Futures of global interdependence (FUGI) global modeling system: Integrated global model for...

Journal of Policy Modeling 27 (2005) 101–135

Futures of global interdependence (FUGI) globalmodeling system

Integrated global model for sustainable development

Akira Onishi∗

Centre for Global Modeling, FOST (Foundation for Fusion of Science and Technology),1-4-24 Hiyoshi-honcho, Yokohama 223-0062, Japan

Received 1 July 2004; received in revised form 1 October 2004; accepted 13 October 2004Available online 20 November 2004

Abstract

The FUGI (futures of global interdependence) global modeling system has been developed as ascientific policy simulation tool of providing global information to the human society and finding outpossibilities of policy coordination among countries in order to achieve sustainable development of theglobal economy under the constraints of rapidly changing global environment. The FUGI global modelM200 classifies the world into 200 countries/regions where each national/regional model is globallyinterdependent through international trade, export/import prices, financial flows, ODA, private foreigndirect investment, exchange rates, stock market prices and policy information, etc. The latest softwareof FUGI global modeling system (FGMS200) for the Windows 2000/xp professional is also available.© 2004 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

Keywords:FUGI global model 9.0 M200PC; FUGI global modeling system (GGMS200); Integrated global modelfor sustainable development, economic systems engineering; IT economics; Lifeinformatic economics; Economicsof brain physiology; Scientific tool of global policy coordination

∗ Present address: 2-16-7-1915, Konan, Minato-ku, Tokyo 108-0075, Japan. Tel.: +81 3 5783 0023;fax: +81 3 5783 0023.

E-mail address:[email protected].

0161-8938/$ – see front matter© 2004 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.doi:10.1016/j.jpolmod.2004.10.002

102 A. Onishi / Journal of Policy Modeling 27 (2005) 101–135

1. Introduction

In the 21st century it is expected that integrated progress of science, technology, and neweconomic development will be seen in the human society which consists of a globally inter-dependent complex system. The information technology innovation will give tremendousimpacts on human life, culture and economic development. Historically speaking, humanbehaviors under the global cultural changes imposed by the increasingly interdependentglobal human society are a rather new experience and challenge for the human society.

On the other hand, it is also expected that the 21st century will be an age of terrorismand refugees.

Under these circumstances, the FUGI (futures of global interdependence) global mod-eling system seems likely to play a significant role in efforts to envisage the future ofglobal interdependence and provide global information on the economic development andenvironmental changes through alternative policy scenario simulations for the sustainabledevelopment.

Project FUGI was started in 1976 with the cooperation of three Japanese institutions,namely, the University of Tokyo, Osaka University and Soka University, under the spon-sorship of the National Institute for Research Advancement in Tokyo. The original FUGImodel consisted of three parts: a global input–output model (GIOM), a global resourcesmodel (GRM), and a global economic model (GEM), Types I, M15. Yoichi Kaya, Facultyof Engineering, the University of Tokyo, Yutaka Suzuki, Faculty of Engineering, OsakaUniversity, and the author coordinated the designing of these models, respectively (Onishi,1977, 1980). Work in progress was reported at the IIASA global modeling symposium in1977 and the years following. The first generation FUGI global economic model (Type I,M15) designed by the author was the development of the multi-nation economic modelwhich was originally designed by the author in 1965 and applied the 15 countries in Asiafor the purpose of projections of the Asian economy (Onishi, 1965). Drawing on experi-ences with global modeling in the 1970s, the author developed a fourth-generation FUGIglobal economic model (Type IV, M62) that divided the world into 62 countries/regions andconsisted of approximately 30,000 equations. It was first made public at a seminar on com-parative simulations of global economic models held at Stanford University, 25–26 June1981 (Onishi, 1981). The United Nations Secretariat, Department of International Economicand Social Affairs, Projections and Perspective Studies Branch for the purpose of long-termprojections and policy simulations of the world economy soon afterward adopted this modelfor use. It was used from 1981 to 1991 when it was replaced by the new generation FUGIglobal model, Type VII, M80.

For the period 1985–1986, a new generation of the FUGI global model was designed asa global early warning system for displaced persons (Onishi, 1986a, 1986b, 1986c, 1986d,1987, 1990, 1997b) during the period 1990–1995, the FUGI model 7.0 M80 was designedas an integrated global model for sustainable development (Onishi, 1993, 1994a, 1994b,1995a, 1995b, 1996, 1997a).

During the period 1991–1999, the author designed a significant new software system forglobal modeling. This expert software system, named as FGMS (FUGI global modelingsystem) using an IBM R/S 6000 workstation was researched and developed as a packagefor specific use in making computations for the FUGI global model 9.0 (Type IX) M200/80

A. Onishi / Journal of Policy Modeling 27 (2005) 101–135 103

(Onishi, 1991, 1993, 1994a, 1994b, 1995a, 1995b, 1998, 1999, 2001a) and M200 (Onishi,2000).

In 2000, this expert system has entered the new stage of FGMS 200 using a personalcomputer (Windows 2000/xp) for running the FUGI global model 9.0, M200PC. This lat-est M200 model, consisting of more than 150,000 equations, classifies the world into 200countries/regions so that the model can produce the forecast simulations of the sustain-able global development with interdependent 200 national/regional developments (Onishi,2001b, 2001c).

The global model simulation exercises using FGMS200 cover the baseline projection ofthe global economy, 2001–2020. The model can provide information not only on the baselineprojections but also on the alternative policy scenario simulations for global coordinations(Onishi, 2002a, 2002b).

2. Outline of FUGI global modeling system

2.1. Regional classification

The FUGI global model 9.0 M200PC divides the world into 200 countries and regions.For three major groupings there are (1) developed or advanced market economies (AME),(2) developing economies (DGE) and (3) economies in transition (EIT). The AME groupingcontains the following sub-groupings; these are developed market countries including thedeveloped Asia-Pacific, North America, member countries of the EU and other western Eu-rope. The DGE grouping contains the following sub-groupings as Asia-Pacific (subdividedinto East Asia, Southeast Asia, Southwest Asia and Pacific Islands); Middle East; Africa(subdivided into North Africa and Sub-Saharan Africa); Latin America and Caribbean; andMediterranean. The EIT grouping includes two sub-groupings: (a) East Europe and (b) CIS,the former USSR. Ultimately, this global model divides the world as a whole into 200 coun-tries/regions. Because almost all developed market economies, developing economies andeconomies in transition are treated as country units, the model has the advantage of beingable to analyze precise country-specific relationships within the framework of global inter-dependence (see Appendix A). We have prepared for six global table formats such as CGM(above-mentioned regional classification), EU (for the European Commission), IMF (forIMF classification), UN (for the United Nations classification), UNCTAD (for UNCTADclassification), UNESCAP (for UNESCAP classification) and WB (for the World Bankclassification). It is worth noting that a user of FGMS200 can easily make his own format.

