GENIe - Global-problematique Education Network Initiative

UNIVERSITAT POLITÈCNICA DE CATALUNYA Generalitat de Catalunya Departament de Medi Ambient Published by: GENIe European Office CÀTEDRA UNESCO A LA UPC TECNOLOGIA, DESENVOLUPAMENT SOSTENIBLE, DESEQUILIBRIS I CANVI GLOBAL GENIe Global-problematique Education Network Initiative GENIe coberta+blanca 29/10/2001 18:48 P‡gina 1 aaaaa coberta+blanca 29/10/2001 18:48 P‡gina 2 UNIVERSITAT POLITÈCNICA DE CATALUNYA Generalitat de Catalunya Departament de Medi Ambient Published by: GENIe Global-problematique Education Network Initiative GENIe European Office CÀTEDRA UNESCO A LA UPC TECNOLOGIA, DESENVOLUPAMENT SOSTENIBLE, DESEQUILIBRIS I CANVI GLOBAL GENIe 3 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 3 4 GENIe / Global-problematique Education Network Initiative Editors GENIe European Office; UPC Mihajlo D. Mesarovic Josep Xercavins i Valls GENIe Co-ordination Center; CWRU Mihajlo D. Mesarovic Narasingarao Sreenath Proof reading and Correction Ana Andrés Lleó Computer Consultancy Oscar Sahun i Reguant Published by UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change C/Colom, 1 – 08222 Terrassa (Spain) Tel: 00 34 93 739 80 50 Fax: 00 34 93 739 80 32 E-mail: [email protected] http://campusterrassa.upc.es/catedraunesco UPC Information, Image and Publications Service, 2000 (5269) ISBN: 84-7653-751-4 © the authors Acknowledgments The UNESCO Chair at UPC and its associated GENIe European Office have been supported by the Government of Catalonia. The Population Growth, Water and International Peace project at CWRU has been supported by The David and Lucile Packard Foundation. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 4 5 Contents Contents Chapter 1 Presentation.............................................................................................. 7 Chapter 2 "Methodology: Towards Integrated Assessments with Reasoning Support Tools".......................................................................................... 13 MIHAJLO D. MESAROVIC, NARASINGARAO SREENATH, DAVID MCGINNIS, JOSEP XERCAVINS Chapter 3 UNESCO-UNITWIN GENIe ............................................................... 43 Annex to Chapter 3: Recognition of GENIe as UNESCO-UNITWIN network............................................................... 51 Chapter 4 Collaboration Agreement for the Creation of the GENIe European Office with the Support of the Ministry of the Environment of the Government of Catalonia......................................................................... 55 Chapter 5 Some GENIe Workshops “Bridging the Gap between Science and Decision-Making”................................................................. 61 Chapter 6 First Encounter with GLOBESIGHT (GLOBal forESIGHT)................. 69 ALI M. VALI, GUNDO SUSIARJO, NARASINGARAO SREENATH, MIHAJLO D. MESAROVIC, JOSEP XERCAVINS Chapter 7 The INTRANET of GENIe..................................................................... 85 UPC COMPUTER SERVICES, JOSEP XERCAVINS 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 5 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 6 7 Presentation Chapter 11 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 7 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 8 9 Chapter 1 / Presentation 1. Presentation "The challenge in bridging the gap between science and decision-making is in blending reasoning with vision". FEDERICO MAYOR ZARAGOZA at the International Meeting of Science Editors The symbiotic relationship of education, science and culture –the very foundation of the UNESCO mandate– has become increasingly important at the threshold of the new millennium. The formulations of development policies which will satisfy basic human needs on the one hand and sustainability on the other in the context of globalization of the economy, resources allocation, environmental change, etc., mandates that an unprecedented number of scientific facts be taken explicitly into account when considering development policies. At the same time, legitimate aspirations and visions rooted in respective cultures play pivotal role. Hence, it is imperative that science and culture (objective and subjective aspects of reality) be blended together in responsible development policies. Education in the broadest sense is needed to equip the present and future stakeholders for that task in the new era of the complex, global society of the 21st century: a) Decision-makers have to acquire skills to take into account explicitly what the sciences are providing at the time. b) Scientists have to learn how to identify scientific priorities more directly responsive to policy needs. c) The public-at-large has to understand the reality of globalization. d) Perhaps most importantly, education is needed to prepare youth for careers and lifestyles in the 21st century. The UNITWIN-UNESCO CHAIRS PROGRAMME was launched in 1991. Its key features are the rapid transfer of knowledge and assistance within the institutional development of higher education with a special focus on develop5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 9 And so the key question is: how these two categories of indicators are related or how these two sets of indicators are connected, i.e., how the human system functions in time? This requires: a proper representation of the process of interaction between humankind as a system and the natural system; and explicit recognition of the specific and unique character of human functioning as a system. The first aspect (the relationship of humankind with nature) is best understood in terms of the reflexivity concept (see Figure 2.1). Simply put, humanity is changing the environment while simultaneously being changed by it. It is a continuous feedback relationship. Humans are not outside observers of environmental change but rather are on the inside of the system being changed. This imposes a fundamental uncertainty (a limit to complete, objective knowledge or predictability). The human impact and the impact on humans cannot be considered separately but as clearly related (connected) in real-time. Understanding this reflexive, feedback configuration of the global earth/human systems is central to understanding the human role in global environmental change. FIGURE 2.1: REFLEXIVE RELATIONSHIP BETWEEN HUMANKIND AND NATURE The second aspect (proper representation of the specific character of humankind and the role it plays in global environmental change) needs a paradigm different Humankind’s Impact Global Change System Natural Subsystems Natural Subsystems Humankind Subsystem Impact on Humankind GENIe / Global-problematique Education Network Initiative 16 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 16 17 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” than the input/output or state transition paradigm used thus far in the study of global change. In the state transition paradigm the system is assumed to be fully describable in terms of the state of the system at a given time and the system transformation (mapping, transfer functions) of that state to another state as well as the input between two instances in time. This paradigm originated in physical sciences. To convey the true nature of such a paradigm we refer to it as the "Newtonian mechanics" paradigm. It assumes that only lack of data and knowledge prevents us from being able to fully predict the future; there is no room for uncertainty or indeterminism. The state transition (input/output, stimuli/response) view can be useful under limited circumstances in the representation of humankind as a subsystem but erroneous if overextended. Using this paradigm, models (economic, energy, integrated, etc.) are developed in terms of differential (or difference) equations with or without equilibrium processes. It has been observed that the problem with such models is not that their predictions are wrong, but that they are right most of the time except when the predictions are really needed. If the time horizon is short and "business as usual" prevails, the prediction using input/output paradigms does not go wide from the mark. It is when the change is sufficiently large and the consequences are felt over a sufficiently long period of time that the input/output paradigm breaks down. An alternative to state transition is the goal-seeking (or decision-making) paradigm. It has its origin in biology and the study of human behavior rather than physical phenomena. More concisely, the functioning of the system in the goalseeking paradigm is represented by two items: goal(s) of the system; and the processes which the system possesses to pursue these goals and to respond to the influences from the environment. The goal-seeking paradigm requires more items. The following are needed for representation of the system in the most general case: ·A range of alternative actions (decisions), available to the system in response to what is happening or is expected to happen in the system's environment. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 17 ·A range of uncertainties, which the system envisions as possibly affecting the success of the selected decision. The uncertainties can be due to two sources: uncertainty as to what might happen in the environment, i.e., the external input from a range of anticipated inputs; and uncertainty due to an incomplete or inaccurate view (representation, image) of what the outcome of the decision will be even if the external input is correctly anticipated. This represents the bias on the part of the goal-seeker as to how the overall system functions. For example, if the first kind of uncertainty is resolved in the sense that the environmental input is exactly as expected, the outcome can still be uncertain due to the lack of knowledge on the part of the decision system as to how the environment is going to react to the decision. ·A range of consequences (outputs) following implementation of the system's decision. ·An evaluation set ("performance scale"), used by the system to compare the results of alternative actions; i.e., given the outcomes of the two decisions, which of the two is preferable. ·The decision system's view of the environment; i.e., what is the system's understanding of the environment. In other words, what output (consequence) the system expects after a decision is implemented and the environmental influence is correctly anticipated. In reality, it is seldom, if ever, a complete and accurate reflection of the reality. ·An evaluation mapping, used to compare the outcomes of the decisions using the preference scale, and taking into account the "extent or cost of the effort". ·The tolerance function (relation) which indicates the degree of satisfaction with the outcome if a given uncertainty comes to pass. For example, if the conditions are of full certainty, the best (i.e., optimal decision) can be idenGENIe / Global-problematique Education Network Initiative 18 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 18 19 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” tified. If, however, there are several events which are anticipated the performance of the system can be allowed to deteriorate for some uncertainty, but it must stay within a tolerance limit which will ensure "survival of the system". This paradigm accommodates concepts of “satisfactory human behavior” as opposed to the “optimization” view commonly used in economic theory, explicitly accounts for uncertainty -both true uncertainty and uncertainty under risk (usually accounted using probability theory), and tolerance (acceptability, survival, etc.). An important role in this formulation is explicit recognition of uncertainty and the concept of tolerance (acceptability, survival). The performance can deteriorate for extreme occurrences in the environment but it can still be acceptable or satisfactory (the outcome being within tolerance limits) if "survival" of the system is assured regardless of what occurs within the range of anticipated occurrences. Several remarks are helpful in clarifying the contrast between the two paradigms: ·The input/output paradigm is far easier to model and should be legitimately used whenever it does not result in a large distortion of reality. However, if the behavior of the system is truly purposive, i.e., goal-seeking, this might not be possible. An illustration of this can be found in the computer programs for theorem proving, chess playing and the likes. These programs are not developed in terms of state transitions but rather in terms of the so-called end-means, i.e., in terms of goals (ends) and processes (means) to pursue these goals. ·The need for a new, human-based paradigm is recognized even in wellestablished fields such as economics. Kenneth Arrow has recently observed "...the very notion of what constitutes an economic theory may well change. Some economists have maintained that biological evolution is a more appropriate paradigm for economics than equilibrium models analogous to mechanics." 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 19 ·Formalization of the goal-seeking paradigm briefly outlined above provides a basis for a deeper theory of the "human dimension" of global environmental change, as well as for other phenomena where recognition that humans are not inert physical objects (machines) is essential. ·Input/output representation appears to be simpler in the sense that it requires fewer items to be described. This, however, can be misleading. If the system is truly goal-seeking, the input/output representation depends on the range of environmental influences (inputs). Under different circumstances (different category of inputs) the input/output representation becomes different. The system appears to "switch" from one mode of behavior to another (e.g., in the so-called self-organizing systems). If the environmental change is extensive, a large number of alternative representations are needed with the system appearing to switch, in time, from one mode of behavior to another. On the other hand, if the goal-seeking representation is achievable, it remains invariant over a large range of environmental inputs. ·Goal-seeking representation requires a deeper understanding of the system and is often difficult, if not prohibitive. However, even if the input/output description(s) has to be used, the results of the analysis should be interpreted in reference to the true paradigm of the system. Accepting the need for a reflexive and goal-seeking representation of humankind in global change, the question is how this can be realized. One approach is to develop computer algorithms which represent the processes which the goal-seeking system uses to pursue its goal. This is within the domain of so-called artificial intelligence. GENIe / Global-problematique Education Network Initiative 20 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 20 THEN (Consequences at the End of the Entire Policy Time Period) IF (Assumption and Policy Options for the Entire Time Period) Another approach being considered at present consists of putting the human inside the model. Rather than simulating goal-seeking behavior by computer algorithms, the human (user) is put in the position of being an integral part of the model (a component, subsystem) representing goal-seeking (decision-making) behavior. The human is in a reflexive relationship with the computer models of the natural systems. One way to look at this is to view the human as being in a "game" type, interactive relationship with the computer algorithm parts of the model. The human/computer inter-linkage is "tight" in the sense that the computer model cannot evolve in time unless the user "simulates" the functioning of the humankind system. The architecture is that of a blended simulation/gaming process. It is not pure simulation because the computer components of the total model cannot proceed to the next step without the human's actions and it is not pure gaming in the sense that the human action is deeply imbedded in the structure of the overall system (model) –it merely represents the subjective view of humans as to how humankind responds to changes in the environment. A brief description of such an interaction in reference to time evolution is given in Figures 2.2 and 2.3. In order to blend subjective (humanistic, non-numerical) aspects of the future and to avoid projection of the past into the future in a "mechanistic" fashion governed exclusively by