In order to understand the importance of this document it is necessary to search the Rand Corporation and the Tavistock Institute . . . The Rand Corporation is behind mind control techniques to crete a global society that is manageable and to drastically reduce the global population. .
Below are EXCERPTS from the above document -
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Among the first group processes, the “Delphi” technique was developed by RAND researchers in the 1950s
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9The predictions included the following: a world population of 5.1 billion, large-scale ocean farming and synthetic protein production, regional weather control and con- trolled thermonuclear power as a source of new energy, development of a universal language and “high IQ” robotic machines, mining on the Moon and a landing on Mars, weather manipulation for military purposes, and effective anti-ICBM defenses (“air-launched missiles and directed-energy beams”). The panel was closer to the mark in forecasting “general immunization against bacterial and viral diseases” though still a bit premature in forecasting the correction of hereditary defects through “molecular” engineering (pp. 40–41).
In general, the most successful near-term strategies will foreclose some future options while leaving others open. For instance, parents often strive to give their children a great deal of opportunity to pur- sue their own interests but little chance and inclination to deviate from the parents’ values. In the political arena, achieving consensus on an adaptive strategy often requires the ability to foreclose certain future options contingent on future events or information that becomes available. For example, a decision to conduct an environ- mental-impact assessment prior to a construction project may gather the greatest support if all parties believe the construction will go forward or not depending on the conclusions of the assessment. The proper functioning of the adaptive, error-correcting capabilities of democratic governments requires that their constitutions restrict certain behaviors, such as the ability to silence minorities or ignore the results of elections (Holmes, 1995). Similarly, market economies rely on government power to enforce private contracts entered into in anticipation of future events that may or may not unfold as envi- sioned. In general, economies operate in a manner that causes resources to flow to those whose risk-taking is successful and away from those whose risk-taking is not.
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In this report, near-term strategies are explicitly presented as algo- rithms that initially prescribe a particular set of actions. Over time, however, the algorithms incorporate new information by monitoring one or more key trends in the internal or external environment. They may then specify new actions contingent on these observations. Often the concepts and techniques of agent-based modeling are used to represent these algorithms. Precedents exist in the psycho- logical and organizational literatures to support representing deci- sionmaking in the form of algorithms. Humans generally reason using heuristics to determine which information they should track among an infinite sea of possibilities and how they should respond to the expected observations (Gigerenzer and Todd, 1999).
The design of successful adaptive decision strategies is difficult. Human policymakers often conduct mental simulations to play out the implications of decision algorithms and to appeal to experience in determining the decisionmaking heuristic they ought to use. This rule-of-thumb approach works well as long as the future is similar to the past. As the long-term future deviates from familiar experience, trusted rules of thumb can break down. To surmount this obstacle, robust decision methods compare the performance of alternative adaptive decision strategies looking for those that are robust across a
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The tools used in this study allow humans to conduct a systematic, interactive, computer-aided exploration across a multiplicity of plausible futures with the goal of reaching consensus on near-term actions to shape the long-term future favorably. The particular robust decision method used in this study, RAPTM23, begins as ana- lysts define the decision problem faced by their intended audience— e.g., government officials, business leaders, a community of stake- holders party to a decision—and gather a wide variety of information relevant to that decision. This might include quantitative data; rele- vant theoretical understanding from the scientific, economic, and behavioral science literatures; existing computer simulation models; existing forecasts; public positions staked out by parties to the debate; and elicitations of qualitative understandings, intuitions, and values from key parties. Analysts embody the available information in scenario generators—i.e., the computer code designed to trace out the consequences of each alternative set of assumptions.
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THE CHALLENGE OF GLOBAL ENVIRONMENTAL SUSTAINABILITY
What Near-Term Strategy Will Help Ensure Strong Economic Growth and a Healthy Environment over the Course of the Twenty-First Century?
