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Complex systems analysis and environmental modeling
European Journal of Operational Research 122 (2000) 178±189
www.elsevier.com/locate/orms
Complex systems analysis and environmental modeling Y. Nakamori a
a,*
, Y. Sawaragi
b
Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Tatsunokuti, Ishikawa 923-1292, Japan b Japan Institute of Systems Research, 6 Ushinomiya, Yoshida, Sakyo, Kyoto 606-8302, Japan Received 1 October 1998; accepted 1 April 1999
Abstract Most studies of complex systems take the way of comparing a developed arti®cial world in the computer with the real world and trying to explore the principles of the complex real world. But, this is not enough to understand complex phenomena and make decisions. This paper emphasizes a soft approach that uses both the logic and the educated intuition of people. Our methodology originates in Sawaragi's shinayakana systems approach that is based on Japanese intellectual tradition. This paper introduces our systems methodology together with our trial of applying it to the global environmental problems. Ó 2000 Elsevier Science B.V. All rights reserved. Keywords: Decision support systems; Environment; Modeling; Simulation; Fuzzy sets
1. Introduction In the process of environmental prediction, people usually develop a dynamic or static model using the past time-series data, and evaluate it by the power of explaining the past phenomena. As far as the global environmental problems are concerned, it is almost impossible to gather the full set of time-series data necessary. Even if such a data set as obtained, it will be quite dicult to
* Corresponding author. Tel.: +81-761-51-1755; fax: +81-76451-1149. E-mail addresses: [email protected] (Y. Nakamori), [email protected] (Y. Sawaragi).
construct a model that explains the changes of social and economical structures well. The global environment is a complex system consisting of a variety of cause±eect relations including chaotic elements. A small change in an element spreads over the world with enlarged reproduction. The complex systems approach to the global environmental problems oers a new insight into them by taking a bird's-eye view of complex cause-eect relations. In this paper, we ®rst consider traditional systems thinking related to global environment, then rethink it according to a Japanese traditional philosophy which might aect shinayakana systems approach proposed by one of the authors of this paper, Sawaragi. Then, we will introduce our
0377-2217/00/$ - see front matter Ó 2000 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 7 - 2 2 1 7 ( 9 9 ) 0 0 2 2 7 - 1
Y. Nakamori, Y. Sawaragi / European Journal of Operational Research 122 (2000) 178±189
work to implement this methodology in the ®eld of environmental sciences. It includes developed and developing techniques of modeling and simulation for large-scale, complex systems. 2. Systems methodology 2.1. The earth system Systems thinking related to global environment can be summarized in the following. · The functioning of a system depends on the conditions of its surroundings. The existence of the earth, which is a system providing air, water and food, is needed for people to be alive. The natural and social conditions that help people to employ their physical and intellectual faculties are indispensable. · Functions of a system are determined by its structure. The structure of a system is de®ned by the number and varieties of subsystems, and relations between them. In a system that includes human beings as its elements, subsystems are connected by information. Smooth exchange or ¯ow of information plays an essential role for the system to ful®ll its functions. For a system in which interactions between subsystems are weak, interpretation by the reductionism is acceptable to a great extent. But, for a system which includes human beings as subsystems, it is dicult to estimate interactions between subsystems. · A change at a part of a system aects the entire system. For instance, a very small increase of carbon dioxide in the atmosphere may cause global warming and the rise of the surface of the sea. A system can be de®ned as what has such a character. On earth, the multiplicity of relations between subsystems causes a diversity of diusion. The process of propagation is further complex since the earth system contains the human society as a subsystem. · A system evolves. This aects its surroundings as well as the system itself, and enhances functions of the system. And this is the key to interpret the mechanism of the earth that is changing as time passes. Here, development of a system itself
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does not include value judgment. A system moves into another one with dierent characteristics even when the impact from its surroundings is the same but exceeds a certain limit. Conditions by which human beings can exist on the earth will be lost if impacts by human beings exceed the limit. · A system ¯uctuates. As a human being is a complex system, the number and types of ¯uctuations are large and variable. Behaviors of people are not necessarily the same even if they are in the same environment. Free will of a person is one of the ¯uctuations. Before human activities became active, the ecosystem formed by other animals and plants determined the scenery of the surface of the earth, and the ecosystem was in a quasi-steady state. The expansion of human activities gradually destroyed the quasisteady state, which implies that nature on the earth has changed basically from the quasi-steady state to an unsteady state. In principle, the earth system could be understood by exploring connections between subsystems that exist under the multiplicity of relations. It is, however, almost impossible to construct a model that expresses all system±subsystem relationships, with which all possible problems can be understood. The discipline that discusses the earth based on such a concept has not been fully developed. 2.2. Traditional models The traditional model expressing the relation between nature and human beings follows the belief: · nature consists of several subsystems that are connected to each other by natural mechanisms to which the laws of natural science are applied, · the human system consists also of several subsystems that are connected with each other by human relationships to which the laws of social science are applied, and · the human system does not change the nature system. But, a new model should take into account: · interaction between the human society and nature,
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· interaction inside the society transmitted by nature, and · interaction inside nature transmitted by the society. Environmental science is such a discipline to explore relations between the human society and nature. 2.3. Systems analysis Systems analysis is an approach taking account of the dynamic linkages between phenomena and the hierarchical organization of the natural and social worlds. There is a well-known institute named the International Institute for Applied Systems Analysis in Austria. The former director P.E. de Janosi says in the brochure that tackling complex interdisciplinary problems requires the utilization of sophisticated methodology that adequately takes knowledge from many domains and disciplines and that successfully synthesizes these into a coherent whole. Traditional approaches designed for dealing with issues falling in the purview of a single discipline unfortunately will not do. But, systems analysis has been criticized as inappropriate by a number of systems thinkers. In the book by Flood and Carson [1], systems analysis together with systems engineering and operations research are categorized as hard approaches: The most obvious theme linking the three hard systems approaches is the explicit belief that any problem can be solved by setting objectives and then ®nding from a range of alternatives the one solution that will be optimal in satisfying those objectives. Hard systems methodologies are fundamentally based on means±end analysis. Ends and means can themselves be problematic; for example, where con¯ict in strategies, decisions, and the means of achieving them give rise to a new set of issues to be managed.
