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The Millennium Project explores both content and methods in futures research. Since the project's major work on methodology: Frontiers in Futures Studies: A Handbook of Tools and Methods (http://nko.org/millennium/methods.html), produced for the United Nations Development Project is expected to be published shortly, this report does not repeat that work. As described in Section 1, the primary methodologies used by the project are look-out studies, scenarios, and scanning - each receiving one section in this report. The current section addresses methodological issues that are not included in Frontiers in Futures Studies or elsewhere in this book.
5.1 Apparently Incredible Developments
A problem of considerable significance arose during the study. In Rounds 1 and 2, the panelists were asked to contribute developments, derived from their own experience, that suggested future issues or opportunities of global significance. A few panelists submitted developments that seemed incredible or suggested items that had yet to occur or forecasts that did not fall under the rigors of what is understood to be a development. What should be done with these ideas?
The dilemma: if these developments were included in the subsequent research, the study as a whole might loose credibility. Yet, summarily dropping these suggestions would impose the analysts' ideas about the proper content of the study.
The panel approach was designed precisely to encourage respondents to contribute observations about discontinuous developments or changes in progress that seem surprising. Participants were promised anonymity to help remove any impediment to the suggestion of off-beat ideas. After all, the most important technologies and systems of today were at one time perceived as silly, irrelevant, hopelessly idealistic or impossible. Physicists today are grappling with the nature of gravity and time, including the possibility of its reversal under special circumstances. Bio-scientists are exploring cloning and the mechanisms that control aging. Neuro-psychiatrists are probing for a deeper understanding of the brain and the nature of consciousness.
Millennium Project participants were asked to contribute ideas about those developments that they thought would prove to be important.
Despite what the staff thought was an open mind set, some suggestions seemed outside the bounds of acceptability. We made the decision to preserve these suggestions separately in this and future reports for several reasons. First, the suggestions might, in fact, turn out to be correct. Secondly, they represent a record of what was considered incredible by the study team and such a record could prove useful in future studies of the sociology of policy research.
For the record, here are the developments in the respondents' own words, diverted out of Rounds 3 and 4, but included below:
Item 26, from Round 1:
By far the most important thing on the technological horizon is so-called Ôcold fusion' (which is probably neither.) There can now be no further doubt that we are tapping new energy sources by several different routes.
Item 43, from Round 1:
Zero-point energy/cold fusion breakthrough. Beginning of the end of fossil fuels. Energy revolution which effects every aspect of life. Potentially eliminates most of global air pollution problems. Allows large undeveloped countries (e.g. China) to move ahead economically without adding to the pollution. Changes the geopolitics of Middle East. Philosophical shift: potentially decentralized way of producing power; no longer dependent upon central provider.
Item 64, from Round 1:
Memes becoming a new method for planning and directing the evolution of technology and society. Memes, recently postulated as the smallest unit of social evolution, will become a powerful new method for taking charge of the evolution of technology and society, rather than the present condition of adaptation to constant and seemingly random change in the environment. This development will introduce many new metaphors and tools for organization, management and development. These new tools and metaphors incorporate strategies and dynamics drawn from self organization, chaos theory, Chinese medicine, Taoist philosophy, cellular automata, etc. The potential consequences (include): A new renaissance of self-organizing creativity and invention, with self-awareness and solidarity pulling cultures together to steward the planet into a new era. An evolutionary convergence. (But also), a new era of market based designer culture, and cults. A diversity of idiosyncratic and isolated pockets of change, an evolutionary divergence.... Memes could be developed from an existing market/political/social paradigm or memes could be developed through a visioning process such as scenario building or shamanic journeying...Scenarios as currently used offer insight and options to dealing with the future, only on rare occasions are they used in memetic fashion as a proactive tool to consciously engineer the future. Bibliography (includes): Eric Jantsch, Design for Evolution, Magoroh Maruyama, Morphogenetic Joint Ventures, Dawkins, on Memes...
Item 44, from Round 1:
Return of the Messiah. Sri Satha Sai Baba proves himself to be reincarnation of the Christ. Indian holy man who regularly does "miracles" including raising people from dead, does, in fact, as he has promised, convince world that he is this age's avatar. Potential Consequences: More people than the present millions flock to his small town in India. A global spiritual revolution begins.
Item 46, from Round 1.
