The VIAAC Transferic Model

Fundamentals of Transferics
Transferics generalizes economics into a larger domain of study of systems which affect the movement of resources.

Introduction
The beginning of the current epoch of civilization is marked by the radical transformation of mercantilism into modern capitalism, driven by the explosive development of industrial technologies. The enclosure of land by increasingly powerful states drove peasant farmers off their land in large numbers, leaving them to become the peasant workers of the emerging social system. This new social system would shape the world into what we see today, shaping not just the economy, but also governance, geography, ethnography, ecology, and culture.

Today we may be reaching the end of this epoch, with the changes ahead of us being just as tumultuous as those during the emergence of capitalism. Technology and capitalism has augmented the scale of human influence on the Earth to a domain formerly reserved only for the forces of nature. This unprecedented state of society demands a social system that is self-aware, even sensitive. The “invisible hand” needs a brain to point in the right direction. The technological prerequisite for such a structure has come into being and is becoming ubiquitous today: Sensors, computers, and digital networks are permeating the world and turning our existing networks into those with the potential for cybernetic feedbacks.

Transferics is a framework for new resource distribution systems with more generalized axioms than those of economics. Assuming neither a scarce nor abundant state, neither two-way (trade, exchange, undirected) nor one-way (gift, donation, directed) resource transfer, it encompasses economics of all “schools”, as well as the new and obscure study of agalmics. Transferics provides a way to radically expand the range of possibilities for social systems into territory far beyond what is possible in economics alone.

Transfer systems (an “economic system” being an example class of transfer systems) can be defined by their basic properties, which are ordinarily taken for granted. For example, the basic properties of our most commonly-used transfer systems include: Exchange-based transfers, deferred completion of exchanges (exchanges use an intermediary, i.e. money), property-based controls, agent-based transference, intermediary-based constraint (i.e. money is used to constrain transference), transfer-based rules, action-based rules, and many other more minor properties.

Transfer systems can likewise include agalmic systems (gift-based transfer systems that exist in abundant states), or systems which are neither totally economic nor totally agalmic. The latter are of particular interest in the age we are approaching. A transfer system could be conditionally economic or agalmic at different times or even simultaneously. Using an interdisciplinary approach, we can construct transferics systems that are highly adaptive to changing conditions while providing a reliable, high quality of service.

Introduction
A “computational” or “natural” economy is a framework for a new economic system which attempts to reconcile the environmental, physical, and social needs of humanity with the opposing needs of our current economy. Building on the work of Herman Daly, Robert Costanza, Johann Rockström, Jacque Fresco, Robert Ayres, as well as a multitude of scientific disciplines, a natural economy is a non-monetary, steady-state, anti-hierarchical method of allocating resources and physical space. It contains as its goals: Setting an adequate standard of living that is met for all people, aggressive enforcement of fairness (as in max-min fairness), keeping exploitation and energy levels under environmental limits, and meeting rigorously measured demand using a minimum number of resources. This requires a rather large change in both economic and social structure, but it is one with a high payoff.

Computational vs Planned Economies
It’s difficult to begin discussing these ideas with anyone who isn’t already familiar, because I am usually cut off by the immediate argument that planned economies cannot possibly work, which I will address now. A computational economy is not a planned economy; it is simply far more designed than a capitalist or socialist economy. Free market proponents claim that the economic calculation problem is best solved by having no design in the economy, letting individual preference and personal wealth accretion dictate every decision. However, this does not necessarily result in good decisions or a stable economy (see black markets). The Soviet Union seems to be one of the favorite nations of free marketeers, because they are able to claim it as proof that planned economies are doomed to fail. However, the collapse of the Soviet Union was not predicted, and the reason is still not clear. What is clear is that its support of the Eastern Bloc and attempts to keep up with the capitalist arms race contributed significantly. It is not clear, however, that its economic model did so, and to claim it unequivocally did so is disingenuous, wishful thinking.

Ineffective Law in Mixed Economies
Even in a mixed economy, the rules, or “institutions” as economists call them, are simply reactionary fixes, which usually have a certain guaranteed amount of deviance (analogous to a covert channel problem). There is nearly always someone out there not knowing, ignoring, or working around the rules, and often doing so is much more highly rewarded. Wal-Mart is notorious for bending the rules in order to get their labor subsidized by welfare, for example. Not only are there people not following the rules, there must be some establishment that enforces the rules, and it won’t always be successful. There have been dozens of recent industrial disasters, including Fukushima Dai Ichi, and Deepwater Horizon, which together have contributed the two biggest environmental disasters in human history to the two biggest oceans on the planet. There were laws in place that would have prevented these tragedies, if laws were actually capable of doing so. In both cases, the operators were supposed to perform safety inspections and maintenance, but chose instead to save money by being negligent and reckless. After the fact, there has been no reaction from the law enforcement body significant enough to repair the truly staggering damage from either of them. BP even has the audacity to complain that it’s paying too much.