a model, symbiotic interactive processes of scenario formulation and assessment is used in these studies. In traditional scenario analysis (Figure 2.2) the assumptions and policy options are selected at the beginning of the model run and the future is determined from the initial time until the end of the entire policy time horizon solely by the fixed structure of the computer model and parameters estimated from the past data trends. Scenario Generation Using the Traditional Computer Modeling Approach FIGURE 2.2 21 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” Model 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 21 In the interactive process used in the policy analysis (Figure 2.3) the future course is outlined in time increments; the human is but a sub-model on par with the computer algorithms. The process starts with the implementation of present policies and assumptions about uncertainties over a relatively short time increment (although the long-term view is taken into account as needed in making the incremental assumptions). The computer program portion of the model generates feasible consequences of the policies and assumptions at the end of the first increment. The human then makes new policy choices and assumptions for the second time increment on the basis of the newly arrived at state of the system at the end of the first time increment. In response, the computer generates the state of the system at the end of the second time increment providing a basis for policy consideration by the human for the next time increment. The process proceeds iteratively until the end of the entire policy time horizon. Computer algorithms (models) do not predict the future in such a process but rather have the role of consistency checks to make the vision and goals of the human consistent with the facts (reality). Implementation of such human/computer modeling goes beyond the time interactive process. The challenge of developing such symbiotic, human/computer models consists fundamentally of carefully distinguishing where human intuition and common sense, vision, views on uncertainty, etc., (subjective aspects) are needed from where the logic, numbers, and facts (objective aspects) are used for deeper computer analyses. Symbiotic human/computer modeling provides a framework to take into account non-numerical (non-measurable) aspects of reality. The omission of non-measurable aspects can lead to a major distortion of the representation. GENIe / Global-problematique Education Network Initiative 22 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 22 Scenario Generation Using the Human/Computer Partnership Process FIGURE 2.3 2.2. Characteristics of Global Earth/Human Issues and Systems: Complexity and Multilevel Hierarchy Modeling Uncertainty and complexity are two different obstacles to understanding which should not to be confused; instead they should be addressed in different ways. Making representation of a real system more complex does not diminish the underlying uncertainty; rather it merely obscures the source of the lack of understanding. 23 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” THEN1 (Consequences at the End of the First Time Increment) THEN2 (Consequences at the End of the Second Time Increment) IF1 (Assumptions and Policies for the First Time Increment) IF2 (Assumptions and Policies for the Second Time Increment) COMPUTER PROGRAM PORTION OF THE (OVERALL) MODEL HUMAN AS A COMPONENT OF THE (OVERALL) MODEL ...etc. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 23 Actually, in a number of instances a simple projection of trends is not much different than the results obtained by large input/output models. The size of the model does not improve its being true to the reality. Increasing the size of the model could be counter-productive by reducing the transparency of representation (i.e., obscuring what is really happening). This is particularly true when analysis is to result in real-life policies. Complexity is a concept (or term) which does not have a meaning in itself but acquires its meaning only in a broader context. There is a dynamic, burgeoning, exciting new field of "complexitology" which attempts to come to grips with a general theory. The research has been criticized as accommodating too many distinct, even contradictory, views. This is a bit unfair because complexity is a derived rather than a primary concept. It can legitimately be defined in different ways within different contexts. Global environmental change is most certainly a complex phenomenon. Understanding global environmental change requires the notion of a complex system. In this regard, the notion of a complex system in the mathematical theory of general systems is relevant. The starting point is the notion of a system as a relation among items or objects. A complex system is then defined as a relation among the systems. Items which form a complex system through interaction (i.e., subsystems) have their own recognizable boundary and existence while their behavior (functioning) is conditioned by their being integrated in the overall system. The human body is an obvious example; its parts (i.e., organs) are recognizable as such but their functioning (and even existence) is conditioned as being part of the total system, i.e., body. In our view, it is futile to argue whether this concept is a valid representation of the complexity. What is important is whether the concept can help us in addressing the challenges such as global environmental change. We argue that the concept of a complex system can be useful in that respect in two ways: in presenting a more truthful and credible representation of the global change environmental phenomenon; and in providing a framework for representation of the decision-making processes in the global environmental change. GENIe / Global-problematique Education Network Initiative 24 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 24 25 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” Several additional remarks on complexity as reflected in the above notion of complex systems can help clarify the concept: ·Complexity should not be confused with unpredictability or indeterminacy ("surprising behavior"). A simple system in the sense of being faithfully described by a small set of equations can be chaotic (i.e., indeterminate) or self-organizing (i.e., have several modes of behavior) exhibiting surprising (unexpected) behavior without being complex. ·The concept of a complex system has an intimate relationship with the concept of hierarchy (another concept which can have alternative legitimate interpretations!). The behavior of a complex system, by definition, can be considered on at least two levels: the level of subsystems; and the level of the overall system. Conversely, a hierarchical system which has two or more levels can be legitimately considered as complex. The distinction between complex and "complicated" systems is suggestive in this context. A single level, large, integrated model is "complicated". For example, some computer-based policy models takes hours, if not days, for a single run. Such models are not practical for policy analysis where uncertainty prevails and transparency is a prerequisite. In its crudest form a complex system is viewed as having a large number of variables (items) and being characterized by the phrase, "everything depends on everything else." However, complex systems do function in nature in an orderly fashion and have functioned as so throughout human history. The Roman Empire provides an example of a system that was truly complex in view of the available means for communication and management. Yet the system functioned successfully for centuries. The statement "everything depends on everything else" indicates the breakdown state of the complex system which otherwise functions by its own internal management rules. Under normal conditions, a complex system possesses internal rules of management or behavior which allocate the responsibilities to subsystems commensurate to their information processing and decision-making capacities. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 25 sphere and non-living sphere. Together this is called Nature sphere. In the final representation in the right hand portion of Figure 2.6, the human sphere is further deconstructed into representation with hierarchies. In general the subsystems at the top in a hierarchical representation provide constraints through the downward directed arrows, whereas the upward arrows from the subsystems at the lower levels provide performance specification to their upper level. Other examples of such hierarchical stratification is given in Figure 2.7. FIGURE 2.6: DECONSTRUCTION OF THE GLOBAL SYSTEM GENIe / Global-problematique Education Network Initiative 32 GLOBE Biosphere Non-living Sphere GLOBE META-MODEL Human Sphere Finance Values, Cultures Stratum Societal Stratum Economics Nature Sphere Non-Human Species Sphere Non-living Sphere Demography Techno/Resources Sphere: Human Life Support Systems Human Sphere Non human Species Sphere Non-living Sphere Nature Sphere 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 32 33 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” FIGURE 2.7: OTHER EXAMPLES OF HIERARCHICAL REPRESENTATION 2.4. Integrated Assessment as a Process The concept of integrated assessment is then introduced in recognition of the less than reliable forecast capabilities of integrating modeling. Although, in general, integrated assessment is not identified with integrated modeling, in practice, integrated assessment very often turns out to be the development of an integrated model followed by sensitivity analysis. From the cybernetic viewpoint, integrated assessment is a human-based process of reasoning about the future in which all available tools and information are used in contrast to the computer-based approach, such as in integrated modeling plus sensitivity analysis. The process is akin to the decision support approach used in management science and practice. AUTOMATON PHYSICAL DEVICE The computer as a complex system Stratification Hierarchy: Conceptual Levels Stratum 4: Composition Stratum 3: Sentences Stratum 2: Words Stratum 1: Sound A four-strata diagram of a poem reciting machine PSYCHOLOGY PHYSIOLOGY BIO-CHEMISTRY MOLECULAR LEVEL The human as a complex system 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 33 GENIe / Global-problematique Education Network Initiative 34 2.5. GLOBESIGHT: a Reasoning Support Tool To research integrated assessment as a process, a prototype of an integrated assessment support system, named GLOBESIGHT –from GLOBal forESIGHT– has been developed and used in several alternative circumstances around the GENIe (Global-problematique Education Network Initiative), and under the leadership of Professor Mihajlo D. Mesarovic. In the process that begin in understanding the past, evaluating the present and looking into different feasible futures, GLOBESIGHT, playing a role of a “consultant”, requires the human to represent the subjective and qualitative aspects of the issue at hand whereas known data, procedures, models are inherent in it. Historical data (time series), other kinds of information (i.e., textual), and a family of models (both integrated and partial) are used in the reasoning process. The architecture of GLOBESIGHT is shown in Figure 2.9. GLOBESIGHT reasoning support software has been available on SUN hardware as well as PC hardware for a number of years. SUN Solaris and LINUX version are available. Currently only Microsoft Windows 95/98 and Windows NT are supported. The front end is based on Visual C++/Visual Basic with the back end in MS Access. Using a time interactive, "reflexive", feedback configuration of the human and the computer, the human and the computer "walk hand in hand", step by step, along alternative, feasible, future paths. The time horizon is broken into shorter time intervals and at the end of each time interval the human reconsiders assumptions (regarding policies, as well as scientific uncertainties) and makes the necessary changes for the next time interval. The scenario which emerges in such a process is not known beforehand (i.e., at the beginning of the model run). It is the result of a symbiotic relationship between the human and the computer in which objective (numerical) and subjective (human visions) sides of the future evolution are blended. (Remember the detailed discussion of section 2.1.). 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 34 So with this reasoning tool we are able to, as a summary: ·Blending Science with Vision: To quote Federico Mayor, Director General of UNESCO, in 1995: “The challenge in bridging the gap between science and decision-making is in blending reasoning with vision”. In other words, we want to blend objective with subjective, quantitative with qualitative, numerical with non-numerical. This means that one needs to account for scientific as well as political, sociological, and behavioral –the so called soft aspects– explicitly when considering modeling policy formulation and analysis. ·Reasoning About the Future: Foresight and insight rather than forecast (numerical prediction), is at the heart of our approach (see Figure 2.8). Developing foresight involves considering all possible (not probable) contingencies and developing a feel for potential futures. As one saying goes –the future is not yet determined completely since decision about the future are yet to be made. Thus, we rule out forecast as a goal. True uncertainty in parameters would not allow us to forecast. Insight, on the other hand, relates to the approach of determining or finding dominant relationships that helps in understanding and explaining away the system behavior based on experimentation of the model. FIGURE 2.8: DIFFERENCE BETWEEN FORECAST, FORESIGHT, AND INSIGHT 35 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” Forecast Prediction Foresight Anticipating Uncertainties Insight Understanding Forces behind Development 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 35 GENIe / Global-problematique Education Network Initiative 36 The GLOBESIGHT analysis support system consists of the following modules (see again Figure 2.9): ·The Information Base contains quantitative, and verbal (or qualitative), data and information that is useful to the user for consulting during the exploration of an issue at hand. This information and data of a country/region/world takes the form of description of the geography, culture, socio-economic data and so on. The qualitative data will be helpful to the user to get a general idea about the conditions when researching specific issues in a region. The quantitative data in the form of numerical time series gives us the past and present trends in demography, economy resources, etc. FIGURE 2.9 ·The Issues Base is a depository of the analyses (results, as well as assumptions) already conducted for future reference, comparative evaluation and extension of analyses. Information Base Models Base Issues Base Functionalities Base GLOBESIGHT Architecture Analysis Support System Human 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 36 37 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” ·The Funcionalities Base contains interactive procedures which allows the user to actively participate in the process. It deals with three tools basically (input, output, and process). Broadly input consists of data import and model management utilities. Utility exists to transfer data into and out of the database. Output formats include multi-axis graphs with an easy to use interface to change different type of plots (line, bar, stacked bar, pie, etc.). In addition a geographical information system (GIS) interface is available. Features such as rivers could be overlaid on the graphs. Standard geography views are included. Interpolation routine to shape key inputs such as rate of economic growth, etc. using multiple interpolation methods are available. 