Development of faster than light travel.: Experimental results confirm mathematical theory that space-time can be "engineered" to allow movement of object from one place to another instantaneously. Potential Consequences: Radical shift in military capability (if by country other than USA), Rethinking much of physics and ultimately technology. Confirmation of the possibility of extraterrestrial travel. Goals: Evolve into new era such that it maintains the integrity of social systems and doesn't shake fundamentals too abruptly.
Item 45, from round 1:
Extraterrestrial Life Confirmed. Previously secret Post W.W.II projects come to light that confirm that humans have been interacting with life from elsewhere for some time. Japanese unveil new museum with parts from crashed vehicle. Potential Consequences: Could be negative, could be positive, could be both. In any case, massive global shifts in attitudes. Depends upon whether there is any "official" contact that comes out into the open and what the "message" is that is communicated from the aliens. (We should) become open-minded and knowledgeable enough to be able to make a contribution to the huge debate which will follow.
Round 2:
Remote Viewing (ability to see at a distance in time and space) shown to be a valid human capacity, and increasingly used as a research tool by futurists.
Round 2 :
Popular Claims Of Alien Abduction And Experimentation On Humans Turn Out To Be Valid. It appears that a hybrid human-alien race is secretly being developed by aliens.
Round 2:
Quasi-Psychedelic "Designer" Drugs, (such as the "smart" drug, 2-cd), are shown to make it possible for humans to greatly expand their capacity for visualization and understanding of complex systems, and to intuit solutions to problems.
Round 2:
Genetic Research On Basis Of Memory leads to genetically engineered offspring that can remember past lives.
Round 2:
Genetic Research And Experimentation Focused On Cognition, Especially Memory, leads to discovery of simple but subtle (nanometer level) structure involved in "past life memory." Engineering techniques proved feasible to bring this to all who can afford it. Either human civilization becomes a powder keg of newly remembered ethnic conflicts, or it leads to rapid evolution in consciousness to higher levels of functioning.
Round 3; Issue 2:
Fresh water is becoming scarce in localized areas of the world. Comment from respondent: "....One of my friends has developed a system, using musical sound patterns, that would enhance crop yield under dry conditions, but sadly he has been unable to attract funding."
5.2 Improved Decision-Making: A New Frontier
Background
While quantitative methods have been devised to assess the relative costs and benefits of alternative policies (e.g. cost/ benefit, utility theory, risk analysis, net-energy, etc.) all of these methods depend on assumptions about the future and the future will always be imperfectly and incompletely known. Therefore decision making will always involve risk and uncertainty. Any means that accepts the limitations of futures research while reducing risk and uncertainty will certainly improve the ability to obtain desired policy outcomes and therefore deserves the most serious attention.
It may never be possible to know the extent of uncertainty when dealing with issues about the future. There are at least six sources of uncertainty that limit the ability to understand the range of possible decisions and their potential outcomes:
The availability of large and fast personal computers gave numerical methods of futures research and policy analysis great impetus. Models were often used to test proposed decisions. The usual approach was to build a model that duplicated history and run it into the future to make a forecast. In effect, the model was assumed to project the situation if no decision at all were made. In subsequent runs, a decision was simulated. The two runs - one with the decision and one without - to determine whether or not the decision was beneficial.
Some models forecast well some of the time and others never forecast well; but it is very difficult to tell, a priori, which models are going to work and which are not. Why? Macroeconomic models used by governments to "manage" economies have a low predictability that are based on assumptions of "general equlibrium". When the model closely resembles the system because both are linear or change is slow, for example, in population forecasting or in estimating the trajectory of a spacecraft on a course from earth to the moon, models are accurate. But when change is rapid, when inertia is low, when surprises are possible, when the time horizon is distant, the models will fail regardless of the number of equations, the mass of data, or the size and speed of the computer. Accuracy may not improve with increasing computing power, or collecting more precise data.
In a linear world, forecasting would be precise if only there were enough sufficiently accurate data, and a computer big enough to organize and manipulate it. In such a case, historical relationships among the factors driving change could be deduced. A central theory could be formed, inferred from the data if nothing else, creating the body of information and models of the gear-cogs of change, from which forecasts could be produced.
When systems are non-linear, however, the situation changes, and models may produce misleading forecasts. The level of complexity is a system attribute: at the simple end of the scale are linear Newtonian systems; that is, systems in which output is proportional to input. At the other end of the complexity scale is randomness: the snow on the face of a TV tube when the transmitting station is off the air. Random systems are, like an honest roulette wheel, unpredictable.