Incentives
It’s well-known that money as a reward does not result in effective, creative work. That people have both creative and unskilled jobs that pay is not due to the incentive of money as a reward, but rather the implicit death threat that capitalism makes on everyone: Get a job, or you’re going to have no place to live, no water to drink, no food to eat. There are so many efforts to feed, house, and clothe people, except for redesigning the economy to ensure that everyone gets a basic standard of living. Imagine if some technology were created that allowed your job to be done by unskilled laborers being paid far less than you. How long would you survive on just what you have now? This actually happens, and though the common response is that the newly unemployed person should get an education to gain a useful skill, this is just an excuse. Either the education is at the unemployed person’s expense, or the cost of education is socialized. If you’re socializing something as high cost as education, why would you argue against the socialization of basic needs? It may not be good for growth, but it certainly would be good for the economy. Unconditional basic income has worked wonders in multiple experiments, and would serve as an important first step in the transition to a more sensible economic system.

Unsustainable Marketing
Marketing has become increasingly important in driving the growth of our boundless economy. The purpose of marketing is to change people’s behavior to buy something they ordinarily wouldn’t. It’s a strange sort of doublethink that we would value marketing so highly, in an era where our biggest problems are too much trash, over-exploitation of resources, excessive pollution, and global poverty. If the economy can’t be successful without a huge amount of production that wouldn’t ordinarily occur, how can it be said to be efficient or based on voluntary transactions? How can a so-called scientist sit there and say that the basis of any economic system is dealing with scarce resources and unlimited wants, when marketing makes it clear that the reality at hand is that human wants are scarce and must be created? Why would the selling of things that people don’t need ever be prioritized over our home planet, breathable air, or allowing people to have a basic living standard? We start companies and then try to maximize the number of products or services pushed out on people. This creates a world of products that are rarely ever used, and by only one person or household, or used only once and then thrown away. Companies attempt to minimize the price of their product, but that means that each product is low cost, rather than all products being low cost. This combination of individual low cost and the need to sell as much crap as possible is symbolized truly well by the giant whirlpool of trash in the middle of the Pacific Ocean.

Structural vs Institutional
A truly efficient economy based on voluntary [trans]actions would allow people to freely use the product or service, and fulfill that demand at the lowest cost. This acknowledges both the ecological reality that we must operate under constraints, and the sociological reality that more problems happen when peoples’ status in society is unequal. Modern culture regards violence as a natural tendency in humans, but the reality is that violence is strongly predicted by several factors that are recent and avoidable: Lead poisoning, social inequality, and patriarchal society. A natural economy would attempt to rectify all of these factors structurally, as opposed to institutionally. Punishing people with violence for being violent has always been a ridiculous prospect, and is clearly not something that is ever going to rid us of crime. We should always have the goal of no more police, and that can be attained through aggressively moving to a functional social system, rather than aggressively punishing deviant individuals for whom the system has failed.

Socialized Communities
Communities should be designed around socialization: Community kitchens rather than private kitchens, community recreation centers, community manufacturing, computer labs, and so on. This encourages the open design process that has been found so effective, even in a capitalist system, at providing high levels of customization and low costs. Community kitchens could be co-located with dining halls, where people can collaboratively plan and cook meals. This model could be used so extensively that everyone could comfortably live in a small room or micro-apartment. After all, it’s not the size of a micro-apartment that’s the problem, but the lack of access to the utility that’s normally provided by private possessions. It wouldn’t be so bad to live in an apartment without a kitchen if you were a short walk away from a kitchen that’s nicer than any kitchen you’ll ever have, where you can even get a serving of a meal that someone else is cooking.

In contrast with the restaurant model, this strips away most of the economic activity involved in cooking and turns it into a primarily cultural activity. This is precisely the opposite of what has happened over the last century or so, with art and culture becoming increasingly commodified. Openness increases health standards since there’s no hiding filth in a kitchen that anyone can enter. Most importantly, it eliminates the subjugation of people into service in a restaurant. If someone wanted to run something like restaurant service, they could easily do so, but they wouldn’t be forced, or forcing someone else, into doing it for them or the restaurant to survive. The early stages of the transition, away from private meals and restaurants, towards community meals and shared dining, is already beginning. In terms of economics, the only thing that needs to be done is ensure there is enough equipment for the highest number of simultaneous users, which is always less than the total number of users. The latter would be the number of kitchens produced in an ideal private society.