2.2. Scientific design concept of the global modeling system

Thescientific integrated economicsdesign concept of FUGI global modeling system hasbeen influenced by the recent advancement of life science, biotechnology and informationtechnology. The keywords are given below. (1)Lifeinformatic economicscoupled withlife science, information technology and economics. (2)Global dynamic cooperation andpolicy coordinationamong the countries, (3)Self-organizationin accordance with changingenvironment, (4)automatic error correction system, (5) Brain physiologyeconomics incollaboration with right and left brain, (6)Fluctuation phenomenonconsidering alternative


composite policy scenario projections underuncertaintyworld and (7)Global earlywarningsystem for geographical and global risks. It is worth noting that quick policy prescriptionand coordinated policy actions might be feasible through early recognition on possibleglobal risks, since FUGI global modeling system will be able to provide up-to-date globalinformation on alternative futures within very much limited time span.

Econometrics has thus come forward as a powerful tool that radically supersedes for-mer theoretical models based on abstract logical methodologies. Of course some structuralparameters will have a high degree of stability over time, but econometric models never-theless face the dilemma that certain of their structural parameters are indeed changeable,thus posing a problem in forecasting. Indeed, this type offluctuationphenomenon seen inlife phenomenaalways threatens forecasts of the future using econometric methods.

In a similar way, the appearance of complex and interrelated global issues such as en-vironment, energy, development, peace and security, human rights and displaced persons,and so on, has posed problems whose solution is quite impossible within the traditionalframeworks of economics.

Integrated life-supporting systems require a new methodology ofLifeinformatic eco-nomics(life science + information technology + economics) beyond econometrics (eco-nomics + statistics). At the same time, there is increasing need for a new system designmethodology on fuzzy systems that can manipulatesoft variables that are not so easilyquantified.

These facts help explain why, since the early 1970s, research was begun on the designof integrated global models.

There has come into use an integrated global modeling which supplements this weakpoint in econometric models. Known as system dynamics or SD for short, it is the methodused in the world system dynamics model developed by Jay Forrester, who gained rapidrecognition for discussions of the model in theLimits to Growthreport to the Club of Romeprepared by Dennis Meadows et al. The most distinguishing point about the SD method isits seeing reality in terms of dynamic (i.e. active and continually developing) structures forsystems. Systems used in such models have a number of variables, which govern the waysin which changepattern recognitionmethods take place in the past, present, and future.

It is a fact that SD methods are the object of various types of criticism; in particular,by econometric methods. Econometrics methods have achieved qualitative improvementsthrough the advancement of information technology. Thus,IT economics(Information tech-nology + economics) has appeared. In any case, the main distinguishing feature of econo-metric models is the fact that the models’ structural parameters are inferred from real statis-tical data by stochastic methods. Compared with econometric methodology, the structuralparameters used in SD models are not necessarily as appropriate, especially in the caseof Forrester’s world model, since his model seems to be too “deterministic,” neglectingstochastic natures of the systems. Consequently, criticisms are often voiced alleging thatwith the relatively rough parameters used in SD models, forecasts about the future musttherefore have a low credibility.

SD seems likely to be an outgrowth of old-fashioned Newtonian dynamics systems thatare too deterministic to allow for stochastic fluctuations of systems. Furthermore, Forrester’sworld model does not classify the world into regions or country groups, so it cannot discussglobal interdependence as north–south issues.


However, it is difficult to assert that these are fundamental faults in the SD method.This method’s most outstanding characteristic lies in its comprehensive,intuitive patternrecognitionof social and economic phenomena as being a complicated loop of cause-and-effect relationships. In this process, there is the problem of how to estimate stochasticstructural parameters as intermediaries in determining the cause-and-effect links among thevariables.

In spite of these serious defects of not regarding stochastic phenomenon, the SD method,which can easily accommodate a nonlinear fuzzy system, may be said to be relativelyversatile in comparison with the econometric method. Of course this is not to say that aneconometric model is incapable of handling a nonlinear fuzzy system. But it cannot bedenied that the econometric method has the disadvantage of being less flexible than the SDmodeling method when we include non-economic and non-quantifiablesoft variablessuchas terrorism, peace and human rights.

We are now faced with the task of deepening our understanding of the various method-ologies and creating a new approach that includes the best features of all of them. The authorshould like to call such an approach, which will ideally exercise both the left and the righthemispheres of the human brain, a stochastic and fuzzy “dynamic soft systems analysis”(DSSA), using human-intelligence oriented modeling. For purposes of making simulationsof the future global economy it is necessary to quantify reality and make analyses by meansof computer-aided modeling; yet there is at the same time a need to make qualitative anal-yses, i.e. scenario analyses because the future should have certainfluctuation phenomenawithin the range of optimistic or pessimistic futures in accordance with human behaviors.

To gain a grasp of not just a part of economic reality but of its whole, a method ofsystemsengineeringis indispensable. Thedynamic soft systems analysis(DSSA) is indispensablefor the analysis of a world in which the whole of socioeconomic and environmental realityis constantly changing and developing over time. DSSA is an attempt to offer practicalprescriptions by which we can respond to the “crisis problematiques” facing humankind,as Aurelio Peccei, founder of the Club of Rome, suggested. The prescriptions derived froma model of interdependent dynamic system structures, patterned after the real world andsubjected to human-intelligence-oriented modeling, in turn allow us to elaborate probableor possible pictures of our world in the future, depending on various possible “scenarios”and “policy exercises”.

Stimulated by our joint research with the United Nations University on a “global earlywarning system for displaced persons” (1986d), we have felt the need for our FUGI model togo beyond its present capacities centered on an econometric model in the rather traditional,restricted sense of the term. We have therefore developed an integrated global model thatcan make future simulations of “global problematiques” or “global complexes of symp-toms”, including various types of environmental problems and the displaced persons issue(1987, 1995b, 2003a, 2003b). See (Onishi, 2003b) FUGI global model for early warningof forced migration (http://www.forcedmigration.org) Forced Migration Online, RefugeeStudies Centre, University of Oxford. The FUGI global model is presently being expandedin scope to deal with such issues by usingLifeinformatic economics.

The latest FUGI model 9.0 M200 treats almost all countries, regardless of how large orsmall, as having the possibility of being dealt with as country units. It is designed to bea comprehensive system model that can not only deal with economic problems but also

http://www.forcedmigration.org/


incorporate subsystems to take account of environmental issues, population, energy, food,indicators of quality of life, as well as issues concerning human rights, peace, and secu-rity. Although our methodology is first and foremost based on various country or regionalstudies, we have felt it desirable, using these country or regional studies as a base, to adoptan orientation that further gives consideration to a highly sophisticated global modelingsystem.