Sustainability presents a serious global policy challenge, one suffi- ciently novel that decisionmakers are likely to make serious mistakes if their actions are not based on quantitative policy analysis. The sustainability debate is well developed. It has identifiable camps that have generated articulate, conflicting arguments that cannot, at pre-
70 Shaping the Next One Hundred Years
sent, be proven to be false. A considerable store of data and computer-simulation modeling is available to inform the debate. Finally, sustainability represents a type of policy problem in which the pursuit of one widely shared goal—in this case, economic growth—can undermine other important objectives—in this case, environmental quality. Each is intrinsically valuable and necessary to the pursuit of the original goal.1
THE “XLRM” FRAMEWORK
As with any formal analysis, the approach used in this study requires assembling and organizing the relevant, available information. Because the process is cyclical and iterative, this step recurs throughout the course of the analysis. To help guide the process of elicitation and discovery and to serve as a formal intellectual book- keeping mechanism, it is useful to group the elements of the analysis into four categories. Policy levers (“L”) are near-term actions that, in various combinations, comprise the strategies decisionmakers want to explore. Exogenous uncertainties (“X”) are factors, outside the control of the decisionmakers, which may nonetheless prove impor- tant in determining the success of their strategies. In the language of scenario planning the Xs help determine the key driving forces that confront decisionmakers. Measures (“M”) are the performance standards that decisionmakers and other interested communities would use to rank the desirability of various scenarios. Relationships (“R”) describe the ways in which the factors relate to one another and so govern how the future may evolve over time based on the deci- sionmakers’ choices of levers and the manifestation of the uncer- tainties, particularly for those attributes addressed by the measures. The relationships are represented in the scenario generator computer simulation code. As described in Chapter Three, there may often be considerable structural uncertainty about many rela- tionships.
In keeping with the didactic purpose of this exercise, no specific group of deci- sionmakers has been envisioned as having responsibility for imple- menting the recommendations of this study. Imagine the analysis as being placed at the disposal of such global gatherings as the United Nations environmental summits.4
Exogenous Uncertainties Affecting the Future (X)
Those who participate in the sustainability debate make important assumptions about the factors likely to guide the course of events over the twenty-first century. These assumptions often prove crucial to supporting their views about desirable near-term policy actions.
3This discussion continues the long-standing practice of ordering the letters XLRM, notwithstanding the fact that it provided clearer exposition in this treatment to discuss these factors in the order found below.
4This work was briefed at the Science Forum, a parallel event of the World Summit on Sustainable Development held in Johannesburg, South Africa, on August 31, 2002 (Lempert, 2002b).
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carrying capacity Resiliency of environment Potential surprises
ronmental quality on population
Future Decisionmakers Information
5The story is often different in the developing countries of the Southern Hemisphere.
Equations contained in modified “Wonderland” scenario generator
(L) Policy Levers
Policies to speed decoupling rate
Rate of improvement in GDP per capita Longevity Environmental quality
mental measures Discount rate Vantage years
6There is, for instance, increasing interest in and understanding of potential abrupt changes in the climate system that might be caused by human actions. See National Academy Press (2002).
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Sustainability has become a key agenda item for many businesses, governments at all levels, and for a wide variety of nongovernmental organizations and other citizen’s groups. At one extreme of the sus- tainability debate lie those who believe that catastrophe awaits if society fails to take immediate and aggressive steps to reduce pollu- tion. Perhaps the most famous exemplars of this camp are the “Limits to Growth” advocates, such as Paul Ehrlich and the Club of Rome (Meadows and Meadows, 1972). Noting the properties of exponential growth and the astounding fact that up to 40 percent of all the energy in the Earth’s biosphere is currently consumed by humans (Vitousek et al., 1986), these commentators argue that the environment imposes real physical limits on the growth of both human populations and economic activity. Other commentators such as Bill McKibbon (1989), following in the tradition of Aldo Leopold (1949), focus on moral rather than physical constraints. They assert that human activities are threatening to engulf all of nature, thereby creating a sterile planet where nothing lives that is unmanaged by humans. They question whether material posses- sions are worth such a cost.
At the other extreme are those such as Julian Simon (Myers, Myers, and Simon, 1994) and, more recently, Bjorn Lomborg (2001), who argue that human ingenuity mobilized by scarcity can eliminate almost any credible environmental constraint. They note that, over the decades, the forecast price increases of most resources deemed “nonrenew-able” have failed to materialize because the incentives and technologies for extraction or for substitution have always improved at a faster rate than society’s demand has increased. The real price of oil, for example, is now lower on average than it has ever been except when shortages induced by political crisis or war tem- porarily inflate the price. The “Unlimited Growth” advocates rec- ommend faster economic growth as a means of alleviating poverty
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and a host of other human miseries. Many in this camp take a liber- tarian bent, focusing on human ingenuity that draws its incentives from markets. Others, such as Gregg Easterbrook (1995), argue that the dramatic improvement in environmental quality in developed countries is the most significant, unsung success story of the last three decades.5 However, they credit innovations in government institutions and regulations that give expression to new human val- ues as the prime movers of this promising trend.