The grand target of the Santa Fe Institute for control and application of complex systems, which recently arouses our interest, seems to require for its success the following two assumptions: · there are common characteristics in all complex phenomena, and · these characteristics are reproduced by a simple system with the computer. It is doubtful to succeed dealing with the complexity of the man±environment system under these assumptions. But, there is something worth listening to W.B. Arthur, an economist of the Santa Fe Institute, who also criticizes the costbene®t approach (systems analysis) that this approach assumes well-de®ned problems and alternatives, but it is almost impossible to de®ne the world so well. He continues that if we do not give up the dualism of nature and human beings, we will not able to talk about optimization which is no longer meaningful. This is, however, not a new idea from the viewpoint of Oriental philosophy. 3. Systems thinking in the East 3.1. Oriental systems methodologies During the last several years, systems methodology has drawn signi®cant attention within the systems community in China and Japan. Some of the oriental methodologies are introduced in the following. 1. Meta-synthetic engineering [2]. According to its developers, this is the only feasible methodology for dealing with open complex giant systems; in each there is a large variety of subsystems with hierarchical structures and complex interrelations. Meta-synthetic engineering is claimed to have the following characteristics: · qualitative and quantitative studies are united in such a way that qualitative comprehension is raised to a quantitative one;
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· scienti®c theory and empirical knowledge are combined, and pieces of knowledge of the objective world are collected; · various scienti®c disciplines are studied as a group; · macroscopic and microscopic studies are united; and · applications of meta-synthetic engineering should be supported by a computer system. 2. General systems methodology [3]. This begins with an argument that for many social-problem-solving, the problem themselves are something which have to be studied and de®ned, since objectives are often unclear. While engineering-type problems can be termed wellstructured hard problems, social problems can be termed ill-structured soft problems. General systems methodology is comprised of three related stages and has a particular sub-methodology for each stage: · The ®rst stage involves problem-discovering and -forming, which is need-directed and strategy-oriented. If the outcome of this stage is a well-structured problem, we go to the third stage. · The second stage is called the problem-exploring and -demonstrating stage, which is said to be problem-directed and learning-oriented. · The third stage is the problem-solving stage, which is to be goal-directed and optimization-oriented. 3. Wuli±Shili±Renli methodology [4]. This methodology attempts to reconstruct, and to build itself upon, the Neo-Confucian concepts of general Li (essence of reason) and particular lis (patterns and regularities) and the Confucian teaching of Ge Wu Qiong Li (investigating things for their utmost lis in dierent ways). · This methodology begins with an assumption that humans take action not in a vacuum but conditioned by a dynamic complexity. · This complexity can be viewed as constituted by Wu (objective modeling), Shi (subjective modeling), and Ren (inter-subjective relations). · To take proper action, we need to study and follow regularities or patterns that govern
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and condition Wu, Shi and Ren. These regularities and patterns are called Wuli, Shili, and Renli, respectively. 4. Meta-decision-making methodology [5]. This is itself a dierentiated kind of decision that decides how to make practical decisions. From this viewpoint, decision-makers have a dual identity: as ego-decision-makers they participate in primary decision activities, working with subordinates, making practical decisions; at the same time as ego-meta-decision-makers the most dicult task for decision-makers in the meta-decision-making process, it is maintained, is to know themselves well and to regulate themselves eectively. There are two major tasks in the methodology: · The ®rst is to select the practical decision style suitable to the concerns and qualities of participants: those styles can be autocratic, consultative, group sharing, delegative, and so forth. · The second task is to design the practical decision process, which in turn may generally be materialized into three sub-phases: an opportunity-identi®cation phase, a solution-development phase, and a solution-selection phase. 3.2. Japanese intellectual tradition There exists some Japanese approach to knowledge that integrates the teachings of Buddhism, Confucianism, and major Western philosophical thoughts. According to Nonaka and Takeuchi [6], there are three distinctions of the Japanese intellectual tradition: 1. Oneness of humanity and nature. While Japanese epistemology has nurtured a delicate and sophisticated sensitivity to nature, it has prevented the objecti®cation of nature and the development of sound skepticism. The Japanese had failed to build up a rational thought of clear universality, because they did not succeed in the separation and objecti®cation of self and nature. 2. Oneness of body and mind. Another important intellectual tradition of Japan is the emphasis on the whole personality as opposed to the
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Western sense of knowledge, which is separated from human philosophical and epistemological development. For the Japanese, knowledge means wisdom that is acquired from the perspective of the entire personality. This orientation has provided a basis for valuing personal and physical experience over indirect, intellectual abstraction. 3. Oneness of self and other. While most Western views of human relationships are atomistic and mechanistic, the Japanese view is collective and organic. It is within this context of an organic world view that the Japanese emphasize subjective knowledge and intuitive intelligence. 3.3. Shinayakana systems approach One of the authors of this paper, Sawaragi, has proposed the shinayakana systems approach [7], where, the adjective shinayakana has a nuance of Japanese characteristics, and means in English between soft and hard, or both; a close expression may be ¯exible and elastic. This systems approach is based on the above-mentioned Japanese intellectual tradition, and takes into account: · the limitation of our ability to objectify the real world, · the limitation of our ability to understand indirect observation, and · the limitation of our ability to analyze things objectively. Shinayakana systems methodology stresses the adaptive learning and stimulation of intuition and creativity of people. One of the main tasks of systems analysts is to develop decision or thinking support systems that provide system models and system methods with which people can make decisions taking into account social aspects or human relations. Fig. 1 illustrates a conceptual plan of the shinayakana approach. This methodology includes the following approaches: 1. Perception of systems multiplicity. The total system is inseparable. But, an ordinary person cannot perceive the inseparable whole. Let us cut o, for a while, weak links and consider individual subsystems that people can well imagine.
Fig. 1. Shinayakana systems approach.
But, never forget that each element can be included in plural subsystems, and that those subsystems are interrelated with each other for this reason. 2. Nonlinear reductionism. The criticism of reductionism is, to put it more precisely, the criticism of the assumptions of linear independence between elements and the principle of superposition. The reduction itself is not blameworthy at all. What we are looking for is not a mysterious wholism but a logical method to explore the total characteristics based on the study of elements and their relationships. This approach requires a schema of nonlinear integration. 3. Integrated assessment. During the past few years, a new approach integrated assessment has found increasing popularity. As is the case with systems analysis, integrated assessment is not well de®ned and covers a multitude of methods and concepts. But here, integrated assessment includes mathematical methods for nonlinear integration as well as information techniques for integration of models or knowledge. Often, integrated assessment requires educated intuition of the analyst. 4. Expanding and deepening one's insight into the system. A classic technique insight will be rebirth in connection with advanced technology such as simulation, virtual reality, etc. The task here includes development of support systems to expand and deepen people's insight into present and future situations.
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The shinayakana systems approach never makes light of mathematical methods and models, but limits them to playing the role of problem solving support only. We should always keep it in mind that any precise models of reality would never incorporate all human concerns. Therefore, an essential part of problem solving is the issue of human±computer interaction. Models should be built interactively, involving not only analysts but also domain experts and decision-makers. Their perceptions of the problem, relevant data and model validity should be taken into account in model building so that the model can express their goals and preferences de®nitely and correctly. Interaction is essential at the decision stage as well, and it should be dynamical because human decision-makers typically learn when using a decision support system. In order to make good use of interaction, the support system must be intelligent. A problem solving support system should have a working area in a knowledge-based subsystem. Frameworks of dynamical knowledge utilization should be designed so that we cannot only retrieve data or knowledge, but also acquire or modify them interactively. At the modeling stage, a model is identi®ed partly and stepwise associated with mental models for the object and the knowledge in the support system. The registered knowledge for modeling support can be improved both in quality and quantity by the results of data analysis or by the users' perceptions. At the decision stage, the knowledge-based system should suggest the objective of optimization or the order of priority in constraints. New knowledge can be obtained by considering the gaps between the target and actual plan, or the feasibility and eects of the plan. It is a notable fact that the existence of human beings is very important to all systems, though human beings give them vagueness and ambiguity. Therefore, in modeling the reality we have to think of a system with human beings as its center. Systems engineers should play coordinators or integrators to help people in solving their problems. The main feature of shinayakana systems approach lies in intervention of human beings at every phase of the problem solving.