The “dynamic soft system approach”, derived from Lifeinformatic economics, reflectsthe astounding development of information technology, particularly in the field of computersduring 1970s, 1980s, and 1990s. Extraordinarily sophisticated handling of information hasbecome possible. In this regard, too, the software which computers use, that is to sayutilization techniques, have made notable strides toward what we might calleconomicsystems engineering.

This approach is supported not only by the so-called soft sciences but also by the de-velopments in a number of interrelated fields of the frontier human sciences. For example,our understanding of the human brain has greatly advanced through developments inbrainphysiology. As a result, it is seen that the right brain perceives images of reality, while theleft brain analyzes these in logical and conceptual ways and constructs logical models. Asa part of its own division of labor, the brain’s central ridge facilitates high-level flows orexchanges of information between the left and right hemispheres. Through a skillful treat-ment of the organically linked functions of the left and right brains, one can develop a softsystem model. In a similar way, what we have tried to develop for our present purpose is nota model that merely collects information but a model that skillfully collects information,analyzes it, and provides a sophisticated global information system based uponeconomicsof brain physiology.

The developments in life sciences are making ever clearer the conceitedness betweenindividual cells of the human body and the human body as a whole organic entity. Individualcells contain information pertaining to the entire body. Thus, at times of special stress, theindividual cells invoke a regulatory mechanism by which they pool their forces, workingtogether in the face of difficulties. This is an extraordinarily important capacity, which livingthings possess, and we in fact need to incorporate just this sort of capacity into any globalmodeling system to prevent or mitigate, through international cooperation, undesirablephenomena in the global human society.

In the author’s view, first-generation modern economics was based upon Newtoniandynamics and Darwinism. Second-generation economics was econometrics, which has beengreatly developed through progress with particle physics, stochastic statistics and economicmodeling. The third generation might be called the dynamic integrated systems analysis orLifeinformatic economics, reflecting progress in life sciences, biotechnology, ecology, andsoft systems science and information technology.

In the 21st century, we may expect that economic models will come to have much softerdynamic systems. The information revolution, often known as the third wave, has had agreat impact on the field of economic research, and through the extraordinary progress beingmade with computer hardware and software systems, great changes are being made in thetraditional methods of economic research. With the advent of large-scale capabilities for dataprocessing by personal computers, FUGI global modeling system has become accessible toeconomists, and as a result we look forward to greatly improved capacities for research on


economic theory and economic policies. The making of policy proposals and the buildingof theoretical economic models, formerly dependent on professional economists with richexperience, sharp intuition, and outstanding capacities for judgment and analysis, can now,through intelligent expert systems, be achieved to a large extent by ordinary researchers.Consequently, what the author has created FUGI global modeling system as Lifeinformaticeconomics is about to enter an age of global interdependence when it can justly claim prideof place as a science of economics.

2.3. Model structure

The FUGI global model 9.0 M200PC classifies the world into 200 countries/regions.Each country/regional model is globally interdependent through direct linkages of the worldtrade matrices, export/import prices, primary commodities prices, foreign exchange rates,official development assistance, private foreign direct investment, external debt, interestrates and, etc. It is also globally interdependent through indirect linkages such as pop-ulation changes, economic development policies, energy policies, environmental polices,etc.

Each national/regional model consists of integrated nine major sub-systems: (I) popu-lation, (II) foods, (III) energy, (IV) environment, (V) economic development, (VI) peaceand security, (VII) human rights, (VIII) health care and (IX) digital divide. Economic de-velopment system as a major core of the model has 11 economic sub-blocs. It includes(1) labor and production at constant prices, (2) expenditure on GDP at constant prices, (3)income distribution: profit-wage, (4) prices, (5) expenditure on GDP at current prices, (6)money, interest rate and financial assets, (7) government finance, (8) international balanceof payments, (9) international finance, (10) foreign exchange rate and (11) developmentindicators. For further details of the model, see Appendix B. FUGI global model M200PC;theoretical model. See also (Onishi, 2003a) FUGI global model 9.0 M200, Integrated GlobalModels for Sustainable Development, UNESCO Encyclopaedia of Life Support System,EOLLS Publisher, Oxford, UK, 2003 (http://www.eolls.net).

2.3.1. Population systemIn the population system, there are key variables such as population, birth rates, total

fertility rates (TFR), death rates, population below 15, population above 65, economicallyactive population, 15–65, life expectancy at birth for male and female.

The population changes can be explained by birth rates and death rates as well as netimmigrant’s rates. The natality depends upon birth rates while the mortality depends upondeath rates. The both birth rates and total fertility rates are mostly explained by real GDPper capita, rates of government education expenditures to GDP, the death rates over thepast 5 years and the life expectancy at birth. The death rates can be explained by realGDP per capita, rates of government health and welfare expenditures to GDP, the sharesof population over 65 to total population, the birth rates over the past 5 years and lifeexpectancy at birth. The life expectancy at birth of both male and female can be explainedby real GDP per capita, rates of government health, and welfare expenditures to GDP,rates of government education expenditures to GDP as well as, total fertility rates. Theratio of the urban population to total population is estimated by (1− (rural population/total

http://www.eolls.net/


population)) which in turn can be explained by the changes in the ratio of agriculturalincome to GDP.

2.3.2. Economic development system

2.3.2.1. Labor and production at constant prices.In this supply-side sub-bloc, specialproduction functions are used in order to estimate real potential gross domestic product,i.e., GDPP# in a given country. In this supply-side system productivity trends in the devel-oped market economies are estimated not by the traditionally used neo-classical productionfunctions, but rather by the author’s own concept of “production functions”. The value-added productivity per employed person, GDPP#/LCLF, is explained by non-housing cap-ital stock per employed person, NHFCS#/LCLF, educational assets per employed person,EDUA#/LCLF, technology assets per employed person TECHA#/LCLF, ratio of cumulativenon-housing capital investment over the past 5 years to non-housing capital stock, SUMT5(NHI#)/NHFCS#, and ratio of petroleum prices (as expressed in domestic currency) relativeto WPI, namely PEO× FERSI/WPI as a dummy variable for energy inputs.

Of course, value-added productivity will rise with higher ratios of capital to labor (i.e.,with the advance of automation and the use of robots), and as seen in the case of highvalue-added products, productivity will rise with technology assets per employed personas a result of research and development expenditures. Also, a lessening of the vintage ofcapital stocks works to raise productivity. As for energy inputs as a factor in production,there is a tendency for production to lag when a given country’s petroleum prices, expressedin its own currency (domestic energy cost), are high in comparison to its GDP deflator orWPI. Also, value-added productivity seems to be influenced by the capacity utilization rate,CUR that reflects the current demand situation.

Its worth noting that the shares of information technology investment over the past 5years to non-housing capital stock, SUMT5 (ITI#)/NHFCS#, tends to play much greaterrole in increasing productivity of the US and Japanese economy.