Post-Scarcity Sharing
One of the major criticisms about post-scarcity economies is that there are still scarce commodities, like beachfront property or rare art. However, this criticism has an implicit assumption that any of those things should belong to one person. There is no reason that only one person should be allowed to set foot on a certain part of the Earth. If a piece of land is highly desirable, then it should be shared even more than land that isn’t. If a piece of art is highly valued to our culture, then it should be seen by everyone. Our current society takes exactly the opposite position: The best things are to belong exclusively to those who are best able to get the most for themselves. This sort of cutthroat, violent outlook on society is at the root of many social and environmental problems. Letting go of the idea that things belong to people is the first step to a better world.

Open Design
The separation of production and consumption results in inferior products with more guesswork involved in their creation. On the other hand, open design processes result in products that fulfill all the needs of the users, since they are (at least partially) designed by the users. They tend to result in quicker adoption of standards, which perhaps counter-intuitively results in immensely higher diversity of products. Computer hardware, for example, is highly standardized which results in infinitely many different configurations. Smart phones are highly proprietary, which means there is no inter-compatibility for phone hardware and so only the configurations that large companies create ever exist. It results in extreme fragmentation discourages users from fixing problems with the hardware, leaving them to just grumble about it and replace it with something different, which merely has different problems. This is an absurd runaround that will probably not end any time soon, while squandering valuable resources in the process.

General Applicability
One of the differences between my philosophy and that of The Venus Project, The Zeitgeist Movement, or Singularists about solving similar problems, is that they all see a primarily technological problem, while my goal is to allow a post-scarcity economy with no specific level of technology. Scarcity doesn’t have to mean not everyone has a Tesla Roadster or Google Glass, it should primarily mean that everyone gets a fair shot at living. After everyone is able to eat a wholesome meal, sleep under a roof, and not walk around naked, then you can start worrying about how we’ll possibly have enough for everyone to live forever in the ever-important immortal cyborg bodies. This is negative utilitarianism, "the least bad for the smallest number." The strong focus on high technology and technological solutions by all three groups, to me, shows a tacit aloofness for the actual well being of people, as well as a sort of overly optimistic techno-fetishism. However, technology does not fix the environment or reduce growth, because these are not technological problems. Believing that electric cars and solar panels will fix our environmental problems may sound reasonable, except that it’s not gas-powered cars or fossil energy that’s the problem. It’s too many gas-powered cars and too much fossil energy for the constraints that we should be operating under. This and social problems such as increasing wealth inequality are caused by too much focus on always getting more and newer things for ourselves, and will not be solved simply by having the capability to produce more things. We are conditioned into always feeling like what we have is not enough, which means no amount will solve that problem. What will solve that problem is realizing that some of us have enough, some of us have far more than enough, but most do not, and we should take care of them the way we take care of ourselves.

Production Planning
How do we make effective decisions quickly enough to keep up with a large number of simultaneous problems in the same system? Decisions are not always single actions, they are ongoing movements whose path can be altered.

One way to do this would be to make a best guess immediately, and then wait for better information that may reveal it as the optimal/suboptimal decision. As the probability of the decision being in error increases, the likelihood that the decision needs to be changed also increases.

This can be thought of as repeated estimation, polling, and hypothesis testing. I have previously visited the problem of how many computers to produce for a group of people. If this is your first time reading, the basic design assumption is that products are used more like services, where a set of computers is shared by a number of people in a community. My hypothetical example resulted in the derivation of Little’s Law, which relates the necessary size of a queue to the average time spent in the queue and the rate that members of the queue arrive. However, when we start up a new community, or create a new service, we have no idea what values either of these parameters will take, so we have to make a guess at first.

It’s pretty safe to assume that for the vast majority of people, 8 hours a day will be the highest amount of time spent using any single service. If we are trying to decide how many computers to build for, say, 100 people, our best first guess can be made using this 8h/d assumption. There are 24 hours in a day, so we’ll say that each person is likely to use a computer for at most 1/3 of the day. One-third of 100 is 33.33…, which we will round up to 34. This is the initial guess for number of computers to produce. For simplicity, let’s say it takes an infinitesimal amount of time to build them, and we start with all 34.