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Here, it is very important to understand the role of intuition in decision making. Wierzbicki [8] de®nes intuitive decisions as quasi-conscious and subconscious information processing, leading to an action, utilizing aggregated experience and training and performed by a specialized part of the human mind. The shinayakana systems approach aims at supporting various phases of an intuitive decision process directly or indirectly. Wierzbicki [8] also emphasizes that to provide indirect support to intuitive decisions, it is clear that we must concentrate more on the phases of intelligence and of design and analysis, preceding the phase of choice. This is exactly the objective of shinayakana systems approach. 4. Implementation The framework of the total model-based analysis and decision support is illustrated in Fig. 2. Some examples of models for prediction and evaluation will be introduced in this section. The principle in modeling and simulation in our approach is the nonlinear reductionism. 4.1. Prediction models We prefer the fuzzy model which is a nonlinear model consisting of a number of rule-based linear models and membership functions that determine
Fig. 2. Model-based analysis and decision support.
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the degree of con®dence of the rules. For instance, we have generated fuzzy rules to predict ozone concentration at a receptor in Europe: Rule i: If the photolysis rate of nitrogen dioxide at the receptor is, for instance, small (a fuzzy subset), the eective NOx emission from the country including the receptor is medium, and the total eective NOx emission from other countries is small, then the ozone concentration is given by the output from a linear function of the total eective NOx and the total eective VOC from all countries. Here, NOx and VOX mean the nitrogen oxides and the volatile organic compounds, respectively, which are ozone precursors. This fuzzy model can be used for prediction of ozone concentration at the receptor provided that the values of the photolysis rate at the receptor and the eective NOx and VOC emissions from all counties are given: The ozone concentration is the sum of (the relative con®dence of Rule i) times (the output from Rule i). Fuzzy modeling has some interdependent subproblems such as fuzzy partition of the data space, identi®cation of membership functions and linear models. To obtain a satisfactory model from among an unlimited number of combinations, we should decide two things in advance: which sphere we will examine and how we obtain a satisfactory result. It is generally a good incentive for us to determine a criterion and a searching algorithm because it is logically satisfying to accept a solution found by them. This is also a big temptation in building a fuzzy model. The non-interactive algorithms have been developed by many researchers, but it is theoretically impossible to come across an ideal model by a non-interactive approach only. Unless we use educated and informed intuition to ®nd a way to the goal, it is dicult to obtain a convincing model that can be used in an actual situation.
The fuzzy sets theory does not merely provide interpolation techniques to analyze nonlinear systems. It is something to join logic and intuition together. A modeling algorithm should have a strategy for increasing the chances of ®nding a better model through the modeler's judgment. In this respect, an interactive approach with computer assistance [9] is recommended, where the theme is how to analyze data in order to summarize them to a certain level at which we can understand the nature of the data. The main technical proposal in Nakamori and Ryoke [10] is a clustering algorithm that will search fuzzy subsets based on our desire about their shapes, where interaction is very important in creating a balance between continuity and linearity of the data distribution within clusters. After developing a number of fuzzy subsets, we identify linear substructures of the system under study. The second technical proposal is related to the integration of rules: selection of conditional variables, identi®cation of membership functions and veri®cation of a fuzzy model. 4.2. Evaluation models A number of environmental indices have been developed by some local governments in Japan from a part of the environmental management planning. The background of this activity can be summarized as follows: · Preparation of environmental information. Evaluation indices are indispensable when trying to take a broad view of the environment by properly summarizing a large quantity of information. · Objective prediction of the environment. Tools for predicting the environment quantitatively are necessary because the so-called predictive and comprehensive polices have been required. · Subjective evaluation of the environment. Interests in environmental policies have been extended from pollution to comfort, and further, to local individualities. Broad criteria to evaluate them are required.
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It should be noted that to evaluate items independently and to determine weights of items independently are dierent things. When identifying coecients of a linear model based on data obtained by a questionnaire survey, interdependent coecients are determined due to the existence of correlation between data vectors, even though items are evaluated independently. In this sense, it is dangerous to think of weights as a lot more than partial charges in estimating the comprehensive evaluation by a model. In Nakamori [11], the structure of subjective environmental evaluation by residents is analyzed by using the Choquet integral model with fuzzy measures. It is an extension of the Lebesgue integral model and can be used as an evaluation model when the additivity in weights is relaxed. The model is identi®ed by introducing composite items explicitly and by the convex quadratic programming. Therefore, similar to the linear model, the weights are partial charges in the assumed model. Nevertheless, this model provides useful information compared with the ordinary linear regression model. 4.3. A model of models The global environmental problem has become a big subject of international politics, as a new ®eld lying between natural science and modern society. There are too many models and opinions based on unreliable information, and too many proposals based on individual national strategies. It is necessary to build a platform between policy makers and researchers to ®nd a reasonable path to sustainable development. We are developing a global environment framework model [12] as such a platform. The objectives of the framework model are: · to explain complex information to decision makers simply; · to identify each research topic, and to show how it is related to other topics; · to explain which issues are more important; and · to show which is the most important uncertainty to be reduced. To achieve these objectives, the following systems or methods are considered:
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· a matrix system which describes the most recent integrated knowledge; · a knowledge-based system which stores the relevant information; · a method based on sensitivity analysis to ®nd serious environmental issues; and · a method based on the fuzzy sets theory to identify items of uncertainty to reduce. Fig. 3 is the environmental framework model of Japan. This model is very simple, but, at the same time, very complex, because to determine each coecient of matrices requires a lot of work, a lot of discussions with relevant researchers. Our ®nal goal is to build a world framework model, but now, we are developing a model of Japan. The subsystems correspond to the following matrices: 1. Basic production matrix. Food production, industrial production, and services are required for human development which is measured by population and GDP/capita. 2. Production factor matrix. Labor, capital, land, natural resource, and energy are required for basic production. 3. Waste matrix. Solid, liquid, and gaseous wastes are produced by consumption of natural resources, energy, etc. 4. Measure matrix. The cost-dependent technical, economic, and social measures are introduced here to reduce the emissions of wastes. 5. Environmental change matrix. This treats environmental changes caused by land use, resource consumption, and waste emission. Environmental changes considered here are air pollution, water pollution, toxic waste, soil degradation, deforestation, extinction, and water resource. 6. Interaction matrix. We consider interdependence between the environmental conditions. 7. Environmental impact matrix. This describes various impacts of the total environmental change on human activity and welfare. 8. Adjustment matrix. The human development is adjusted to the environmental changes and impacts. The outputs from the model are adjusted population and adjusted GDP/capita which is called Green GDP/capita.