2.6. The Models Base in GLOBESIGHT First of all we try with our models to combing scientific integrity and transparency of models: ·We model only those parts of the system where scientific data and scientific knowledge is available. This essentially means “Do not model what is not modelable.” Adhering to this principle is easier said than done. Modeling is an art. Knowing what aspects of the system to model and to what depth/level is primarily driven by the requirements of the analysis and the availability of data to parameterize the system. Ability to recognize this comes with experience. The models that we use are borrowed from the literature in specific disciplines, or have basis in them. Our models can be a simple representation but have a scientific basis. Its parameters values are computed based on more complicated model runs (for example more complicated models running on supercomputer), data available through detailed analysis by credible research, and further made available in literature. In keeping with this principle we do not model the political process, expectations of the people, any behavioral aspects, values, attitudes, cultural norms, the impact of basic human physical and other needs, on society, and economy, etc. These subjective aspects 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 37 GENIe / Global-problematique Education Network Initiative 38 accounted for by the approach labeled as “putting the human inside the model” largely explained before. Models that we use will be reduced form models. This approach is one of the latest new trend in complex system modeling particularly for policy analysis (the “goal” in our study). Reduced form models also reflect the final audience for our approach who are from decision-making, education and the public domain. Rather than building complex models dominant relationships (sometimes key identities, e.g., kaya identity, PAT identity) having a strong interaction between variables are identified with the parameters; complexity is traded for uncertainty in parameter changes. By reduced form models we also mean models that are easier to understand and explain to decision-making staff, decision-makers or the public. This will assure that while the models are scientifically credible, the results of the models would be easy to explain. These “small” but “approximate” models are parameterized from the results of the supercomputer models. Often during rigorous scientific representation the model transparency is lost and one would require the model builder or an expert to be present to operate or use the model. This principle helps us to overcome this limitation. Then, consequently with all the aspects seen in this chapter, we try to address these considerations, as well as the need to deal with complexity and uncertainty in a proper, differentiated manner using a multilevel hierarchical architecture and an integrated assessment approach. In the simplest terms, models for determining, for example, population evolution are developed on three levels. On the higher, policy, level assessment is made by aggregate considerations. On that level only the key factors are represented, while detailed mechanisms of how these factors evolve over time is either assumed or delegated to separate, more specific studies. On the lower level, such detailed considerations are conducted either on an integrated or sectorial basis. The result is a two-layer hierarchical structure illustrated for the global warming issue in Figure 2.4. The model on the higher level is parameterized by the analysis on the lower level while the results of the policy analysis on the higher level represent constraints for the assessment on the model level. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 38 39 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” Taking population issue like another example, and the first part of the policy analysis of this study, the population model first level consists of a simple first order growth rate equation popt=popt–1 * [1+ rpopt–1/100] where poptpopulation of the region in the year ‘t’, and rpop - rate of population growth in percentage In words the equation above simply states that population next year is the population this year plus change in the population represented by the growth rate times the population this year. Such a representation is not inaccurate but could be highly uncertain with all the uncertainty embodied in the growth rate. A second level population model resolves the uncertainty somewhat by representing the births and deaths separately but statistically through the use of crude birth (crbrt) and crude death (crdth) rate –usually given in the units of per thousands of population–. Thus the second level model is represented by: popt=popt–1+ popt–1 *[crbrtt–1 – crdtht–1] and the rate of population growth is computed now as rpopt–1= [crbrtt–1 – crdtht–1]/10 The third level model tracks individual cohorts from age 1 through age 85 and age 85+, and uses fertility and mortality information. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 39 GENIe / Global-problematique Education Network Initiative 40 It is very important to insist and to realize that the very simple model on the higher level is not inaccurate. Rather, it is uncertain since the change in the growth rate depends on a number of uncertain factors. However, given a growth rate profile, the model correctly outlines the population evolution. In other words, it is the uncertainty of the input rather than inaccuracy of the model structure, which is reflected on the top level. On the lower level the relationship between uncertainty and complexity changes. While complexity is increased, uncertainty still remains but it is within a reduced range. Even if the dynamics of the population on the lower level is properly represented in terms of age cohorts, the question of attitudes towards family planning and the impact of education, religion and other factors, still remain uncertain. In other words, uncertainty goes deeper and deeper and still remains there. Uncertainty cannot be removed by increasing complexity. What is achieved, however, is that the range of uncertainty is reduced and the assessment could lead to more feasible, realistic results. For example, on the higher level one can assume a dramatic population growth rate change, i.e., dropping to zero in say 10-15 years. But the analysis on the deeper level would indicate, however, the impossibility of such an assumption in view of the dynamics of the age cohort nature of the population. If the age distribution pyramid is broad-based (i.e., percentage of population of young people is much higher than that of older people), then the population growth will continue for a period of time even if family size is transformed overnight to the replacement level. This is obviously due to the fact that the number of girls at an early age range is much higher than the number of women in the reproductive age range and since these girls will move into the reproductive age cohort, the number of children will still be as high if not higher than before even if family size is reduced. This is a well-known phenomenon which has to be taken into account when making assumptions on the policy level. The approach then is the following: using the population model on the lower level, a number of alternative scenarios regarding attitudes towards family planning are analyzed and the family of population growth rate time profiles is used as an alternative inputs to the population model on the top level. This prevents unreasonable and unsubstantiated assumptions from the higher level while still 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 40 Chapter 2 / “Methodology: Towards Integrated Assessments with Reasoning Support Tools” leaving enough room for uncertainty considerations. The approach taken is to focus on the policy level with additional assessment on the lower level for the assumptions that need to be better justified. Essentially, analysis has been conducted in terms of the growth rates of the relevant factors with the justification of growth rates changes provided by the analysis on the lower level. 