Now, after a short period, we have enough data to be considered a fairly large sample, and it appears that the sample mean use time is actually 5.85h/d. This is significantly less than our original guess. Now comes the hypothesis testing:

$$H_0$$, the null hypothesis, is our original guess, which says that the mean is 8h/d

$$H_a$$, the alternative hypothesis, is our observation, which is 5.85h/d.

We should perform a z-test on this, which is the equivalent of asking: “If the actual mean were 8 hours a day, what would be the probability that we would observe the mean to be 5.85 hours a day?”

I lost some of the details of this calculation in the intoxicated haze the original draft was written in, but the outcome of the Z-test was that the probability in this case was 2%; the likelihood of observing 5.85h/d average when the true average is 8h/d is only 2%. In other words, it is 98% certain that we have overestimated the number of computers we needed to produce.

Now, let’s revise our earlier calculation: 24/5.85 = 4.1; The reciprocal of 4.1 is 0.24375, and multiplying this by the population of 100 gets us 24.375, which we will round up to 25. This is a 26% reduction in the number of computers needed.

Now we have an additional problem: What do we do with these extra computers that we have on hand? There are three obvious possibilities:


 * 1) Repurpose: Transport the computers somewhere they are needed.This is workable provided the following conditions hold true:
 * 2) The computers are needed elsewhere
 * 3) The energy requirements for transportation of these computers is the lowest among the three options
 * 4) Recycle: Deconstruct the computers into their constituent materials for something else. Conditions:
 * 5) The energy use is the lowest among the three options.
 * 6) Store and wait for a deficit: Store the computers for a set period and signal other communities of a surplus state. Wait for a signal from another community of a deficit state.  Conditions:
 * 7) The variance in demand for computers is high (or, this is early in the production stages and the likelihood of incorrect guesses is high)
 * 8) The energy use is lowest among the three options.

Assuming this community already had the space for all these computers, (3) will probably be the best option, at least for a short period of time.

In a different case, say we didn’t produce enough computers. Our choices here are essentially converse to the previous three: Request more, build more, and wait for a surplus.

Notice that in all three possible actions, we want to pick the one with the lowest energy use. In the near future, i.e. in the next century or so, it is a near-certainty that we will be operating under energy constraints. Renewable energy sources work, but it is not yet clear that a reliable, high-energy infrastructure like the one we have now will be immediately possible. It would be very wise to operate under the assumption that we will have to constrain our energy use, so it becomes very important to account for the energy use of our decisions.

As it turns out, money is (usually) an indirect account of energy use. However, the way money is used to make decisions encourages increasing energy scales and decisions based on personal gain rather than community gain. Money is traded for the goal of profit, resulting in the maximum number of products to fulfill a given demand. By using energy-minimizing actions to fulfill demand, we come up with a minimal number of products, which means more user demands can be fulfilled with a given material and energy supply. It requires a restructuring of the way we use things and think about civilization, but this process is already well under its way. Paper & Pencil Production Simulation

The VIAAC Economic Model for Labor Specializations
The networking of intentional communities, once established, would allow a specialization scheme for greater efficiency and robustness of the network. Due to the four-color theorem we know that 4 or more specializations are required. I propose two sets of disjoint specializations: $$\left\{ h,v* \right\}$$ and $$\left\{ M,T,C,S\right\}$$. These sets are not disjoint, so any member of the first set can also be a member of the second set.

Connection Type
$$\left\{ h,v*\right\}$$, also known as hubs (h) and spokes (v*) describes the node by its connection to other nodes and its factor of overproduction. The factor of overproduction, $$\Omega$$, is a factor of the demand multiplicity, $$\delta$$. We also need to define a time parameter, $$T$$, which is the time taken to transport a bundle of resources from the adjacent node to this node.

$$D = \int_t^{t+T}\delta$$ is the demand over time T.

$$\Omega_{v*}$$ is defined more simply, so I will define that first. To ensure logical consistency, I will define this in English, French, and mathematical notation. This is a technique to ensure the math is logically consistent, not just meaningless symbol manipulation:

Theorem 1.1:

$$\Omega_{v*} = D + p(f)*D$$

"The demand over the time taken to transport the materials demanded, plus that demand times the probabilty of failure."