In the case of the developing market economies, production functions cannot be appliedin the same way as when applied to the developed market economies. In the developingcountries, expenditures on research and development are in most cases almost negligible.Instead of R&D, technical cooperation over the last 3 years, SUMT3 (ODATC), seemslikely to increase labor productivity. As the education may influence on the productivityof developing countries, educational assets, EDUA# seem rather more persuasive in theirimpact. The same is seen in government expenditure on health over the last 5 years, SUMT5(GH#). In the non-oil-producing developing countries, high-energy costs have the samenegative impact on productivity growth as in the developed countries.

For the economies in transition, the main explanatory variable is fixed capital stock peremployed person. This is because in the countries in this category, data for expenditures onresearch and development, together with data for some other major explanatory variablesare unavailable.

Research and development expenditures, RD#, can be explained by operating surplus,OS#, minus corporate income tax in some countries as well as by government defense ex-penditures and economic services. In the following examples, it is seen that in Japan, unlikethe US, R&D expenditures are not notably influenced by government defense expenditures.


Unemployment rate, UNEMPR, tends to increase with rises in employment cost,WSEI/LPI, or WSEI/CPI/LPI<USA>, as well as with a decrease in overall economic cli-mate in terms of GDP#.1/GDP#.2 and GFCF#/GDP#. Unemployment rate also tends toincrease in line with supply of labor force, LCLF (adjusted by hours of work, HOW, incase of EU, Japan and USA). Using explanatory variables such as the above, changes inemployment rates may be estimated from given time-series data.

The model’s supply-side subsystem for looking at production with energy constraintsgives special consideration to the possibility of unusual circumstances such as a suddencut in oil supplies due to an intensification of warfare in the Middle East. Simulations canbe made, for example, as to what kinds of impacts on a country’s domestic productionmight be expected under such circumstances. For instance, in the case of sudden oil supplyrestrictions, supply-side questions would arise with regard to alternative domestic energysources (e.g., the extent to which domestic oil production could be increased, or the degreeto which alternative energy sources could be developed), and the extent to which oil reservescould or should be used.

At the same time, the model indicates how, in response to rising energy costs; a non-oil-producing country’s domestic demand is kept down. In other words, effective domesticenergy demand cannot increase to an extent greater than allowed by energy supplies (do-mestic and foreign), and there exists, so to speak, a built-in “feedback system” by which, ifrelative energy costs rise and cutbacks are made in energy use, growth in real GDP is likelyto decline, putting a brake on energy demand.

Thus, in considering one or another possible degree of oil supply restriction, the modelreflects, through a convergent process of computations, the “system” by which domesticprices rise in response to energy price rises and resultant cutbacks in energy use. Higherenergy costs may bring lowered rates of real GDP growth and lowered energy demand untilsupply and effective demand come into balance.

Production by industrial origin is classified into three major sectors: agriculture, industry,and services. Industry is furthermore classified into manufacturing and other industrialactivities. First of all, we obtain theoretical curves showing nonlinear relationships betweenper capita real GDP and ratios of each sector’s “real value added” to real GDP, by usingcross-country analyses. Then, we forecast the future changes in industrial structures in eachcountry or regional grouping using time-series analyses of deviations from the theoreticalcurves derived from cross-country analysis.

2.3.2.2. Expenditures on GDP at constant prices.In this sub-bloc, the demand side ofreal GDP in each country or region is treated in terms of such major items within GDPas exports minus imports, private final consumption expenditures, government final con-sumption expenditures, non-housing fixed investment, housing investment, and changes ininventories. Together, these factors in the model form an organic economic system of globalinterdependence.

In the FUGI global model, global interdependent relationships among nations are ex-pressed in terms of the world trade matrix, and also through a “multilateral trade couplingsystem” (MTCS). The Project LINK system deals with trade matrices using a linear tradecoefficient matrixA (see Project LINK System in EOLLS Encyclopedia). The FUGI modeladopts another method to use non-linear trade coefficients. The exports at constant US


dollar prices of a given countryI to another trading partnerJ , namely E#MAT<I, J> thatis each element of the world trade matrix are explained by the most adequate combinationsof variables. As a matter of fact, the total exports of each country, ETFOB#, are aggregates ofrow elements of the world trade matrix and the total imports in each country, MTFOB#, areaggregates of column elements of the world trade matrix. In the FUGI global modeling sys-tem, for example, the most appropriate export functions are chosen for elucidating varioustrade relationships, such as exports from each country to the developed market economies,to the developing market economies, and to the economies in transition. The model canalso analyze trade flows between developed market economies. For example, Japan’s realexports to the United States E#MAT<JPN, USA> are explained by such variables as the USreal gross domestic product GDP#<USA>, the ratio of Japan’s export prices PES<JPN> tothe United States consumers price index CPI<USA> (indicating conditions of price com-petitiveness between Japan and the US). It is worth noting that Japan’s exports to the USare also influenced by Japan’s research and development expenditures, RD#<JPN>, overthe most recent past 4 years. The same thing is true of US exports to Japan.

At times when Japan’s price competitiveness increases due to a devaluation of the Yenagainst the US dollar, there is a tendency for Japan’s exports to the US to show a temporaryincrease. Even at times when US business conditions are relatively inactive and real GDPis kept down, if US domestic prices should rise in comparison to Japan’s export prices,it is possible that Japan’s exports to the US might not slacken but would rather increase,possibly eventuating in greater US–Japan trade imbalance. On the other hand, US exportsto Japan are influenced not only by Japan’s GDP and tariff and non-tariff barriers but alsoby US price competitiveness and non-price competitiveness, which could be strengthenedby research and development expenditures.

In contrast to the case of Japanese exports to the developed countries, in the case ofJapan’s exports to the developing countries, the model introduces into its export functionsthe concept of “import capability”, CAPM#. In order to determine the “import capabilities”of developing countries, their real exports, E#, adjusted by the terms of trade, PES/PMS,ought, first of all, to be considered. Also to be considered as factors in defining importcapabilities of developing countries are financial inflows, FCI, such as bilateral ODA andprivate direct investments from developed countries, as well as multilateral developmentassistance from other international organizations (deflated in terms of dollar-base importprices, PMS, of the importing countries in question).

For instance, Japan’s exports to a given developing country increase when that risesrelatively to import prices so that there is an improvement in the terms of trade. On the otherhand, if there is a worsening in the developing country’s terms of trade (with drops in theprices of primary commodities under stagnant business situations), there import capabilitiesdrop, and a “system” comes into being whereby Japan’s exports to the country in questionstagnate.

Similar conditions apply to Japan’s exports to the economies in transition. In other words,what determines exports to the economies in transition is not just the scale of those countries’GDP# but rather considerations of import capability.