2.6.1. About the more general Equations in our GLOBESIGHT Models Let us return again to our “familiar” population equation. popt=popt–1 *[1+ rpopt–1/100] which in general could be formulate in the following popt=popt–1 *[1+rpopmt–1 *rpopt–1/100]. This equation looks simple (and indeed it is) and can be grasp intuitively and easily understandable. But often we underestimated the underlying concept. From the mathematical point of view it is an integrated equation that represents the dynamic variation on time of the variable popt. But, why is it this? Because the evolution of this variable on time depends of the initial value –the initial quantity– of this variable and then, mathematically speaking, the universal form of the description of this dynamic evolution (for all kind of phenomenaís in which the evolution on time of the variable depends on the quantity of the variable that we have initially) is an exponential law, which integrated form is the form that we are using here and in general in our models (of course if the variables that we would like to represent follow this kind of evolution). We will use the multipliers, i.e. parameters, i.e. rpopmt–1, in order to take into account the possible or normal variation in time of the time constant (“the rate”) that define the intensity of the variation. 41 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 41 GENIe / Global-problematique Education Network Initiative 48 b) Instructions on the use of joint information which will contain data on key global, regional, national and sub-national indicators, as well as textual information on culture, geography, societal organization, individual values and preferences, etc. Manuals describing scenario development as developed and used by the global issues assessment teaching tool (GLOBESIGHT). A tutorial which includes case studies presented in sufficient detail for easy classroom use. c) Procedures for use of the Internet for interactive communication. 5. Long-Term Objectives 5.1. In the context of universities, education and research are two sides of the same coin. Thus in the long-term, the network must have common research objectives in the Global Problematique. GENIe will contribute positively through research capacity building in the GPIs, especially in developing countries. 5.2. Endogenize the working methods for international comparability, transparency and responsibility in dealing with global issues. 5.3. In recognizing the potential of GENIe we expect that GPIs in future will take a regional leadership role in developing GENIe sub-networks. 5.4. GENIe will actively support and encourage appropriate course development for students in education institutions prior to their enrollment at an university. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 48 Chapter 3 / UNESCO-UNITWIN GENIe 6. Organisation of the UNESCO-UNITWIN Network 6.1. The Advisory Committee will be made up of representatives selected by the Network’s members. 6.2. According to the Foundation Workshop of GENIe, Mihajlo D. Mesarovic, Global Change Advisor to the Director General of UNESCO and Cady Staley Professor of Case Western Reserve University, Cleveland, Ohio, USA, is the co-ordinator of GENIe, and is authorized to sign this charter in the name of GENIe with the UNESCO. 6.3. The Network’s headquarters will be located at the CWRU, Case Western Reserve University (Ohio, USA) –GENIe Co-ordination Center–, and at the UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change of UPC, Polythecnic University of Catalonia (SPAIN) –GENIe European Office–. 49 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 49 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 50 Recognition of GENIe as UNESCO-UNITWIN Network Annex to Chapter 33 51 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 51 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 52 Annex to Chapter 3 / Recognition of GENIe as UNESCO UNITWIN Network Excmo. Sr. D. Jaume Pagès Rector Universitat Politècnica de Catalunya Re.: PROJECT 13 July 1999 Appreciate Rector and friend, I am pleased to inform you that after the meeting held in March with Mr. Mesarovic and Mr. Xercavins, and after having carefully analysed all the activities carried out by the UNESCO Chair of Technology, Sustainable Development, Imbalances and Global Change, I have decided that the GENIe Network (Global Problematique Education Network Initiative), of which this UNESCO Chair is an integral part, will be converted into a UNITWIN Network. I should also like to inform you that I agree with your proposals regarding: 1. That the representative of the net will be its President, Mr. Mihajlo Mesarovic. 2. That the Office of the net will be shared by the Case Western Reserve University (Cleveland, Ohio, USA) and the Technical University of Catalonia, this last one through the UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change. 3. That the European Office of GENIe, which has the support of the Ministry of the Environment of the Government of Catalonia, will have its site at the Technical University of Catalonia. Could you please give my congratulations to Mr. Mesarovic, Mr. Xercavins and all their team for the excellent quality of the work they have done, which coincides fully with the priorities of UNESCO. Please accept my best wishes. Federico Mayor Zaragoza Director General of UNESCO 53 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 53 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 54 Collaboration Agreement for the Creation of the GENIe European Office with the Support of the Ministry of the Environment of the Government of Catalonia Chapter 44 55 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 55 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 56 Chapter 4 / Collaboration Agreement for the Creation of the GENIe European Office with the Support of the Ministry of the Environment of the Government of Catalonia COLLABORATION AGREEMENT BETWEEN THE MINISTRY OF THE ENVIRONMENT OF THE GOVERNMENT OF CATALONIA AND THE TECHNICAL UNIVERSITY OF CATALONIA FOR THE CREATION OF THE EUROPEAN OFFICE OF THE GLOBAL-PROBLEMATIQUE EDUCATION NETWORK INITIATIVE (GENIe) Joan Ignasi Puigdollers i Nobolm, Minister of the Environment of the Government of Catalonia, acting ex officio on behalf of the Ministry of the Environment of the Government of Catalonia (hereinafter Ministry of the Environment) AND Jaume Pagès Fita, Rector of the Technical University of Catalonia, acting ex officio on behalf of the Technical University of Catalonia (hereinafter UPC) HEREBY STATE 1. That the Global-Problematique Education Network Initiative (hereinafter GENIe) was set up under the auspices of the UNESCO in 1996 for the purpose of promoting knowledge and education regarding the human difficulties encountered in the emerging global society of the 21st century. This education is channeled on the one hand towards students and on the other towards both scientists and those entrusted with finding a way to bridge the gap between the two groups and enable them to work in collaboration. 2. That the Ministry of the Environment has been a member organization of GENIe since April 1997 and is interested in ensuring a greater presence for its initiatives within Europe. 3. That Case Western Reserve University (Cleveland, Ohio, USA) is the international headquarters of GENIe, but that in the light of recent developments a need is felt for a European office collaborating with the US office in general organizational tasks and in particular focusing more specifically on its European and African members. 57 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 57 GENIe / Global-problematique Education Network Initiative 64 •Prof. Mesarovic has been appointed as the Scientific Advisor on Global Change to Dr. Federico Mayor, Director General of UNESCO. •Two workshops with participation of decision-making staff and scientists were conducted on the global climate change issue. The first workshop was in Venice (Italy) with a twelve-country participation with global coverage. The second was in Santiago (Chile) co-sponsored by the InterAmerican Institute for Global Change (IAI), with 14 countries of IAI taking part. •In 1996, Global-problematique Education Network Initiative (GENIe) was formed with Profs. Mesarovic and Sreenath as co-directors involving initially fourteen universities with global coverage. In 1998, the GENIe European Office was formed in Barcelona, Spain, sponsored by the Government of Catalonia and directed by Prof. Josep Xercavins. Global virtual classroom is one of the highlights of this effort involving students connected via Internet for multicultural, participatory scenario development and analysis. •Prof. Sreenath has accepted to be a member of the Scenario Panel for the World Water Vision Commission formed under the auspices of the World Water Council. As part of this involvement the Mesarovic/Sreenath team has been asked to develop water vision scenario case studies as a contribution to the construction of the vision tool. •Packard Foundation has given a two year grant to Case Western Reserve University for Profs. Mesarovic and Sreenath’s effort on Population Growth, Water Scarcity and International Negotiations. 