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Fig. 3. Environment framework model of Japan.
4.4. Model integration There exist a lot of models and measurement data in the world. It is, however, dicult to access them from the outside. Models are being developed independently so that only limited problems can be investigated with individual models. Integration of such models enables us to predict the global environment and consider policy options totally. We are designing a system that helps to have several models in common between many researchers who wish to integrate many models, or compares models of the same purpose, or discuss uncertainties with other researchers. To have a variety of models on the computer network, a technique called the remote object is used, which
enables us to access objects in the network. Here, adapters should be prepared at respective stages so that we easily have several models in common. The task to integrate models on the network includes: 1. analysis of model classes, 2. extraction of common methods to use models by object-oriented analysis, and 3. connection of existing models or data by a transfer server. The system receives data through the input element class, carries out simulation by using methods stored inside the system, and passes data to other models or display systems through the output element class. In the root class of environmental models, if the lack of input or output is found, the environmental model it-
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self sends inquiry to the server about lacked data. A set of environmental models connected on the network is called a model ¯ow. Theoretically, a model ¯ow is a large global view that connects all possible models on the network, and is regarded as a large database. If a model ¯ow is too large to understand, a local view, that is, a partial network, is created in response to the user. Fig. 4 shows an example of generation of a model ¯ow. For the general use of environmental models, some common methods are extracted by analyzing common operations among models, and interface classes of models are determined. This oers a uniform framework to use common methods from applications regardless of contents of models. A rough classi®cation of model classes could include the abstract model class, qualitative model class, and numerical model class. A variety of models are de®ned by the same method and used as actual models by labeling input±output values and tuning inner parameters. This can be realized by the method of generating an environmental model in such a way that an input±output class is de®ned in some abstract models and inner parameters are added to them. It is possible to omit to create an environmental model if such an abstract model class is prepared. Compatibility of the semantic contents between dierent models is indispensable. A class is oered
for basic input±output, and the creation of operating sub-classes is put into the user's hands. It is possible that dierent models have the same input± output class. A method dealing with such a case should be considered in an actual implementation.
Fig. 4. A model ¯ow by automatic integration.
Fig. 5. Decision support systems.
4.5. Decision support systems One of the main tasks in our approach is to develop decision support systems with computer assistance. Fig. 5 illustrates our ®nal goal. The evaluation of a decision support system could be made by using the following criteria [13]: 1. Background of analysts. Analytical systems studies have always focused on concrete problems, with a dominant role being played by the general mathematical background of the analysts. They should be able to not only understand clearly the content of the problems, but also to formulate them in a form that is analyzable, using mathematical, computational, and other skills. 2. Principle of practicality. Systems science is a technology to enrich human living by the application of science. From this point of view, like other technologies, on the one hand universality is required, whilst on the other hand practicality is stressed. Therefore, systems science is distinguished from mathematics, logic, or phys-
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ics, for it is a synthetic technology based on all of these sciences and other adopted technologies. However, the small possibility of producing counter evidence is allowed to enhance the principle of practicality. 3. Veri®ability of assumptions. Systems science is a problem-oriented synthetic technology developed for solving complex, large-scale problems, which incorporate uncertainty, and are often beyond the understanding of the systems developer in relation to their complete structure. Therefore, in developing a decision support system, eort should be directed to visualization of the information, which is well-matched with the human ability of sense or perception. And thereby in using a decision support system the models structure and assumptions should be cleared, in order to guarantee veri®ability. 4. Participation of users. As methods or models are becoming larger and more complex, they are inevitably extending beyond the understanding of many users. But, the user's participation in decision-making is quite important in building the models which are used in the decision support system, for otherwise the people who are required to use the developed system may reject its use. 5. Usefulness of information. Because the process of design necessarily involves numerous steps, the ®nal product of a designed decision support system, as well as the results emerging from its use, can never be formally proved to have been the best possible option or design. The major justi®cation of goodness is satisfactory quality, and the usefulness of information the system provides for the users. 5. Concluding remarks This paper presented our idea on systems thinking concerning the global environment, and our approach to dealing with complexity. There are some open questions to develop more useful systems methodologies: 1. In the modeling of complex, large-scale systems, especially, regional or global environment systems, how can we use the soft systems meth-
odologies by combining hard, mathematical approaches? Or, how can we support the judgment based on the insight of decision-makers? 2. In addition to the soft±hard axis, the detailrough axis is important because people want to understand the environmental situation easily, but at the same time, they want to know it correctly. How can we make a balance between detail and rough as well as soft and hard? 3. The role of the computer network in analyzing complex systems is another problem we would like to study. How can we integrate important knowledge, models or data by means of the computer network technology for the creation of future global environment? References [1] R.L. Flood, E.R. Carson, Dealing with Complexity, Plenum Press, New York, 1993. [2] X. Qian, J. Yu, R. Dai, A new discipline of science: The study of open complex giant system and its methodology, Chinese Journal of Systems Engineering and Electronics 3 (2) (1993) 2±12. [3] X. Li, H. Zheng, Study on general systems methodology, in: G. Midgley, J. Wilby (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1995, pp. 49±56. [4] J. Gu, Z. Zhu, Knowing Wuli, sensing Shili, caring Renli: Methodology of the WSR approach, Systems Practice 10, in press. [5] Z. Wang, Meta-decision making, in: G. Midgley, J. Wilby (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1995, pp. 87±92. [6] I. Nonaka, H. Takeuchi, The Knowledge-Creating Company, Oxford University Press, Oxford, 1995. [7] Y. Sawaragi, The shinayakana systems approach as a solving tool for complex systems, in: J. Wilby, A. Zhu (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1997, pp. 7,8. [8] A.P. Wierzbicki, On the role of intuition in decision making and some ways of multicriteria aid of intuition, Journal of Multi-Criteria Decision Analysis 6 (1997) 65±76. [9] Y. Nakamori, Development and Application of an Interactive Modeling Support System. AUTOMATICA, International Federation of Automatic Control, vol. 25, Pergamon Press, Oxford, 1989, pp. 185±206. [10] Y. Nakamori, M. Ryoke, Identi®cation of fuzzy prediction models through hyperellipsoidal clustering, IEEE Transations on Systems Man and Cybernetics 24 (1994) 1153± 1173.