Thus Japan’s total exports are derived from the trade matrices which include Japan’sexports to the other countries and regional groupings of the model. Similarly, all the elementsin trade matrices may be computed on the basis of estimated export functions. While trade


between a given country and itself will of course be zero the diagonal elements of the matrixwill indicate inter-regional trade among regions.

Private final consumption expenditures, CP#, in the case of the developed marketeconomies, can be explained by such factors as GDP# or domestic disposable income,DFI#, compensation of employees, COMPE#, operating surplus, OS#, expected inflationrates, and short-term interest rates, IC. In this model, “private consumption functions” areclassified into categories called the AME type, the Japan type, the USA type and the EUtype, respectively. Both the Japan and the USA types have additional explanatory variablesof financial assets.

In the case of most developing market economies for which a production-oriented modelis used, as well as in the case of economies in transition, private consumption is calculatedas what is left after subtracting savings from income. However, for some developing marketeconomies demand oriented models are used in order to incorporate both supply and demandsides, similar to those employed for the developed economies.

The increasing rate of non-housing real investment, NHI# in most of the developedmarket economies, can be explained by the expected increase in operating surplus, OS#after deducting corporate income tax, TYC# divided by non-housing fixed capital stock,NHFCS# minus long-term interest rate (prime rate), IP. Other relevant explanatory variablesare exports, ETFOB#, research and development expenditures, RD#, share of informationtechnology investment to non-housing investment, ITI#/NHI# and capacity utilization rate,CUR, etc. In most of the developed market economies, a nonlinear investment function isapplied.

Housing real investment, HI#, in the case of the developed market economies can bemostly explained by the gross domestic product, GDP#. Other relevant explanatory variablesare long-term interest rate on housing loan, IH, and housing investment price deflator, PHI.

In the case of investment functions for developing market economies, GDP#, in somecases domestic savings is used in place of “operating surplus”. Also, as mentioned previ-ously, the concept of “capability to import” with respect to capital goods is also used as arelevant explanatory variable. In importing capital goods from the developed countries formaking investment in plant and equipment, most of the developing countries and plannedmarket economies have a restricted availability of foreign currency reserves—a matter towhich the model gives due consideration.

2.3.2.3. Incomedistribution—profits andwages.In the sub-system for determining incomedistribution, the major variables are operating surplus and wages. Factors determining realoperating surplus, OS#, are GDP# (from which compensation of employees, COMPE#, issubtracted), interest rates, IP, and terms of trade, PES/PMS, etc.

Factors affecting employed persons’ nominal wage rates, WSEI, are rate of increasein consumer prices, CPI; rate of change in productivity, LPI; ratio of nominal operatingsurplus, OS, to nominal GDP; and unemployment rate, UNEMPR.

Needless to say, these variables are not all used for all countries; rather, those variablesare chosen which appear to be the most appropriate and to have the highest descriptivecapacity for a given country at a given time. In the above process of selecting variables,the most highly descriptive explanatory variable for nominal wages in many countriesis consumer price index. This reflects the fact that in many countries in Europe “wage


indexation” is a significant factor in labor-management agreements. Consequently, in suchcases, changes in productivity may possibly have little effect on the determination of wages.Cases of unemployment rate affecting wages may be described by the Phillips Curve in post-Keynesian economic theory, but such would not always appear to be the reality at all timesor in all developed market economies.

2.3.2.4. Price.A number of price-related functions are incorporated into the model. Theseare wholesale price index, WPI (producers price index), consumers price index, CPI, and“deflators” for private final consumption expenditures, government final consumption ex-penditures, non-housing investment, housing investment, exports and imports, etc. TheFUGI global model’s most characteristic feature is that each country’s export and importprices are endogenously determined. The “system” is one in which export prices are de-termined first, after which import prices are determined through the trade matrix, takinginto account the weighted average of each country’s export prices in accordance with thevolume of trade with each other country.

The model also gives scope for the testing of a “system” whereby, according to recentmonetarist contentions, wholesale prices could be influenced by changes in money supply,IV# (i.e., rate of expansion of M2 in relation to real GDP#). In other words, it should bepossible to analyze, through such a system; the results of monetarist policy measures takento combat inflation whereby, in an attempt to stabilize consumer prices and keep down wagehikes, wholesale prices are kept down through restrictions on money supply.

The percentage changes in export prices, PES are explained by those of WPI, wage cost,WSEI/LPI, weighted average export prices of the developed countries, PESAME, foreignexchanges indices, FERSI and primary commodities pries, PEC, etc. in the developed marketeconomies. On the other side, the percentage changes in oil prices, PEO in the oil exportingdeveloping countries may be explained by the changes in the weighted average export pricesof the developed market economies, PESAME and those of shares of global oil to globalenergy requirement, OILG/ENGYRG, in addition to the oil shock dummy variables.

On the other hand, percentage changes of non-oil commodities prices, PEC is explainedby those of the weighted average export prices of the developed market economies, PE-SAME and the weighted average rates of interest of the developed market economies,ICAME.

2.3.2.5. Expenditures on GDP at current prices.The values for the various componentsof nominal GDP, when multiplied by the various corresponding deflators yield the corre-sponding nominal values of GDP. The GDP deflator, PGDP, is thus obtained by dividingthis nominal GDP by real GDP#.

2.3.2.6. Money, interest rates and financial assets.In defining M1 and M2 in the monetary“sub-bloc”, the M1 money supply functions are determined first through the selective use(in response to the conditions of a given country) of such explanatory variables as nominalGDP and the central bank’s official discount rate, IN.

Next, quasi-money (time and savings deposits outstanding), MTD, is determined by suchexplanatory variables as GDP and difference in interest rates between long-term governmentbonds and time deposits, (IB− ITD)/ITD. The model is “system designed” so as to be able


to reflect the financial innovation that has taken place in recent years in the United States.Then, M2 is obtained from the sum of M1 and MTD.

The model allows for upper and lower limits of M1 and M2 in response to recent mon-etarist policies. At times when what can be called a policy “target zone” is mapped out todefine the upper and lower limits of permissible expansion for M1 (and/or M2). Such poli-cies are carried out to direct the economy within those limits; M1 (and/or M2) as computedfrom the demand side will be kept down by restrictions on supply. In such a case, changes inthe speed of currency circulation will take place in response to the supply-restricted valuesfor M1 (and/or M2). Thus, as mentioned before, the model makes allowances for such asystem and its possible impact on domestic inflation.

A point that should be given attention here is the impact on interest rates from a strength-ening of control over the money supply in cases where there are certain pressures to increasemoney supply as a result of increased credit to the government sector accompanying an ex-pansion of government expenditures and of the resultant government debt. Interest rates aredescribed in terms of various functions, including the following: prime rate (i.e., top prefer-ential bank rate extended to top-ranking enterprises), IP; housing loan rate, IH; short-terminterest rate, i.e., call rate or Federal Fund rate, IC; government long-term bond yield, IB;central bank’s official discount rate, IN; and Eurodollar rate, IEURO.