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 64 Chapter 5 / Some GENIe Workshops “Bridging the Gap between Science and Decision-Making” Approach The approach used in UNESCO efforts stem from the research conducted for the second report for the Club of Rome “Mankind at the Turning Point”, published in 1974 and co-authored by Prof. Mesarovic and Eduardo Pestel. The basic approach and methodology has undergone a three generational evolution. The basic principles of the approach are: • Scientific Integrity Model only if scientific data and scientific knowledge is available. “Do not model what is not modelable.” • Transparency Reduced from models for decision-making, education and the public domain. • Focus on “Problematique” Holistic perspective of development. • Participatory “Symbiotic” Reasoning Process Time Interactive Use of Scientific Information: “Putting human inside computer”. A reasoning support system termed GLOBESIGHT –short for GLOBal forESIGHT– has been developed to implement the basic principles. 65 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 65 GENIe / Global-problematique Education Network Initiative 66 5.2 “Bridging the Gap between Science and Society in Decision-Making”: Climate Change; the Past, Present and Future of the Kyoto Conference (Terrassa, Spain; 18 December 1998) Approach The so-called greenhouse effect has come about as a result of excessive concentrations of gases in the atmosphere, mainly CO2, originating from the burning of fossil fuels (coal, oil, gas) and resulting in an increase in the radiated energy which is reflected back from the atmosphere to the surface of the earth. Thus, the greenhouse effect leads to the global warming of the earth’s surface, giving rise to climate change and a number of related phenomena. These effects are global in nature (for example, they are brought about wherever fossil fuels are burned, and we all suffer –or will suffer– the consequences). Finding solutions to these problems will involve decision-making on many different scales (state, nation, city, individual citizen) and science and technology also have an important contribution to make. Organization The main philosophical and practical aim of the UNESCO Global Problematique Education Network Initiative (GENIe) is to co-ordinate an international drive for training and education on a number of levels in the area of global issues (population, natural resources, water shortage, food, waste, climate change and others). Made up of basically of universities and a number of secondary schools from a wide range of countries (USA, Brazil, Argentina, Nigeria, Kenya, Portugal, Catalonia, Germany, Russia, China, India and Japan), GENIe also aims to bring the world of science and technology on one hand and society in general on the other closer together in the process of making the decisions 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 66 Chapter 5 / Some GENIe Workshops “Bridging the Gap between Science and Decision-Making” which are so necessary if we are to deal with the challenges which will face us in the 21st century. The Government of Catalonia has supported the initiative and has become its main promoter, creating the GENIe European Office, which is based at the Technical University of Catalonia (UPC) and is closely associated with the UPC UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change. The organizers of the workshop are: LLUÍS MIRET, JORDI HUGUET, MIQUEL RALLÓ, XAVIER RODÓ, JUAN MARTÍNEZ, JOSEP XERCAVINS Collaborators and teachers of the UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change at UPC (Technical University of Catalonia) ALI VALI, GUNDO SUSIARJO, NARASINGARAO SREENATH, MIHAJLO D. MESAROVIC Collaborators and teachers of the Global Change Technology Group of Case Western Reserve University (CWRU), Cleveland, Ohio, USA Programme • Presentation of GENIe and the GENIe European Office • Global Warming and Climate Change • Can Global Atmospheric Conditions be Reversed to the Pre-industrial Revolution State? 67 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 67 • Kyoto (The Past, Present and Future of the Kyoto Conference) and Europe (before and after) • Methodology for Outlining Future Scenarios of Global Warming and a Participative Exercise Involving All the Attendants GENIe / Global-problematique Education Network Initiative 68 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 68 First Encounter with GLOBESIGHT (GLOBal forESIGHT) Ali M. Vali Gundo Susiarjo Mihajlo D. Mesarovic Narasingarao Sreenath Josep Xercavins Chapter 66 69 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 69 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 70 Chapter 6 / First Encounter with GLOBESIGHT (GLOBal forESIGHT) 6. First Encounter with GLOBESIGHT (GLOBal forESIGHT) ALI M. VALI, GUNDO SUSIARJO, MIHAJLO D. MESAROVIC, NARASINGARAO SREENATH, JOSEP XERCAVINS From one tutorial of GLOBESIGHT, and as a preliminary “taste” of it, we are including in this publication one of the easiest examples of its uses. 6.1. Starting GLOBESIGHT In this section you will learn how to start GLOBESIGHT. STEP 1: To start the population model we first load the GLOBESIGHT. Double click on the Globesight icon on the desktop. A Splash screen as in Figure 6.1 will appear. FIGURE 6.1: SPLASH SCREEN 71 5269 GENIe (tripa) 29/10/2001 18:40 P‡gina 71 GENIe / Global-problematique Education Network Initiative 72 STEP 2: You can either close the Splash Screen window by clicking on at the top right hand corner of the window, or the window will disappear automatically after a few seconds. You are now in the Main window (see Figure 6.2). The Main window is usually hidden behind the Splash Screen when you first open GLOBESIGHT. FIGURE 6.2: MAIN WINDOW 5269 GENIe (tripa) 29/10/2001 18:41 P‡gina 72 Chapter 6 / First Encounter with GLOBESIGHT (GLOBal forESIGHT) The Main window contains a menubar, a toolbar of picture buttons (icons), a table of views, and a set of buttons that manipulate the views. The complete functionality of GLOBESIGHT is available via the menubar; the toolbar serves as an iconic shorthand to access the menubar functionality. Observe the location of the Menus, Icons, Table, and Buttons on the Main window. A description of all other icons is given in Figure 6.3. A subset of these icons appear in the other windows as well as in the bottom right hand corner. Clicking on any of these icons will invoke their appropriate functionality and bring forth their respective windows. Practice clicking any of the icons. Finally, from most screens you can access the Main window using the Home icon found at the bottom right hand corner of the window. FIGURE 6.3: DESCRIPTION OF ICONS IN THE TOOLBAR REMARK 1: In this tutorial: (i) All references to windows, frames inside windows, and tables inside the frames used in GLOBESIGHT are in italics, e.g., the main window is referred to as Main window, the frame in Main window is the Views frame, and the table in the Views frame is the Views table. 73 5269 GENIe (tripa) 29/10/2001 18:41 P‡gina 73 GENIe / Global-problematique Education Network Initiative 80 STEP 14: Choose Set variables from the View menu in the Main window. Alternately use the Set Variables icon in the toolbar. STEP 15: Scroll through the variable names in the Variables table, and double click on Population First Level Rate Multiplier (rpopfm). Choose Subscript 1 in the Selected Variables table to be Africa again by double clicking on the mouse. STEP 16: Click on the Value frame and enter in 1 and 0.7 for the values in the First Year and Last Year boxes respectively (see Figure 6.11). Select the interpolation type to be linear in the Interpolation frame. Then click on the Interpolate button. This action, linearly interpolates the values of rpopfm, from 1 to 0.7. REMARK 5: There are other techniques for interpolation that have been implemented in GLOBESIGHT, that is, “constant growth rate” and “variable growth rate”. FIGURE 6.11: ENTER DATA FOR SCENARIO (POPULATION FIRST LEVEL RATE MULTIPLIER – RPOPFM) STEP 17: Now click on the Run Model icon at the bottom right hand corner of the window. Go through the procedure of running the model as you did earlier in Section 5. 