us to keep the merry-go-round revolving. In addition, the larger system that defines the terms of participation, benefits whenever players slip themselves into predefined slots that the system knows how to handle: predator; prey.

The solution then is neither to disavow competition in favor of cooperation, nor disavow cooperation in favor of competition, but, instead, to realize that:

Competition and Cooperation have no independent existence, i.e. they are not objective properties of the world. Competition and Cooperation are called-forth into being, into the world, only as a function of the way in which we choose to observe a domain.

Consequently, the challenge for us all is to be more cognizant, open and aware, of the contexts in which competition and cooperation are highlighted by our choices. The responsibility lies squarely in ourselves.

In other words:

Competition is Cooperation: See Differently

To engage with the original please go to Competition IS Cooperation: Seeing Differently by Paul B. Hartzog

The post Competition IS Cooperation: Seeing Differently appeared first on P2P Foundation.

The following are the key “lenses” through which I view and discuss the ongoing transformation to panarchy. Each of these lenses provide crucial understandings and insights into facets of panarchy, but panarchy itself emerges only as a result of the interactions between all of these elements. Like all complex systems, panarchy itself is an emergent property.

• Commons
• Complex Systems & Networks
• From the Bell Curve to the Long Tail
• Plurality & Diversity
• Cooperation
• Peer Production
• Open Design

Commons

Commons are systems of shared resources. A lifetime of work by Economics Nobel Prize Recipient Elinor Ostrom reveals a plethora of case-studies with insights and strategies for governing our commons. There are many kinds of commons — ecological, social, information, and technological — but the one thing they all have in common is the need for thoughtful management in order to insure sustainability for future generations.

Complex Systems & Networks

Complex systems and networks are systems that are more than the sum of their parts. Because the parts are interconnected, dynamic relationships between the parts result in emergent properties at the system level. In complex systems “more is different.” Complex systems and networks can range from too rigid to too fluid, but the most interesting of them have mechanisms of self-organization that move them towards a robust and resilient balancing act at the “edge of chaos.”

From the Bell Curve to the Long Tail

The bell curve defines systems with normal distributions where averages are meaningful (because populations are homogenous) and “mass” dynamics are the norm. The long tail, or power law, distribution makes averages meaningless and replaces the “mass” with a plural multitude of diverse members. The transition from the bell curve to the long tail is as relevant in philosophy and culture as it is in economics and politics.

Plurality & Diversity

Plurality refers to the fact that new dynamic systems consist of many interacting parts, whereas diversity refers to the condition that exists when those parts are different. Neither plurality nor diversity is itself sufficient for panarchy, but together they provide an accurate description of the new landscape. This new “multitude” is unlike any civil polity that has existed before, and it will demand infrastructures for governance and economics that are equally unique.

Moreover, governance itself has to exhibit authority, legitimacy, and continuity. We are on the cusp of a “Greek moment” wherein we are faced with the challenge of creating new forms of governance that can be responsive to the needs and demands of a diverse and mobile “global civil society.”

Cooperation

Cooperation is responsible for everything you see around you. Civilization itself would not exist if humanity had not overcome the challenges to cooperation. Much is known about the conditions necessary for cooperation to emerge and succeed, and recently we have seen an explosion of technologies that allow for new forms of cooperation. Much of that cooperation manifests in the new economy where community currencies, smart contracts, and peer production exist in a zone of experimentation and innovation.

Peer Production

Peer production (or as Yochai Benkler terms it “commons-based peer production”) is a new form of bottom-up collaboration to fulfill economic needs and wants. The emergence of “maker” culture is predicated on the consequences of technologies of cooperation. Peer production does not have to be merely economic however. The world of peers produces information at an ever-increasing rate, and also produces new shared understandings, cultural norms, social movements, and political pressures. The new infrastructure that connects people catalyzes peer production in a feedback loop with crucial consequences for our world.

Open Design

Open design refers to the challenge of planning for an unpredictable system what futurist Rick Smyre calls “Preparing for a World that Doesn’t Exist — Yet.” But we can design for adaptability if we follow the insights from Stuart Kauffman’s investigations into evolution and biology. Namely, evolutionary process result in complex systems that maximize their own evolvability. In other words, they evolve to evolve better.
Consequently, Michel Bauwens has claimed that what we need is “an infrastructure for open everything.” This means crafting social and technological systems that are based on a diversity of open standards and are easily extensible. Such an approach insures continuous innovation as landscapes shape their inhabitants and in turn those inhabitants shape new landscapes.

Panarchy: A Multifaceted View

So, how then do these lenses combine to give us a better view of panarchy as a whole?

1. Technologies of cooperation allow human beings to collaborate in ways never before possible, i.e. 1) faster, 2) mobile, and 3) global.
2. A heightened awareness of the climate crisis and the earth as a literal ecological commons compels people to do more with less, i.e. to “make less more” to reduce the combined footprint of 7 billion people by sharing physical as well as information resources. Because technologies of cooperation are ideally suited to building global sharing mechanisms, the result is the emergence of new global commons.
3. Because these new networks are complex systems, they behave ecologically, with similar dynamics, except at faster time scales with larger global reach. In addition, understanding them requires understanding the shift from the bell curve to the long tail.
4. If we are to embrace these changes rather than retreat into an imagined idyllic past, we must embrace both plurality and diversity as core elements of a healthy future civilization. The only structure that can do so is one that operates on what I have called “The Difference Engine” and it embodies principles of open design in social, economic, technical, and political spheres.
5. That system of overlapping, interwoven, interpenetrated, diverse, cooperative networks is panarchy.

To engage with the original please go to Panarchy 101: 7 Crucial Lenses by Paul B. Hartzog

The post Panarchy 101: 7 Crucial Lenses appeared first on P2P Foundation.