Most systems lie between the linear and the random extremes. Before the advent of large computers, a non-linear model of even a simple system like a teeter totter took a lot of time to solve, so it was much more efficient to make simplifying assumptions. When computing power grew, it became much more feasible to include the more complex non-linear aspects of the system under study. Where the models produce orderly results when linear assumptions were made, when non-linear factors are included, these models can produce unpredictable and random appearing forecasts: chaotic behavior.
With cheap computing power, a new form of experimentation is possible. Rather than seeking a general law explaining the behavior of a physical or social system, computer simulations can be run millions of times. What emerges is a map of the motion of the system that indicates its complexities, and the conditions under which its motion becomes chaotic and unpredictable. This numerical approach is a sharp break from the standard way of science and has now been applied to systems as diverse as the formation of thunderstorms, the turbulent flow of fluids, the mixing of chemicals, the evolution of species, the stability of eco-systems, the behavior of markets, the flocking of birds, neural networks, earthquakes, plant development, and conflict. Computers combined with random surveys can also locate consensus. This ability to simulate the behavior of real systems by performing multiple runs includes both chaotic and non chaotic behavior - it is a new capability made possible by cheap and available computing power.
Systems in chaos are very sensitive to initial conditions; even minuscule changes can have dramatic effects later, the so-called "butterfly effect". For a system in chaos, the present can't be reconstructed from history and history can't be reconstructed from the present. The whole may be different than the sum of all the parts: little panels on an aircraft wing may vibrate separately, but understanding their individual motion does not necessarily mean that they add up to the motion of the wing as a whole. Chaotic systems may repeat their form at all levels of detail; look at a chaotic system under a microscope or through a telescope, up close or at a distance and it seems to have the same roughness. For example, a graph of stock prices tends to look pretty much the same whether the graph shows yearly, monthly, daily, hourly and probably, minute by minute prices. (But the stock market is a complex system and its "attractors" are of high dimension and its simulation remains a dream; claims to have found a chaos model that duplicates market performance are probably exaggerated.)
Intuition
If models are wrong, how then can good decisions be made? Some people make good decisions intuitively;7 they somehow feel, they somehow know, what is right to do. From an evolutionary point of view, one could certainly argue that ancient ancestors who made good decisions had a better chance at survival than those that made bad decisions. Decision time in the Paleolithic Era: "Can I make it across the field to the cave before that saber tooth tiger catches me?" Perhaps there still exists, somewhere in our makeup, the legacy of the fastest runners and best decision makers.
Good physicians do it. While most all diagnosticians can recognize obvious symptoms, the experts among them also take into account clues that would escape the less skilled or less observant physician. Their rules of thumb might include slight slowness to answer a question, unusual odors, lack of luster in the eye, a sense of nervousness, personal knowledge of the patient. Training helps, but there may be an innate talent for making good decisions.
What do these intuitive decision makers have? Experience? Certainly. This seems to be a prerequisite. But experience alone doesn't account for their successes. Maybe it's the ability to learn from experience. Maybe it's the ability to pick out small indicators from a flood of information and to subconcsiously build images of the future that give courage to proceed. Genius for making a good decision - particularly when there is little data, when the signals are weak and the risks are high - is similar to the gift of musical proficiency. Skilled politicians can over-ride conclusions drawn from faulty models by using good "judgement."
But luck and chance undoubtedly play some role in what we see as intuition.
Neuroscience
That genius for decision making, if it exists at all, resides in the brain. Research in neuroscience is advancing rapidly, but fundamental questions have not been answered such as: how the brain functions, how memory is stored and retrieved, how images are created, how imagination functions, the meaning of self-consciousness, how decisions are made from these raw materials. The complexity of the brain is evident in the diversity of the types of cells it contains, the intricacy of their interconnections, and in the number of neurotransmitters used to modulate the flow of information from one cell to another. The brain makes us what we are, but we know less about it, the center of our intellect, than we do about the center of the earth.
Scientists are learning about the brain from the bottom up, so to speak, component by component: from the range of neurotransmitters - the bio-chemical that modulate the current flow of neural synapses, to the regions of the brain that "light up" in PET scans of people and laboratory animals performing certain tasks, to the neural pathways created and abandoned in the course of development. Some people argue that the component approach may not yield a total theory of brain. By analogy, would an analysis of the components of a computer - resistors, transistors, microprocessors - lead to an understanding of how the computer works?