Y. Nakamori, Y. Sawaragi / European Journal of Operational Research 122 (2000) 178±189 [11] Y. Nakamori, Human evaluation models for urban environment, Proceedings of the IFAC96, vol. L, San Francisco, USA, 30 June±5 July, 1996, pp. 97±102. [12] Y. Nakamori, T. Kusube, T. Morita, Global environment framework model ± objective and possibility, Proceedings of the 1996 IEEE International Conference on Systems,
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Man and Cybernetics, 14±17 October, Beijing, China, 1996, pp. 2601±2606. [13] Y. Nakamori, Y. Sawaragi, Methodology and systems for environmental decision support, Annual Reviews in Control 20 (1997) 143±154.
www.elsevier.com/locate/orms
Complex systems analysis and environmental modeling Y. Nakamori a
a,*
, Y. Sawaragi
b
Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Tatsunokuti, Ishikawa 923-1292, Japan b Japan Institute of Systems Research, 6 Ushinomiya, Yoshida, Sakyo, Kyoto 606-8302, Japan Received 1 October 1998; accepted 1 April 1999
Abstract Most studies of complex systems take the way of comparing a developed arti®cial world in the computer with the real world and trying to explore the principles of the complex real world. But, this is not enough to understand complex phenomena and make decisions. This paper emphasizes a soft approach that uses both the logic and the educated intuition of people. Our methodology originates in Sawaragi's shinayakana systems approach that is based on Japanese intellectual tradition. This paper introduces our systems methodology together with our trial of applying it to the global environmental problems. Ó 2000 Elsevier Science B.V. All rights reserved. Keywords: Decision support systems; Environment; Modeling; Simulation; Fuzzy sets
1. Introduction In the process of environmental prediction, people usually develop a dynamic or static model using the past time-series data, and evaluate it by the power of explaining the past phenomena. As far as the global environmental problems are concerned, it is almost impossible to gather the full set of time-series data necessary. Even if such a data set as obtained, it will be quite dicult to
* Corresponding author. Tel.: +81-761-51-1755; fax: +81-76451-1149. E-mail addresses: [email protected] (Y. Nakamori), [email protected] (Y. Sawaragi).
construct a model that explains the changes of social and economical structures well. The global environment is a complex system consisting of a variety of cause±eect relations including chaotic elements. A small change in an element spreads over the world with enlarged reproduction. The complex systems approach to the global environmental problems oers a new insight into them by taking a bird's-eye view of complex cause-eect relations. In this paper, we ®rst consider traditional systems thinking related to global environment, then rethink it according to a Japanese traditional philosophy which might aect shinayakana systems approach proposed by one of the authors of this paper, Sawaragi. Then, we will introduce our
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work to implement this methodology in the ®eld of environmental sciences. It includes developed and developing techniques of modeling and simulation for large-scale, complex systems. 2. Systems methodology 2.1. The earth system Systems thinking related to global environment can be summarized in the following. · The functioning of a system depends on the conditions of its surroundings. The existence of the earth, which is a system providing air, water and food, is needed for people to be alive. The natural and social conditions that help people to employ their physical and intellectual faculties are indispensable. · Functions of a system are determined by its structure. The structure of a system is de®ned by the number and varieties of subsystems, and relations between them. In a system that includes human beings as its elements, subsystems are connected by information. Smooth exchange or ¯ow of information plays an essential role for the system to ful®ll its functions. For a system in which interactions between subsystems are weak, interpretation by the reductionism is acceptable to a great extent. But, for a system which includes human beings as subsystems, it is dicult to estimate interactions between subsystems. · A change at a part of a system aects the entire system. For instance, a very small increase of carbon dioxide in the atmosphere may cause global warming and the rise of the surface of the sea. A system can be de®ned as what has such a character. On earth, the multiplicity of relations between subsystems causes a diversity of diusion. The process of propagation is further complex since the earth system contains the human society as a subsystem. · A system evolves. This aects its surroundings as well as the system itself, and enhances functions of the system. And this is the key to interpret the mechanism of the earth that is changing as time passes. Here, development of a system itself
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does not include value judgment. A system moves into another one with dierent characteristics even when the impact from its surroundings is the same but exceeds a certain limit. Conditions by which human beings can exist on the earth will be lost if impacts by human beings exceed the limit. · A system ¯uctuates. As a human being is a complex system, the number and types of ¯uctuations are large and variable. Behaviors of people are not necessarily the same even if they are in the same environment. Free will of a person is one of the ¯uctuations. Before human activities became active, the ecosystem formed by other animals and plants determined the scenery of the surface of the earth, and the ecosystem was in a quasi-steady state. The expansion of human activities gradually destroyed the quasisteady state, which implies that nature on the earth has changed basically from the quasi-steady state to an unsteady state. In principle, the earth system could be understood by exploring connections between subsystems that exist under the multiplicity of relations. It is, however, almost impossible to construct a model that expresses all system±subsystem relationships, with which all possible problems can be understood. The discipline that discusses the earth based on such a concept has not been fully developed. 2.2. Traditional models The traditional model expressing the relation between nature and human beings follows the belief: · nature consists of several subsystems that are connected to each other by natural mechanisms to which the laws of natural science are applied, · the human system consists also of several subsystems that are connected with each other by human relationships to which the laws of social science are applied, and · the human system does not change the nature system. But, a new model should take into account: · interaction between the human society and nature,
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· interaction inside the society transmitted by nature, and · interaction inside nature transmitted by the society. Environmental science is such a discipline to explore relations between the human society and nature. 2.3. Systems analysis Systems analysis is an approach taking account of the dynamic linkages between phenomena and the hierarchical organization of the natural and social worlds. There is a well-known institute named the International Institute for Applied Systems Analysis in Austria. The former director P.E. de Janosi says in the brochure that tackling complex interdisciplinary problems requires the utilization of sophisticated methodology that adequately takes knowledge from many domains and disciplines and that successfully synthesizes these into a coherent whole. Traditional approaches designed for dealing with issues falling in the purview of a single discipline unfortunately will not do. But, systems analysis has been criticized as inappropriate by a number of systems thinkers. In the book by Flood and Carson [1], systems analysis together with systems engineering and operations research are categorized as hard approaches: The most obvious theme linking the three hard systems approaches is the explicit belief that any problem can be solved by setting objectives and then ®nding from a range of alternatives the one solution that will be optimal in satisfying those objectives. Hard systems methodologies are fundamentally based on means±end analysis. Ends and means can themselves be problematic; for example, where con¯ict in strategies, decisions, and the means of achieving them give rise to a new set of issues to be managed.