At present, numerous studies are being made in the attempt to explain interest rates,but it is worth noting that this model is specially designed to incorporate “policy reactionfunctions” in order to analyze what kinds of environmental conditions influence the raisingor lowering of official discount rates by central banks. In other words, when setting officialdiscount rates, central banks do not have a limitless degree of freedom in policy choice.The model indicates the realities according to which official discount rates can be raisedor lowered by central banks only within a rather narrow margin of choice as a result ofinfluence from the rates of consumer price hikes, 1 + DOT (CPI)− CPI/((CPI + CPI.1)/2);expectation of GDP# growth rates; exchange rate index, FERSI; interest rates in the UnitedStates, IN<USA>, etc. Recent studies have shown that what most influences short-terminterest rates, IC, are rates of change in consumer prices or expected rates of inflation aswell as rates of increase in money supply.

On the other hand, government long-term bond yield, IB, when government bond issuesincrease as a means of obtaining funds during a period of expanding government deficit,government bond yields are raised due to concern that the value of the bonds might otherwisefall, and this at the same time influences the short-term money market. If government deficitsshould expand during efforts to recover better business conditions, there is likely to arisea greater competitiveness with private demand for funds, with increased concern about theso-called “crowding out” phenomenon, which may be understood to be a result of pressurespushing for higher interest rates (or at least influences preventing a lowering of interestrates), unless there is a large-scale inflow of capital from abroad. In this way, governmentfinances and private financing are closely connected in the interdependent world economy.Moreover, trends affecting the US interest rate are also reflected in the Eurodollar rate.

2.3.2.7. Government finance.The “government finance sub-bloc” is defined as having twosides: (1) revenues and (2) expenditures. The revenue side is made up of various types of gov-ernment revenue, including personal income tax, corporate tax, social security contributions,


and various items of indirect taxation. The expenditure side is expressed in functional terms,e.g., public services, national defense, education, public health, social welfare, housing,community services, economic services, etc. “Government final consumption expenditures”and “public investment” are considered to be not functional classifications but rather classifi-cations of types of government “economic activity” in accordance with national accountingsystems. The various functionally differentiated items of expenditure are defined accordingto functions showing them to be subject to restrictions conditioned by a given country’sgovernment revenues, government debt outstanding, previous year’s expenditure level, etc.

2.3.2.8. International balance of payments.The model makes a rather detailed analysisof the international balance of payments. Here, first of all, estimates are made for FOB-base exports and imports, as well as for various “service” items in terms of current USdollars. FOB-base exports and imports are derived from the aforementioned trade matrix.The current values of “services” are calculated from both credits and debits on such itemsas transport foreign travel and all others. In all other items, computer software transac-tions occupy greater weights in recent years. “Income” items cover returns on investmentand other income transfers. Private foreign direct investment and “portfolio investment”(i.e., investment in stocks and bonds) are calculated in terms of both assets and liabilities,respectively.

Private foreign direct investments, DIA, are explained by GDPS; employment costdifferentials, ratios of operating surplus to GDP and anticipated changes in foreign ex-change rates. In the case of portfolio investments—expressed as PIONA (assets) and POINL(liabilities)—the principal explanatory variables are ratios of M2 to GDP#, internationaldifferences in interest rates, anticipated changes in foreign exchange rates, current balanceof payments and ratios of operating surplus to GDP, etc. It can thus be seen that applicationof the model in this sub-bloc permits a comprehensive analysis of trade balances, currentbalance of payments, and capital accounts.

2.3.2.9. International finance.The official development assistance (ODA) of the developedto the developing countries is determined as a function of the size of the donor country’sdollar-base GDP (GDPS). It is then distributed both bilaterally and multilaterally. Bilateralassistance is distributed to developing countries in accordance with judgments made bythe donor country. In its handling of such assistance, the model uses OECD figures ofODA matrices for the most recent years for which geographical distribution matrix data areavailable.

In its accounting of multilateral assistance, the model uses the concept of a “worldpool”. In other words, the cumulative worldwide flow of multilateral aid is collected, anda “system” is designed within the model whereby funds flow to the various developingcountries in accordance with already existing distribution patterns. A useful characteristicof the model is its ability to estimate impacts on given developing countries from a givendegree of raising or lowering the ratio of ODA to the GDPS of the DAC member countriesamong the OECD. In this model, it is worth noting that a complex system structure forthe external debt of the developing countries has been introduced by using the World Bankdata.


2.3.2.10. Foreign exchange rates.Various types of theoretical models have been developedfor determining exchange rates. The present model uses, as an indicator, an exchange rateindex (1995 = 1). For example, in describing the index for the Japanese Yen against the USdollar, we have made the following explanatory variables. These are ratio of Japan’s GDPprice index to the US GDP price index, PGDP<JPN>/PDP<USA>; ratio of Japan’s exportsto the US vis-̀a-vis the US exports to Japan, ESMAT<JPN, USA>/ESMAT<USA, JPN>; anddifferences in interest rates between Japan and the US, (IC<USA>− IC<JPN>)/IC<JPN>;and Euros exchange rate index against the US dollar; FERI EUR.

Of course the explanatory capacity of these variables will depend upon a given countryand time span. With respect to recent trends in Japan and EU, the variable with the greatestexplanatory capacity is the influence of differences in interest rates with respect to theUnited States. Another important explanatory variable is the ratio of these countries’ currentbalances of payments to the US balance of payments. Differences in inflation rates betweenthese countries and the US have a consistent explanatory capacity in the determination ofthe exchange rates against the US dollar.

Because the present model’s object of analysis is not merely the developed marketeconomies but includes also the developing market economies and economies in transi-tion, the model must provide a means of determining exchange rates not only for certaindeveloped market economies representing the major currencies traded on the world mar-ket, but also for the developing economies and economies in transition. In most of thedeveloping economies and economies in transition, the theories of supply and demandtraditionally used in determining exchange rates for currencies directly traded in foreignexchange markets have little significance. Rather, there are numerous cases in which theinfluence of domestic inflation rate relative to the inflation rate in the US, and the “effec-tive exchange rate” of the US dollar hold the greatest descriptive capacity. A number ofcountries’ exchange rates are linked with the US dollar and/or “market baskets” of majorcurrencies.

In the latest FUGI model 9.0, M200, PPP (purchasing power parity) exchange rateshave also been introduced. For instance, we can compare GDP# at 1995 constant dollarprices with GDP#PPP at 1995 PPP dollar rates in IPCID (international per capita incomedisparity).