How does the brain make decisions? Lesions at the base of the prefrontal lobe apparently interfere with the decision process. After an operation that removes tissue in this area, for example, IQ can remain high, but simple decision making can become greatly impaired. For example, Calvin and Ojemann, in Conversations With Neil's Brain: The Neural Nature of Thought and Language, write of a patient:
...He was often unable to make simple, rapid decisions about what toothpaste to buy or what to wear. He would instead become stuck making endless comparisons and contrasts, often making no decision at all or a purely random one. Relatively simple decisions could take hours. Going out for dinner required that he consider the seating plan, menu, atmosphere, and management of each possible restaurant. He'd even drive by them to see how busy they were, yet continue to be indecisive, unable to come to a decision about where to eat dinner.
If a decision science emerges in the future, a significant portion of the new field will come from neuroscience.
Utility
This is the domain of economic rationality, of cost/benefit analysis and operations research. The expected value of a win is the amount that can be won times the probability of winning, so if one were to bet one dollar on red, and the odds of it hitting are 50/50, then the expected value of a win (and similarly, a loss) is 50 cents. Anything that changes the situation so that the expected value of the win is greater than the expected value of the loss makes the bet a better one.
When the stakes get high, when the uncertainty grows, this rational picture can evaporate. Let's say the local woman's club has been given a green Jaguar convertible worth $75,000 and is auctioning it to raise money for a charity. The tickets cost $1.00 each and the club swears they will sell no more than 65,000 tickets. How many tickets should a person purchase? The odds of winning are 1 in 65,000. This means that each $1.00 ticket has an expected value of $1.15, clearly a good deal. So does a person buy all 65,000 tickets? This is a significant expenditure for most people. Perhaps, only 32,500 tickets should be purchased to change the odds of winning to 50/50.
The bet, ultimately, depends on a number of factors. Is $65,000 significant? If the car were to be won could the tax be paid? Even though expected value indicates that the bet is a good one and a richer person might do it, most people might buy just a few tickets instead and trust luck.
Irrationality
Suppose that a die has an equal number of red and black faces. A sequence of rolls gives 6 reds in a row. The rational statistician will tell you that on the next roll, there is an equal chance that red or black will turn up. Most people, however when presented with 6 reds in a row will guess the next will be black "to even things out." For reasons that have not yet been discovered, the mind thinks in patterns that do not meet the statisticians view of rationality.
Based on the work of Kahnamann and Tversky, and other psychologists who deal with decision distortions here are some other propensities of human thinking:
The Moral Side
Decisions have a moral quality as well that has engaged philosophers over the centuries. Their advice is often in conflict:
Operations research has some basic rules of decisions: minimize regret - that is if you missed the chance to do it how badly would you regret it - win or loose - later on? Another is mini-max, which calls for minimizing the chances for a maximum loss. And common sense says make the payoff commensurate with the downside risk. But there are other techniques as well. Modularity allows the piecemeal assembly of approaches that preserve options as the future unfolds. Hedging is a strategy that protects against uncertainty: it doesn't matter if the market goes up or down, properly hedged investors can win - or at least not lose much. However, individual hedging in financial markets may increase risks of systemic breakdowns. Scenarios are a form of hedging - define a set of future worlds and find strategies that work in all of them. These strategies are the good bets of today. Portfolios are a means of guarding against uncertainty: put packages together that will have some winners and losers, some with high risk and commensurate high return and some with lower return but greater certainty.
Important note: decision makers can be trained to recognize and deal with uncertainty, to recognize their own propensity toward overconfidence, for example, or to make explicit their tolerance for risk.
Cognitive Science
Cognitive science exists at the confluence of computers and neural networks; brain physiology, neurology, and mental functioning; economics - particularly the economics of optimization; cognition, intelligence, memory and learning; and, importantly, self-consciousness. In short, it is a search for a better understanding of the relationship of brain and mind and the capability of computers and neural networks to explain either mind or brain, or reproduce their functioning. It asks how people learn, recall, and make decisions, and to some degree, how people ought to make decisions. Some of the people in this field have raised images of a social collective intelligence, global mind, drawing an analogy between minds interacting in a social context and neurons interacting in a single mind. This raises the possibility of a social intelligence, which is certainly worth searching for.