The grand target of the Santa Fe Institute for control and application of complex systems, which recently arouses our interest, seems to require for its success the following two assumptions: · there are common characteristics in all complex phenomena, and · these characteristics are reproduced by a simple system with the computer. It is doubtful to succeed dealing with the complexity of the man±environment system under these assumptions. But, there is something worth listening to W.B. Arthur, an economist of the Santa Fe Institute, who also criticizes the costbene®t approach (systems analysis) that this approach assumes well-de®ned problems and alternatives, but it is almost impossible to de®ne the world so well. He continues that if we do not give up the dualism of nature and human beings, we will not able to talk about optimization which is no longer meaningful. This is, however, not a new idea from the viewpoint of Oriental philosophy. 3. Systems thinking in the East 3.1. Oriental systems methodologies During the last several years, systems methodology has drawn signi®cant attention within the systems community in China and Japan. Some of the oriental methodologies are introduced in the following. 1. Meta-synthetic engineering [2]. According to its developers, this is the only feasible methodology for dealing with open complex giant systems; in each there is a large variety of subsystems with hierarchical structures and complex interrelations. Meta-synthetic engineering is claimed to have the following characteristics: · qualitative and quantitative studies are united in such a way that qualitative comprehension is raised to a quantitative one;
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· scienti®c theory and empirical knowledge are combined, and pieces of knowledge of the objective world are collected; · various scienti®c disciplines are studied as a group; · macroscopic and microscopic studies are united; and · applications of meta-synthetic engineering should be supported by a computer system. 2. General systems methodology [3]. This begins with an argument that for many social-problem-solving, the problem themselves are something which have to be studied and de®ned, since objectives are often unclear. While engineering-type problems can be termed wellstructured hard problems, social problems can be termed ill-structured soft problems. General systems methodology is comprised of three related stages and has a particular sub-methodology for each stage: · The ®rst stage involves problem-discovering and -forming, which is need-directed and strategy-oriented. If the outcome of this stage is a well-structured problem, we go to the third stage. · The second stage is called the problem-exploring and -demonstrating stage, which is said to be problem-directed and learning-oriented. · The third stage is the problem-solving stage, which is to be goal-directed and optimization-oriented. 3. Wuli±Shili±Renli methodology [4]. This methodology attempts to reconstruct, and to build itself upon, the Neo-Confucian concepts of general Li (essence of reason) and particular lis (patterns and regularities) and the Confucian teaching of Ge Wu Qiong Li (investigating things for their utmost lis in dierent ways). · This methodology begins with an assumption that humans take action not in a vacuum but conditioned by a dynamic complexity. · This complexity can be viewed as constituted by Wu (objective modeling), Shi (subjective modeling), and Ren (inter-subjective relations). · To take proper action, we need to study and follow regularities or patterns that govern
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and condition Wu, Shi and Ren. These regularities and patterns are called Wuli, Shili, and Renli, respectively. 4. Meta-decision-making methodology [5]. This is itself a dierentiated kind of decision that decides how to make practical decisions. From this viewpoint, decision-makers have a dual identity: as ego-decision-makers they participate in primary decision activities, working with subordinates, making practical decisions; at the same time as ego-meta-decision-makers the most dicult task for decision-makers in the meta-decision-making process, it is maintained, is to know themselves well and to regulate themselves eectively. There are two major tasks in the methodology: · The ®rst is to select the practical decision style suitable to the concerns and qualities of participants: those styles can be autocratic, consultative, group sharing, delegative, and so forth. · The second task is to design the practical decision process, which in turn may generally be materialized into three sub-phases: an opportunity-identi®cation phase, a solution-development phase, and a solution-selection phase. 3.2. Japanese intellectual tradition There exists some Japanese approach to knowledge that integrates the teachings of Buddhism, Confucianism, and major Western philosophical thoughts. According to Nonaka and Takeuchi [6], there are three distinctions of the Japanese intellectual tradition: 1. Oneness of humanity and nature. While Japanese epistemology has nurtured a delicate and sophisticated sensitivity to nature, it has prevented the objecti®cation of nature and the development of sound skepticism. The Japanese had failed to build up a rational thought of clear universality, because they did not succeed in the separation and objecti®cation of self and nature. 2. Oneness of body and mind. Another important intellectual tradition of Japan is the emphasis on the whole personality as opposed to the
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Western sense of knowledge, which is separated from human philosophical and epistemological development. For the Japanese, knowledge means wisdom that is acquired from the perspective of the entire personality. This orientation has provided a basis for valuing personal and physical experience over indirect, intellectual abstraction. 3. Oneness of self and other. While most Western views of human relationships are atomistic and mechanistic, the Japanese view is collective and organic. It is within this context of an organic world view that the Japanese emphasize subjective knowledge and intuitive intelligence. 3.3. Shinayakana systems approach One of the authors of this paper, Sawaragi, has proposed the shinayakana systems approach [7], where, the adjective shinayakana has a nuance of Japanese characteristics, and means in English between soft and hard, or both; a close expression may be ¯exible and elastic. This systems approach is based on the above-mentioned Japanese intellectual tradition, and takes into account: · the limitation of our ability to objectify the real world, · the limitation of our ability to understand indirect observation, and · the limitation of our ability to analyze things objectively. Shinayakana systems methodology stresses the adaptive learning and stimulation of intuition and creativity of people. One of the main tasks of systems analysts is to develop decision or thinking support systems that provide system models and system methods with which people can make decisions taking into account social aspects or human relations. Fig. 1 illustrates a conceptual plan of the shinayakana approach. This methodology includes the following approaches: 1. Perception of systems multiplicity. The total system is inseparable. But, an ordinary person cannot perceive the inseparable whole. Let us cut o, for a while, weak links and consider individual subsystems that people can well imagine.
Fig. 1. Shinayakana systems approach.
But, never forget that each element can be included in plural subsystems, and that those subsystems are interrelated with each other for this reason. 2. Nonlinear reductionism. The criticism of reductionism is, to put it more precisely, the criticism of the assumptions of linear independence between elements and the principle of superposition. The reduction itself is not blameworthy at all. What we are looking for is not a mysterious wholism but a logical method to explore the total characteristics based on the study of elements and their relationships. This approach requires a schema of nonlinear integration. 3. Integrated assessment. During the past few years, a new approach integrated assessment has found increasing popularity. As is the case with systems analysis, integrated assessment is not well de®ned and covers a multitude of methods and concepts. But here, integrated assessment includes mathematical methods for nonlinear integration as well as information techniques for integration of models or knowledge. Often, integrated assessment requires educated intuition of the analyst. 4. Expanding and deepening one's insight into the system. A classic technique insight will be rebirth in connection with advanced technology such as simulation, virtual reality, etc. The task here includes development of support systems to expand and deepen people's insight into present and future situations.