Of course, most commentators and policymakers lie between these two extremes. As commonly conceived, sustainable development seeks to eliminate the seemingly irreconcilable conflicts between “Limits To” and “Unlimited” Growth. As first expressed by the Bruntland Commission, sustainability is often defined as meeting the needs of the present while not compromising the ability of future generations to meet their needs. A vast body of literature attempts to help governments and businesses balance the goals of economic growth and environmental protection. For example, in the GSG sce- narios described in Chapter Two, the “Conventional Worlds” scenar- ios capture the worldview of those who believe that well-regulated markets as currently constituted can meet the challenges of sustain- able development. “Barbarization” scenarios represent those who believe that future challenges may cause such institutions to fail. “Great Transition” offers an alternative set of near-term policies designed to help humankind avoid the potential dangers some see in our current path.
The key economic assumptions that distinguish the positions of the participants in the sustainability debate include the exogenous rates of economic growth—i.e., what the base growth rates will be in the absence of environmental degradation and policy designed to pre- vent such degradation—and the decoupling rate (Azar, Holmberg, and Karlsson, 2002)—that is, that rate at which technological and other forms of innovation reduce the amount of pollution generated per unit of economic output. Some of the most important differ- ences between the Limits to Growth and Unlimited Growth camps rest on assumptions about whether or not the future decoupling rate will outpace the future rate of economic growth. Other core
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Near-Term Policy Levers (L)
In the face of deep uncertainty, potential actions nevertheless could be taken in the present to decisively shape the long-term future. The sustainability literature is replete with suggestions for policies rang- ing from environmental regulations to enhancing innovation, chang- ing values, creating institutions, or conducting environmental research. The policy community is struggling with strategies for combining these potential actions into coherent and effective implementation plans.
A key simplification in this demonstration analysis, and the primary reason it cannot support actionable policy conclusions, is that it considers a greatly truncated menu of policy levers. In fact, the analysis begins by presuming that only one option is available: a pollution tax that can speed the decoupling rate—that is, the rate at which innovation reduces the pollution society generates per unit of economic output. Any chosen lever will have associated costs and time lags. The actual values of these factors are among the key uncertainties. This analysis takes a global perspective and thus does not consider questions of a game-theoretic character regarding how policy choices in one region might affect the choices made in another, even though it is clear that regions are linked economically and environmentally.
For example, this is the philosophy that informs the effort to frame and meet the UN Millennium Development Goals. Our project might well be characterized as one designed to develop hitherto unavailable quantitative means for assessing and choosing such milestones.
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For an analysis in an actual decision-support role intended to produce policy-relevant conclusions, it would be necessary to craft measures that better capture the full range of aspirations held by all of the individuals and groups who must participate in any near-term deci- sions about long-term sustainability. The treatment here merely suggests measures that appear to characterize key points of view rep- resented in the sustainability literature. While the selected measures cannot be claimed to represent the values of actual individuals, they do support a demonstration of how sets of such measures can be used in LTPA.
The measures used in this report focus on three key time series pro- duced by the scenario generator: output per capita, longevity, and environmental carrying capacity. The first two are represented in the HDI, albeit in somewhat different forms. The third time series repre- sents one plausible component often proposed for a Green GDP. A series for level of educational attainment could not also be included because the scenario generator used in this robust decision analysis contains no module for education. The environmental carrying capacity considered here is clearly an abstraction that serves to side step important problems of data availability and interpretation con- fronting those currently trying to construct comprehensive measures of environmental quality.
Appendix A fully describes the measures used in this report to rank the desirability of various scenarios. In brief, however, each measure summarizes the average rate of annual change exhibited by two or three time series for a century or more into the future. These average rates of change are discounted to the present so that near-term improvements (or degradations) in output per capita, longevity, or environmental carrying capacity count more heavily than those in the far future. Many economically focused measures of the human condition use such rate-based approaches. The HDI does not, focus- ing instead on annual snapshots of the relative performance of a large number of countries. The rate-of-change measure is preferable for this study, which must assess trajectories for a small number of regions over a large number of years.