"La demande sur le temps pour porter les matériaux demandées, plus cette demande par la probabilite d’échec."

$$\Omega_h$$ is a little more complicated, because the hub nodes are responsible for replenishing the spokes in the case that some shortage happens. Therefore, hub nodes require more storage and management. The overproduction factor may be defined:

Theorem 1.2:

$$\Omega_h = (Dv*) + ND + \sum(p(f)D)$$

"the quantity of v* times the degree, plus the sum of the probabilities of failure times the demand, plus the time to replace the quantity demanded itself via the set {v*}"

"La quantité de v* par le degré, plus la somme de probabilités d’échecs par la demande, plus le temps pour raplacer la demande meme-soi via {v*}"

These two equations are part of what I call the demand replacement theorem. Keep in mind, both are derived from a thought experiment and have no empirical support, but I think they’re general enough to suffice in terms of both shortage protection and efficiency, compared to any market mechanism.

Production Type
Now, for the other typifying set, $$\left\{ M,T,C,S\right\}$$. This one is, I think, a little more compelling.

Each of these members is a type of specialization for a VIAAC (henceforth referred to as a “vertex”) in the network.

M-Class
M is a machine specialization. An M-class vertex would, at the least, have a multimachine, CNC mill, or similar tool for making arbitrary, full-strength parts of a given volume. The machine shop is the main production building in an M-class vert. All unspecialized vertices should probably start as an M-class, because an M-class could hypothetically provide an easy way to reach any other specialization. Early on, M-class vertices should produce construction equipment (earth rams, CEB presses, or contour crafting machines) in order to help the establishment of new vertices, since that allows for greater connectivity, robustness, efficiency, and specialization.

If there is an unspecialized vertex adjacent to an M-class (adjacent as in within a practical distance to transport resources, or especially large pieces of equipment), the M-class vertex can specialize to one of the other classes via a “molting” operation, where the production capabilities of the M-class vertex are used to create specialist equipment of another class, while simultaneously transferring its existing equipment to the unspecialized vertex.

All nodes should take care of their own food and energy needs, plus a surplus for its neighbors, following the demand replacement theorem. M-class vertices can use all forms of food production, and should be able to use gasifiers early on, with windmills coming later, since they require semiconductor technology (though turbines are easy to build from scrap). For energy storage, flywheels and pumped storage make the most sense for an M-class vertex.

M-class vertices should be very easy to build, since there is a great abundance of open-source machine tool designs, most of which can be built at extremely low cost (multimachine, various open-source CNC tools, RepRap, MetalicaRap, and subsequent tools become easier to build, such as brakes). The parts they can produce are fairly high cost on the market, especially when they are uncommon designs and not in truly huge quantities, but there is a high need for the products of machine tools. Thus, M-class vertices are unequivocally the best choice for a first specialization.

C-Class
C-class vertices are the second specialization. C stands for “chemical”, and C-class vertices produce chemicals, polymers, alloys, or other such products. The main production facilities for C-class vertices are refineries and chemical plants. C-class vertices are required to create S-class vertices unassisted (i.e. without monetary participation).

C-class verts are a good second choice, since they greatly advance the available technologies to the network, and provide many essential products that can carry a high cost or dependency on the market. They have a medium bootstrap cost, as there is some open-source information about C-class processes and many of them can use recycled materials (e.g. Filabot). C-class vertices could produce molten salt batteries for energy storage.

T-Class
T-class vertices produce textiles, which are also required for S-class, and fulfill a great need for the VIAAC network. Though the need and usefulness of textiles is high, their relative market cost is low, hence their being ordered after C-class. T-class vertices should start by growing textile crops in increasing quantity; with adjacency to an M-class vertex, it can obtain jacquard looms to enable high production of high-quality textiles.

S-Class
S-class vertices are essentially what brings the network to the economic level of a developed country. They are the ICs that produce semiconductor products, such as circuits, motors, batteries, and solar panels. They will make increased automation possible, and the first S-class will probably mark the point where the rate of development of the network achieves a slope greater than one (in other words, development shifts from linear progress to exponential progress). However, semiconductors currently have lots of dependencies and have a huge capital (production capability) cost. Photolithography is a very high-energy process, and silicon for semiconductors requires extremely high purity (nine nines, 99.9999999%), as well as the ability to produce PCB laminates (which requires textiles, copper, and the aforementioned silicon).

Other specializations could certainly be imagined, but in terms of creating a modern economy, I think these are the minimal four. T-class will probably be the most replaceable with other types of specializations (e.g. cultural centres).

Multi-cellular life evolved to break the single-celled arms race. Increasing connectivity, which is the most obvious of technological trends today, is the biggest enemy of competition. The network, upon reaching a certain size, should be able to respond just the way an organism does to any existential threat, whether natural or societal. A large enough network could conceivably even handle a full-scale attack by outsiders, with a quick, coordinated response, sacrificing a small number of resources to protect the community. A VIAAC could be considered not only a new type of community, but eventually a new type of organism.