2.4. Model software

In 2000–2003, the author designed a significant new software system for integratedglobal modeling using a personal computer (Windows 2000/XP Professional). This ex-pert software system, named as FGMS (FUGI Global Modeling System) 200 was re-searched and developed as a “package” for specific use in making computations forthe FUGI global model 9.0 M 200PC. In addition to the FUGI model database system(FUGIDB), the FGMS 200 software system consists of (1) CONTROL, data file controlsystems for listing, loading, printing, and updating country (CNT), region (RGN) as wellas variable (VAR) data files; (2) DSERVE, supplementary data servicing programs forupdating and storing RGN.DAT file, etc; (3) ESTIMATE, estimating parameters of themodel, using automatic parameter estimating system (APES); (4) SIMULATE, makingsimulations using the FUGI global model; (5) OUTGT, printing out simulation results in


forms of 170 world tables, each country tables and world trade matrices; (6) UTILITY, re-ceiving data from FUGIDB to initialize and overwrite the time-series data, RGN.DAT,as well as to create variable data files, VAR.DAT. A user of FGMS200 can carry outautomatic estimation of a given set of structural parameters of the FUGI global model,using OLS as well as MLBM and making forecast simulations efficiently, using auto-matic error correction system (AECS). This expert AI system has already passed throughthe experimental stage and entered the stage of practical application. It is hoped that theFUGI global modeling system (FGMS 200) can contribute to progress in the integratedglobal modeling in the 21st century. For further details of FGMS 200, seeUsers Guide.FUGI global model 9.0 M200, Integrated Global Models for Sustainable Development,UNESCO Encyclopaedia of Life Support System, EOLLS Publisher, Oxford, UK, 2003(http://www.eolls.net).

3. Some examples of estimated parameters of the model

It is worth noting that through the use of FGMS200, automatic estimation of full setof parameters of the model is carried out very efficiently by automatically selecting eitherOLS (ordinary least squares) or MLBM (maximum likelihood method) in accordance withindicators of DW (Durbin–Watson ratio). Because of constraints of spaces, we cannot showthe full set of parameters of the model, but illustrate only a few examples for referencepurpose. For explanatory notes of the variables, see Appendix B: FUGI global model 9.0M200PC. You can also see any set of the parameters using FGMS200 (above mentionedFUGI global modeling software).

Population—life expectancy at birth (female):<Japan>

E010 LOG(LIFEEXPF< JPN>)

= 4.3183+ 0.0443(4.63)

∗LOG(GDP#.1/NP.1) + 0.0020(2.07)

∗LOG(GH#.1

+GSW#.1)/GDP#.1 + 0.0068(1.18)

∗LOG (GEDU#.1/GDP#.1)

− 0.0844(−2.68)

∗LOG(TFR),

R ∗ R = 1.000 (SE= 0.001), DW = 0.89 (MLBM(1986–1999))

<USA>

E010 LOG(LIFEEXPF< USA >)

= 4.1603+ 0.1021(12.82)

∗LOG(GDP#.1/NP.1) + 0.0393(6.68)

∗LOG(GH#. 1

+GSW#. 1/GDP#. 1) + 0.0042(0.95)

∗LOG(GEDU#. 1/GDP#. 1),

R ∗ R = 0.9999 (SE= 0.003), DW = 0.38 (MLBM(1971–1999))



<Republic of Korea>

E010 LOG(LIFEEXPF< KOR >)

= 4.3875+ 0.0304(14.2)

∗LOG(GDP#.1/NP.1) + 0.0230(6.24)

∗LOG(GEDU#.1/GDP#.1)

− 0.0944(−23.78)

∗LOG(TFR),

R ∗ R = 0.9999 (SE= 0.003), DW = 0.78 (MLBM(1971–1999))

Production function:<Japan>

E102 LOG(GDPP#/LCLF < JPN>)

= 1.8504+ 0.5236(3.75)

∗LOG(NHFCS#∗ CUR/LCLF)

+ 0.0686(0.45)

∗LOG(SUMT5(ITI#)/(NHFCS#),

R ∗ R = 0.9905 (SE= 0.031), DW = 0.95 (MLBM(1983–1999))

<USA>

E102 LOG(GDPP#/LCLF < USA >)

= 3.3526+ 0.2361(1.76)

∗LOG(NHFCS#∗ CUR/LCLF)

+ 0.1560(3.05)

∗LOG(SUMT5(ITI#)/(NHFCS#),

R ∗ R = 0.9978 (SE= 0.018), DW = 0.64 (MLBM(1982–1998))

<Korea>

E103 LOG(GDPP#/LCLF < KOR >)

= 1.0427+ 0.3697(0.63)

LOG(NHFCS#/LCLF) + 0.3672(0.62)

∗LOG(EDUA#/LCLF)

+ 0.1362(3.15)

∗LOG (SUMT5(GH#.1)/LCLF)

− 0.1185(−1.99))

∗LOG(PEO.1 ∗ FERSI.1/WPI.1),

R ∗ R = 0.9884 (SE= 0.034), DW = 2.29 (OLS(1985–1999))

<China-Mainland>

E103 LOG(GDPP#/LCLF) < CHN >

= 3.5472+ 0.3606(1.94)

∗LOG(NHFCS#/LCLF)

+ 0.9886(5.14)

∗LOG(SUMT5(EDUA#.1)/LCLF)


+ 0.0046(0.50)

∗LOG(SUMT3(ODATCR.1)/PMS.1),

R ∗ R = 0.9967 (SE= 0.049), DW = 0.5939 (MLBM(1975–1998))

<Bangladesh>

E103 LOG(GDPP#/LCLF < BGD >)

= 1.6088+ 0.1479(1.30)

∗LOG(NHFCS#/LCLF) + 0.6589(7.98)

∗LOG(EDUA#/LCLF)

+ 0.1569(2.70)

∗LOG(SUMT5(GH#.1)/LCLF)

+ 0.0132(0.50)

∗LOG(SUMT3(ODATCR.1)/PMS.1),

R ∗ R = 0.9957 (SE= 0.013), DW = 2.10 (OLS(1975–1998))

Unemployment rate:<Japan>

E113 UNEMPR< JPN>

= −0.5363+ 0.0298(1.59)

∗LOG LOG(LCLF∗ HOW)

+ 0.2136(10.69)

∗LOG(WSEI.1/ LPI.1(−1.57))

) − 0.0125∗ LOG(GFCF#. 1/GDP#. 1),

R ∗ R = 0.9620 (SE= 0.002), DW = 1.17 (MLBM(1985–1998))

<USA>

E112 UNEMPR< USA >

= −0.5412+ 0.0250(1.99)

∗LOG(LCLF ∗ HOW)

− 0.0935(−0.47)

∗LOG(GDP#.1/GDP#.2)

+ 0.0761(0.73)

∗LOG((WSEI.1/CPI.1)/LPI.1)