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The shinayakana systems approach never makes light of mathematical methods and models, but limits them to playing the role of problem solving support only. We should always keep it in mind that any precise models of reality would never incorporate all human concerns. Therefore, an essential part of problem solving is the issue of human±computer interaction. Models should be built interactively, involving not only analysts but also domain experts and decision-makers. Their perceptions of the problem, relevant data and model validity should be taken into account in model building so that the model can express their goals and preferences de®nitely and correctly. Interaction is essential at the decision stage as well, and it should be dynamical because human decision-makers typically learn when using a decision support system. In order to make good use of interaction, the support system must be intelligent. A problem solving support system should have a working area in a knowledge-based subsystem. Frameworks of dynamical knowledge utilization should be designed so that we cannot only retrieve data or knowledge, but also acquire or modify them interactively. At the modeling stage, a model is identi®ed partly and stepwise associated with mental models for the object and the knowledge in the support system. The registered knowledge for modeling support can be improved both in quality and quantity by the results of data analysis or by the users' perceptions. At the decision stage, the knowledge-based system should suggest the objective of optimization or the order of priority in constraints. New knowledge can be obtained by considering the gaps between the target and actual plan, or the feasibility and eects of the plan. It is a notable fact that the existence of human beings is very important to all systems, though human beings give them vagueness and ambiguity. Therefore, in modeling the reality we have to think of a system with human beings as its center. Systems engineers should play coordinators or integrators to help people in solving their problems. The main feature of shinayakana systems approach lies in intervention of human beings at every phase of the problem solving.
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Here, it is very important to understand the role of intuition in decision making. Wierzbicki [8] de®nes intuitive decisions as quasi-conscious and subconscious information processing, leading to an action, utilizing aggregated experience and training and performed by a specialized part of the human mind. The shinayakana systems approach aims at supporting various phases of an intuitive decision process directly or indirectly. Wierzbicki [8] also emphasizes that to provide indirect support to intuitive decisions, it is clear that we must concentrate more on the phases of intelligence and of design and analysis, preceding the phase of choice. This is exactly the objective of shinayakana systems approach. 4. Implementation The framework of the total model-based analysis and decision support is illustrated in Fig. 2. Some examples of models for prediction and evaluation will be introduced in this section. The principle in modeling and simulation in our approach is the nonlinear reductionism. 4.1. Prediction models We prefer the fuzzy model which is a nonlinear model consisting of a number of rule-based linear models and membership functions that determine
Fig. 2. Model-based analysis and decision support.
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the degree of con®dence of the rules. For instance, we have generated fuzzy rules to predict ozone concentration at a receptor in Europe: Rule i: If the photolysis rate of nitrogen dioxide at the receptor is, for instance, small (a fuzzy subset), the eective NOx emission from the country including the receptor is medium, and the total eective NOx emission from other countries is small, then the ozone concentration is given by the output from a linear function of the total eective NOx and the total eective VOC from all countries. Here, NOx and VOX mean the nitrogen oxides and the volatile organic compounds, respectively, which are ozone precursors. This fuzzy model can be used for prediction of ozone concentration at the receptor provided that the values of the photolysis rate at the receptor and the eective NOx and VOC emissions from all counties are given: The ozone concentration is the sum of (the relative con®dence of Rule i) times (the output from Rule i). Fuzzy modeling has some interdependent subproblems such as fuzzy partition of the data space, identi®cation of membership functions and linear models. To obtain a satisfactory model from among an unlimited number of combinations, we should decide two things in advance: which sphere we will examine and how we obtain a satisfactory result. It is generally a good incentive for us to determine a criterion and a searching algorithm because it is logically satisfying to accept a solution found by them. This is also a big temptation in building a fuzzy model. The non-interactive algorithms have been developed by many researchers, but it is theoretically impossible to come across an ideal model by a non-interactive approach only. Unless we use educated and informed intuition to ®nd a way to the goal, it is dicult to obtain a convincing model that can be used in an actual situation.
The fuzzy sets theory does not merely provide interpolation techniques to analyze nonlinear systems. It is something to join logic and intuition together. A modeling algorithm should have a strategy for increasing the chances of ®nding a better model through the modeler's judgment. In this respect, an interactive approach with computer assistance [9] is recommended, where the theme is how to analyze data in order to summarize them to a certain level at which we can understand the nature of the data. The main technical proposal in Nakamori and Ryoke [10] is a clustering algorithm that will search fuzzy subsets based on our desire about their shapes, where interaction is very important in creating a balance between continuity and linearity of the data distribution within clusters. After developing a number of fuzzy subsets, we identify linear substructures of the system under study. The second technical proposal is related to the integration of rules: selection of conditional variables, identi®cation of membership functions and veri®cation of a fuzzy model. 4.2. Evaluation models A number of environmental indices have been developed by some local governments in Japan from a part of the environmental management planning. The background of this activity can be summarized as follows: · Preparation of environmental information. Evaluation indices are indispensable when trying to take a broad view of the environment by properly summarizing a large quantity of information. · Objective prediction of the environment. Tools for predicting the environment quantitatively are necessary because the so-called predictive and comprehensive polices have been required. · Subjective evaluation of the environment. Interests in environmental policies have been extended from pollution to comfort, and further, to local individualities. Broad criteria to evaluate them are required.
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It should be noted that to evaluate items independently and to determine weights of items independently are dierent things. When identifying coecients of a linear model based on data obtained by a questionnaire survey, interdependent coecients are determined due to the existence of correlation between data vectors, even though items are evaluated independently. In this sense, it is dangerous to think of weights as a lot more than partial charges in estimating the comprehensive evaluation by a model. In Nakamori [11], the structure of subjective environmental evaluation by residents is analyzed by using the Choquet integral model with fuzzy measures. It is an extension of the Lebesgue integral model and can be used as an evaluation model when the additivity in weights is relaxed. The model is identi®ed by introducing composite items explicitly and by the convex quadratic programming. Therefore, similar to the linear model, the weights are partial charges in the assumed model. Nevertheless, this model provides useful information compared with the ordinary linear regression model. 4.3. A model of models The global environmental problem has become a big subject of international politics, as a new ®eld lying between natural science and modern society. There are too many models and opinions based on unreliable information, and too many proposals based on individual national strategies. It is necessary to build a platform between policy makers and researchers to ®nd a reasonable path to sustainable development. We are developing a global environment framework model [12] as such a platform. The objectives of the framework model are: · to explain complex information to decision makers simply; · to identify each research topic, and to show how it is related to other topics; · to explain which issues are more important; and · to show which is the most important uncertainty to be reduced. To achieve these objectives, the following systems or methods are considered:
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· a matrix system which describes the most recent integrated knowledge; · a knowledge-based system which stores the relevant information; · a method based on sensitivity analysis to ®nd serious environmental issues; and · a method based on the fuzzy sets theory to identify items of uncertainty to reduce. Fig. 3 is the environmental framework model of Japan. This model is very simple, but, at the same time, very complex, because to determine each coecient of matrices requires a lot of work, a lot of discussions with relevant researchers. Our ®nal goal is to build a world framework model, but now, we are developing a model of Japan. The subsystems correspond to the following matrices: 1. Basic production matrix. Food production, industrial production, and services are required for human development which is measured by population and GDP/capita. 2. Production factor matrix. Labor, capital, land, natural resource, and energy are required for basic production. 3. Waste matrix. Solid, liquid, and gaseous wastes are produced by consumption of natural resources, energy, etc. 4. Measure matrix. The cost-dependent technical, economic, and social measures are introduced here to reduce the emissions of wastes. 5. Environmental change matrix. This treats environmental changes caused by land use, resource consumption, and waste emission. Environmental changes considered here are air pollution, water pollution, toxic waste, soil degradation, deforestation, extinction, and water resource. 6. Interaction matrix. We consider interdependence between the environmental conditions. 7. Environmental impact matrix. This describes various impacts of the total environmental change on human activity and welfare. 8. Adjustment matrix. The human development is adjusted to the environmental changes and impacts. The outputs from the model are adjusted population and adjusted GDP/capita which is called Green GDP/capita.