Decisionists
From all of these perspectives, there is an attack mounting on the problem of making good decisions. Neurological scientists are beginning to understand how memory is stored and retrieved and where in the brain decisions are made. Social scientists are asking once again about the moral basis for decision making and are designing new tools to evaluate values. Psychologists are experimenting with questions that test the mind's ability to grasp rationality, and statisticians and economists are extending concepts of utility and what ought to comprise a good decision. Gerentologists and lawyers wonder how to tell whether a person is competent to make a complex decision, they wonder how to measure the complexity of a decision. With the Internet, access to expertise and information is global and collaboration is no longer totally bounded by geography. Cognitive scientists grapple with concepts of optimality in artificial networks and natural systems, and the limits to the brain/network analogy.
These and other activities suggest that a new enterprise is underway that will lead to more conscious, systematic, and insightful consideration of the promise and pitfalls of decisions. If this field fully emerges, it may give decision makers of the next century the courage to make great decisions.
A new sort of model has emerged in recent years that provides a new means for exploring social interactions in changing environments and testing the consequences of anticipated policies. The field is generally known as complex adaptive systems, or adaptive agent modeling. It includes non-linear interactions and feedback. This work stems directly from the "artificial life" of "cellular automata" computer experiments of the late 1960's, and owes its heritage to John von Neumann's early thinking about self-replicating automata.9 However, it may well be that the concept of complex adaptive systems has rapidly become over used.
In essence, this modeling technique, from its first beginnings in the game Life to its current implementation in Brookings Institute's Sugarscape, involves providing simple instructions to software "agents" or "avatars" the smallest units of individual behavior in the program. The computer programs run, one time interval at a time, during which the agents interact and behave according to their instructions. The aggregate results are often unexpectedly complex, and are displayed in the form of time series, or as changing parameters placed on the computer monitor screen where the agents themselves are located.
A few examples of this approach will make the concept clear.
In the late 60's, John Conway, at Cambridge, invented the game Life. The agents in his model were given rules that dictated birth, survival, and death.
Life occurs on a virtual checkerboard. The squares are called cells. They are in one of two states: alive or dead. Each cell has eight possible neighbors: the cells which touch its sides or its corners.
If a cell on the checkerboard is alive, it will survive in the next time step (or generation) if there are either two or three neighbors also alive. It will die of overcrowding if there are more than three live neighbors, and it will die of exposure if there are fewer than two.
If a cell on the checkerboard is dead, it will remain dead in the next generation unless exactly three of its eight neighbors are alive. In that case, the cell will be "born" in the next generation.
With these rules, the cellular performers on the screen reproduced and spread in patterns on unexpected complexity. The game variations were myriad and involved the addition of gender, genetic characteristics, competition, mutations, and other features that added complexity and suggested that new medium for social and perhaps genetic experimentation was being created.
In 1987 Craig Reynolds, then an animator at Symbolics Corporation, developed a program that simulated the flocking behavior of birds. The instructions were:
In 1988, Gordon (one of the editors of this report) and Greenspan (a psychiatrist) published an adaptation of Life that introduced a random factor.11 This example simulated a politician going from door to door in a neighborhood and attempting to enlist each household in his cause. The rules they placed on an acceptance were quite simple: before any household can accept the politician's proposition, there must be at least one other household in the neighborhood that has already accepted the proposition and given this condition, a chance probability is imposed. The search began in the center of the screen and spiraled outward. The patterns they achieved ranged from very sparse at 30% probability to dense at 60% probability. The striking aspect of this experiment was that patterns of apparent organization appeared- that is, coherent regions of the neighborhood were either for him or against him.
Finally, the Sugarscape model represents the current state of the art in complex adaptive system modeling. Science News recently described the model as follows:
(Sugarscape) is a two-dimensional landscape, represented as a square grid, containing two regions rich in a renewable resource arbitrarily called sugar. Ever, agent is born into this world with a metabolism demanding sugar, and each has a number of other attributes, such as visual range for food detection, that vary across the population.
They move from square to square according to a simple rule: Look around as far as your vision permits, find the unoccupied spot with the most sugar, go there, and eat the sugar. As it is consumed, the sugar grows back at a predetermined rate. An agent's range is set by how far it can see. Every time an agent moves, it burns an amount of sugar determined by its given metabolic rate. Agents die when they fail to gather enough sugar to fuel their activities.