5269 GENIe (tripa) 29/10/2001 18:41 P‡gina 80 Chapter 6 / First Encounter with GLOBESIGHT (GLOBal forESIGHT) 6.4. Viewing and Plotting Results 6.4.1. Overview In this section you will compare the results of the BaU and the 70% rate reduction scenario illustrating the concept of demographic transition. You will learn to plot the time series of the variables in the model to compare these scenarios. 6.4.2. Procedure for Creating a View The Show Variables window is used to define new views. To create a new view click on Show Variables icon . This will open up the Show Variables window. The output of the model is the Population First Level (popf). We will use the Show Variables window to create a new view to plot the output. STEP 18: Use the scrollbar on the Variables table and scroll down till you see the variable Population First Level. STEP 19: Select the variable Population First Level by double clicking on the row. The variable Population First Levelwill now appear in the Selected Variables table (see Figure 6.12). The Subscript 1 column contains the subscript of the first dimension of the variable Population First Level. You will see “Egypt” since this is the first entry in the first dimension Regions of the variable. STEP 20: To select another subscript, first click once on “Egypt” in the Selected Variables table in the Subscript 1 column. Now double click again on “Egypt” and a popup menu of subscripts will appear (see Figure 6.13). Click on Africa in this menu. STEP 21: Repeat Step 1 through Step 3 for the variable Population First Level - BaU Scenario. If you made a mistake you can clear the selections by pressing the Clear Selections button. This clears the entire Selected Variables table. 81 5269 GENIe (tripa) 29/10/2001 18:41 P‡gina 81 GENIe / Global-problematique Education Network Initiative 82 STEP 22: In the Year frame set the First Year to be 1990 and the Last Year to be 2100. STEP 23: Select Line as the type of graph you want to see in the View frame. STEP 24: In the Formatframe choose Longin the Namescombo box. Enter 10 for Skip Points. Leave the Min and Max boxes empty (this will be chosen automatically by the system unless you enter the minimum and maximum range to plot). STEP 25: Enter a title in the View frame by typing “Population First Level - Demographic Transition”. STEP 26: Save the current state of the Show Variables window as a view by pressing the Save button. This action will save the view with the above title in the Main window. Now whenever you want to view the population of Africa in comparison to a scenario that you have run all you need to do is go to the main window and double click on the view Population First Level - Demographic Transition. REMARK 6: To display the graph, press the Show button. In this case you will get the graph shown on Figure 6.14. You can do that whether you have saved the graph or not. STEP 27: Click on the Home icon to invoke the Main window. Click on the view Population First Level - Demographic Transition. You will get Figure 6.14. FIGURE 6.12: SELECTED VARIABLES FRAME 5269 GENIe (tripa) 29/10/2001 18:41 P‡gina 82 Chapter 6 / First Encounter with GLOBESIGHT (GLOBal forESIGHT) FIGURE 6.13: LIST OF SUBSCRIPTS FIGURE 6.14: POPULATION FIRST LEVEL - DEMOGRAPHIC TRANSITION VIEW 83 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 83 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 84 The Intranet of GENIe UPC Computer Services, Josep Xercavins Chapter 77 85 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 85 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 86 Chapter 7 / The Intranet of GENIe 7. The Intranet of GENIe UPC COMPUTER SERVICES, JOSEP XERCAVINS 7.1 GENIe and the UPC Computer Services As previously mentioned, the UNESCO UNITWIN GENIe (Globalproblematique Education Network Initiative) is a network of universities that form part of the UNESCO-UNITWIN Program whose fundamental objective is to provide an integrated multidisciplinary (scientific and humanistic) education, in a university context, on global issues regarding mankind and nature (population, global warming, carrying capacity, famine, poverty etc.) with a view to establishing a sustainable world. A common element of all member groups participating in the network is the use of the same software: GLOBESIGHT (GLOBal forESIGHT), a support and graphic representation tool for setting up future scenarios for the above-mentioned global issues. Since the beginnings of the network, the possibilities provided by ICTs (Information and Communication Technologies), especially the Internet, have been regarded as an indispensable tool. Joint work by lecturers and students including countries from all over the world is only possible in this context. Formulating questions, discussion and the final configuration of future scenarios of global issues is only possible by working online and virtually on the Internet. Physical attendance, obviously, is possible only occasionally, often only once a year. What ultimately made the project viable for the network was the design and use of the ATENEA solution for the GENIe network. 87 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 87 GENIe / Global-problematique Education Network Initiative 88 7.2 The ATENEA Solution: The Intranets of GENIe ATENEA is a UPC technological support for providing ICT services aimed at long-term training, communication and collaboration. Conceptually, ATENEA may be seen as a network of digital discussion forums in which the various virtual communities find the following integrated features: the necessary systems, instruments and tools for their educational and/or collaborative work. The Intranets of GENIe are an example of one of these digital discussion forums. The first Intranet was designed and started up in 1998 to aid the collaborative work of lecturers and students in the UNITWIN GENIe network, with regard to the formulation, discussion and final joint configuration of future scenarios of mankind-nature global issues. We are currently developing the Digital Campus of GENIe, a plethora of several intranets (see Figure 7.1; this screen explains the main features of the Digital Campus). FIGURE 7.1: DIGITAL CAMPUS OF GENIE 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 88 Chapter 7 / The Intranet of GENIe General information about GENIe can be found in the World Wide Web site http://genie.upc.es. From there you can connect the Digital Campus of GENIe, the intranets of GENIe. Access it is only permitted by providing the username and password. 7.3. The Intranet of the First Common Participatory Internet Exercise: Global Warming During the FIRST JOINT PARTICIPATORY EXERCISE future scenarios of CO2 EMISSIONS were developed in order to analyze the possible success or failure in attaining the Kyoto agreements. The first Intranet of GENIe was used as a tool of intercommunication. The main window is the gateway to different types of information. To access the links, you have to click on the icons (see Figure 7.2). •General Information: You will find some generic information about GENIe, GLOBESIGHT, and the GENIe Internet Exercise. Only the GENIe Co-ordination Center can submit new documents to the General Information link. •Exercise’s Processing: You will find information, data, and documents about the First GENIe Common Participatory Internet Exercise on Global Warming. All GENIe participants can submit documents with text and attached files. •Forum: The Forum is a common place to share ideas and point suggestions or comments among the GENIe members, including exercises and other topics of interest. •Mail Communication: This is the link to the GENIe WebMail. This is most useful to announce new entries to other GENIe members. 89 5269 GENIe (tripa) 29/10/2001 18:42 P‡gina 89