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Fig. 3. Environment framework model of Japan.
4.4. Model integration There exist a lot of models and measurement data in the world. It is, however, dicult to access them from the outside. Models are being developed independently so that only limited problems can be investigated with individual models. Integration of such models enables us to predict the global environment and consider policy options totally. We are designing a system that helps to have several models in common between many researchers who wish to integrate many models, or compares models of the same purpose, or discuss uncertainties with other researchers. To have a variety of models on the computer network, a technique called the remote object is used, which
enables us to access objects in the network. Here, adapters should be prepared at respective stages so that we easily have several models in common. The task to integrate models on the network includes: 1. analysis of model classes, 2. extraction of common methods to use models by object-oriented analysis, and 3. connection of existing models or data by a transfer server. The system receives data through the input element class, carries out simulation by using methods stored inside the system, and passes data to other models or display systems through the output element class. In the root class of environmental models, if the lack of input or output is found, the environmental model it-
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self sends inquiry to the server about lacked data. A set of environmental models connected on the network is called a model ¯ow. Theoretically, a model ¯ow is a large global view that connects all possible models on the network, and is regarded as a large database. If a model ¯ow is too large to understand, a local view, that is, a partial network, is created in response to the user. Fig. 4 shows an example of generation of a model ¯ow. For the general use of environmental models, some common methods are extracted by analyzing common operations among models, and interface classes of models are determined. This oers a uniform framework to use common methods from applications regardless of contents of models. A rough classi®cation of model classes could include the abstract model class, qualitative model class, and numerical model class. A variety of models are de®ned by the same method and used as actual models by labeling input±output values and tuning inner parameters. This can be realized by the method of generating an environmental model in such a way that an input±output class is de®ned in some abstract models and inner parameters are added to them. It is possible to omit to create an environmental model if such an abstract model class is prepared. Compatibility of the semantic contents between dierent models is indispensable. A class is oered
for basic input±output, and the creation of operating sub-classes is put into the user's hands. It is possible that dierent models have the same input± output class. A method dealing with such a case should be considered in an actual implementation.
Fig. 4. A model ¯ow by automatic integration.
Fig. 5. Decision support systems.
4.5. Decision support systems One of the main tasks in our approach is to develop decision support systems with computer assistance. Fig. 5 illustrates our ®nal goal. The evaluation of a decision support system could be made by using the following criteria [13]: 1. Background of analysts. Analytical systems studies have always focused on concrete problems, with a dominant role being played by the general mathematical background of the analysts. They should be able to not only understand clearly the content of the problems, but also to formulate them in a form that is analyzable, using mathematical, computational, and other skills. 2. Principle of practicality. Systems science is a technology to enrich human living by the application of science. From this point of view, like other technologies, on the one hand universality is required, whilst on the other hand practicality is stressed. Therefore, systems science is distinguished from mathematics, logic, or phys-
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ics, for it is a synthetic technology based on all of these sciences and other adopted technologies. However, the small possibility of producing counter evidence is allowed to enhance the principle of practicality. 3. Veri®ability of assumptions. Systems science is a problem-oriented synthetic technology developed for solving complex, large-scale problems, which incorporate uncertainty, and are often beyond the understanding of the systems developer in relation to their complete structure. Therefore, in developing a decision support system, eort should be directed to visualization of the information, which is well-matched with the human ability of sense or perception. And thereby in using a decision support system the models structure and assumptions should be cleared, in order to guarantee veri®ability. 4. Participation of users. As methods or models are becoming larger and more complex, they are inevitably extending beyond the understanding of many users. But, the user's participation in decision-making is quite important in building the models which are used in the decision support system, for otherwise the people who are required to use the developed system may reject its use. 5. Usefulness of information. Because the process of design necessarily involves numerous steps, the ®nal product of a designed decision support system, as well as the results emerging from its use, can never be formally proved to have been the best possible option or design. The major justi®cation of goodness is satisfactory quality, and the usefulness of information the system provides for the users. 5. Concluding remarks This paper presented our idea on systems thinking concerning the global environment, and our approach to dealing with complexity. There are some open questions to develop more useful systems methodologies: 1. In the modeling of complex, large-scale systems, especially, regional or global environment systems, how can we use the soft systems meth-
odologies by combining hard, mathematical approaches? Or, how can we support the judgment based on the insight of decision-makers? 2. In addition to the soft±hard axis, the detailrough axis is important because people want to understand the environmental situation easily, but at the same time, they want to know it correctly. How can we make a balance between detail and rough as well as soft and hard? 3. The role of the computer network in analyzing complex systems is another problem we would like to study. How can we integrate important knowledge, models or data by means of the computer network technology for the creation of future global environment? References [1] R.L. Flood, E.R. Carson, Dealing with Complexity, Plenum Press, New York, 1993. [2] X. Qian, J. Yu, R. Dai, A new discipline of science: The study of open complex giant system and its methodology, Chinese Journal of Systems Engineering and Electronics 3 (2) (1993) 2±12. [3] X. Li, H. Zheng, Study on general systems methodology, in: G. Midgley, J. Wilby (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1995, pp. 49±56. [4] J. Gu, Z. Zhu, Knowing Wuli, sensing Shili, caring Renli: Methodology of the WSR approach, Systems Practice 10, in press. [5] Z. Wang, Meta-decision making, in: G. Midgley, J. Wilby (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1995, pp. 87±92. [6] I. Nonaka, H. Takeuchi, The Knowledge-Creating Company, Oxford University Press, Oxford, 1995. [7] Y. Sawaragi, The shinayakana systems approach as a solving tool for complex systems, in: J. Wilby, A. Zhu (Eds.), Systems Methodology: Possibilities for Cross-Cultural Learning and Integration, University of Hull, UK, 1997, pp. 7,8. [8] A.P. Wierzbicki, On the role of intuition in decision making and some ways of multicriteria aid of intuition, Journal of Multi-Criteria Decision Analysis 6 (1997) 65±76. [9] Y. Nakamori, Development and Application of an Interactive Modeling Support System. AUTOMATICA, International Federation of Automatic Control, vol. 25, Pergamon Press, Oxford, 1989, pp. 185±206. [10] Y. Nakamori, M. Ryoke, Identi®cation of fuzzy prediction models through hyperellipsoidal clustering, IEEE Transations on Systems Man and Cybernetics 24 (1994) 1153± 1173.
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