The agents initially move toward the sugar. There some with long vision and low metabolisms accumulate wealth. Others become poor, just barely subsiding. When gender an sex is added to the instruction set, genetic properties can be inherited by progeny, and wealth accumulated by the parents passed on. The beginnings of culture ensue. Epstien and Axtell have also experimented with war, tribalism, trade, multiple assets (sugar and spice), and bargaining.
Complex adaptive models have also been constructed at the Santa Fe Institute and elsewhere on topics that include insect swarming, financial markets, and transportation systems.
This kind of modeling is not expected to produce exact reproductions of the past or to yield accurate forecasts of the systems under study. Rather, they are designed as a new domain for social and economic experimentation. The rules programmed for individuals result in aggregate behavior, "a laboratory for social science," as Epstien says, that offers an easy way to perform cross disciplinary studies.
The more conventional modes of modeling attempt to replicate systems as a whole, top down. The agent approach attempts to replicate systems by replicating behavior at the lowest level of disaggregation and build system behavior from the bottom up. The search for guiding principles and natural laws need not be so compelling. However, while the results can be unexpected and apparently mirror life, there is no assurance that the researchers have got the correct rules for individual behavior. So the models while useful and suggestive are still only models.
There is another new computer domain for social experimentation: metaworlds. Players in this domain interact with each other in a space created by a game designer. Such multi player games exist on line on Internet; others are provided as a service by Compuserve, America On Line, and others. The models are generally known as MUD's (Multi User Dungeons, after the Dungeons and Dragons game) and MOO's (MUD's- Object Oriented). They are in effect new communities, new social spaces, where the players take on new identities and interact according to the rules of the game designers, or according to ad hoc rules that emerge as the games are played and the cyberspace communities evolve. In general, MUD's provide a game environment in which the players interact- find the bad guy before he gets you, and MOO's are more attuned to real social environments where the rules are socially determined. MUD's use artistic and sometimes scary landscapes; MOO's provide a virtual space and geography. Since the rules are loose, many MOO's use Wizards, players who through their experience are elevated to a position of power, to set rules and adjudicate. Social behavior, norms, and morals evolve in these worlds.
Take the MOO metaworld Kymer, for example. One user described his visits:
... like everyone else, I had to figure out how the world works. I had to learn the conditions of existence, and what they meant..... And then there are the headhunters. Buying a head is the most prominent way to assert your identity in Kymer. Your choice of a head determines how other people see you. ... It's no coincidence that heads are among the most expensive artifacts in Kymer's virtual economy of tokens. As objects of great value, heads also attract criminals.
The headhunters hang out by the docks, waiting for the boat that brings new users to Kymer. When a newcomer disembarks, the headhunter welcomes him with a friendly greeting. He gives the newbie a few hints on places to go and things to do. Then he moves in.
"Here's something fun," he might say. "Did you know you can take off your head? Try it!" The newcomer removes his head. "So I can! That's pretty neat"'
"Here," says the headhunter, "let me show you something else. Give me your head."
You would think that most people would have the sense not to give something valuable, like their head, to a complete stranger. Judging from the number of headless avatars I saw wandering forlornly around the streets of Kymer, a fair amount of people do not. To combat the plague of headhunters, public-spirited citizens have started frequenting the docks just to warn newcomers not to give their heads to strangers.
Some social scientists are beginning to use these strange new worlds as experimental space, testing for the intrinsic laws and evolution of society. It is not difficult to imagine, for example, constructing an artificial world in which citizens of several classes emerge, and deriving from the behavior some insights about culture and inter-cultural norms. Alan Gaitenby recently studied the nature of laws and legal behavior that emerged in some MOO's. He wrote that:
...a partial list of prohibitions of conduct include: filling others' space or screens with unwanted text; moving or manipulating others against their will; spying or the creation of devices to monitor others' actions; the creation of devices that mimic humans or otherwise trick users; and harassment. .... Nominally popular democracy is the manner of virtual codification.... In all MOO space however, regardless of how law is made or where it comes from, enforcement is a Wizardry activity, there are no police drawn from the "citizenry," no judicial system, no jury, and most definitely no appeal.
This is a new form of social experimentation that may in the future
yield protocols for defining and testing effective policies. With the clear
possibility for improved visualization, speech interaction, three dimensional
viewing, and other improvements, the virtual MOO space may become a real
social sphere to some that will provide an even more intriguing way to
explore human social behavior.
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