−0.0636∗ LOG(GFCF#.1/GDP#.1),

R ∗ R = 0.6143 (SE= 0.008), DW = 0.65 (MLBM(1985–1998))

<France>

E113 UNEMPR< FRA >

= −3.3116+ 0.1866(6.29)

∗LOG(LCLF ∗ HOW)

− 0.2892(4.68)

∗LOG(GDP#.1/GDP#.2) + 0.0169(3.77)

∗LOG(WSEI.1/LPI.1)


− 0.0883(5.19)

∗LOG(GFCF#. 1/GDP#. 1),

R ∗ R = 0.9682 (SE= 0.003), DW = 1.69 (OLS(1980–1998))

Exports:<Exports from Japan to USA>

E142 LOG(E#MAT< JPN, USA >)

= −4.6082+ 0.6624(1.04)

∗LOG(GDP#< USA >)

− 1.6411(5.97)

∗LOG (PES< JPN> .1 ∗ FERSI< USA > .1/CPI < USA > .1)

+ 0.4377(1.30)

∗LOG(SUMT4(RD#< JPN> .1),

R ∗ R = 0.9834 (SE= 0.161), DW = 0.5182 (MLBM(1976–1999))

<Exports from USA to Japan>

E143 LOG(E#MAT< USA, JPN>)

= −7.3445+ 1.2079(9.31)

∗LOG(GDP#< JPN>)

− 0.3851(−2.08)

∗LOG(PES< USA > .1/PESAME.1)

− 0.3008(−3.08)

∗LOG(PES< USA > .1 ∗ FERSI< JPN> .1 ∗ (1 + CTR@

< JPN> .1 + NTB@ < JPN> .1)/CPI < JPN> .1)),

R ∗ R = 0.9924 (SE= 0.050), DW = 1.91 (OLS(1976–1999))

Private consumption expenditures:<Japan>

E152 LOG(CP#< JPN>)

= 7.7366+ 0.4227(4.13)

∗LOG (DFI#− (TPI#+ TYC#))

− 0.3054(−0.38)

∗LOG(CPI/(CPI+ CPI.1(2.63)

)/2)) − 0.2757∗ LOG(ICC.1)

+ 0.0786(1.54)

∗LOG(MTD.1 + SMV.1)/CPI.1),

R ∗ R = 0.9975 (SE= 0.056), DW = 0.52 (MLBM(1973–1998))

<USA>

E153 LOG(CP#< USA >)

= 3.1230+ 0.5588(9.29)

∗LOG(COMPE#− TPI#)+ 0.1430(2.94)

∗LOG(OS#− TYC#)


− 0.1049(−0.26)

∗LOG(CPI/(CPI+ CPI.1)/2) + 0.1167(4.20)

∗LOG(SMV.1)/CPI.1),

R ∗ R = 0.9975 (SE= 0.011), DW = 2.33 (OLS(1981–1998))

Non-housing investment:<Japan>

E162 DOT(NHING#< JPN>)

= −0.6170+ 3.4881(2.64)

∗((OS#.1 − TYC#.1)/NHFCS#.1 − IP.1/100)

+ 0.4182(2.36)

∗DOT(ETFOB#.1) + 1.0717(3.77)

∗DOT(RD#.1)

+ 1.0126(2.52)

∗(ITI#.1/NHI#.1),

R ∗ R = 0.8479 (SE= 0.041), DW = 1.998 (OLS(1981–1997))

<USA>

E161 DOT(NHI#< USA >)

= −0.1713+ 1.5150(1.76)

∗((OS#.1 − TYC#.1)/NHFCS#.1 − IP.1/100)

+ 0.2770(1.63)

∗DOT(ETFOB#.1) + 0.2309(0.65)

∗DOT(RD#.1)

+ 0.6138(2.16)

∗(ITI#.1/NHI#.1),

R ∗ R = 0.9031 (SE= 0.022), DW = 2.07 (OLS(1986–1998))

Fixed investment:<China-Mainland>

E163 NHI#< CHN >

= 3998.2 + 0.2682(5.74)

∗ GDP#.1(0.37)

+0.0718∗ ETFOB#.1 ∗ PES.1/PMS.1

+ 1.669(3.70)

∗FCI.1/PMS.1),

R ∗ R = 0.9957 (SE= 0.9985), DW = 1.176 (MLBM(1980–1999))

Foreign exchange rate:<Japanese Yen against US dollar>

E803 LOG(FERSI< JPN>)

= 0.1353+ 1.9332(10.84)

∗LOG(PGDP.1/PGDP< USA >)


− 0.2845(−3.06)

∗LOG(ESMAT < JPN.USA > .1/ESMAT < USA, JPN> .1)

+ 0.0325(1.30)

∗LOG(IC < USA > .1/IC.1) + 0.8318(7.04)

∗LOG(FERIEUR),

R ∗ R = 0.9709 (SE= 0.076), DW = 1.93 (OLS(1980–1999))

4. The projections of the world economy in the global interdependence up to 2010

The 21st century will be an age of integrated technology innovations in the fields ofinformation technology, biotechnology, new energy as solar and superconductor, nanotech-nology, robotics, new materials, space-technology, etc. On the other hand, it is expectedthat this century will be an age of terrorism and refugees. Therefore, we cannot predictfutures of the world economy, because the futures would have certain degrees of “fluctu-ations” that we might depict the futures as either optimistic or pessimistic images. Undersuch circumstances, we would be better to start with the baseline projections of the worldeconomy using the FUGI global modeling system.

The baseline projections mean that what will be most likely futures, if the structuralparameters of the model, estimated from the past data covering latest information, wouldnot be drastically changed. Because of advanced modeling technology, we can efficientlycarry out the baseline projections by only one person using a personal computer. Everyday,we input new information to modify theinitial conditions (CRS file), so that the baselinesprojection might very swiftly accommodate with ever changing the world economy. Itis worth noting that the future of the world economy is not determined by destiny butcould be changed by policy co-ordination. This is why we have designed the FUGI globalmodeling system not only to make thebaselineprojections but alsoalternative policyscenario simulations.

According to the baseline projections (at the time of 27 July 2004), the world econ-omy is expected to be 2.6, 3.5 and 3.0% during the periods 2001–2005, 2006–2010 and2001–2010, following an average growth of 2.6% during the 1990s. For the developed mar-ket economies taken as a whole, the average annual real economic growth rate will be 2.1%during the 2001–2005 and is expected to recover to an average 3.0% in the following period,2006–2010. The US economic growth rate declined from 3.7% in 2000 to 0.9% in 2001 and1.9% in 2002, because of double shocks of stock market crash and 11 September incident.It is worth noting that the US economy was suffered from the war in Iraq in the beginningof 2003, but is expected to attain a high growth rate of 3.0% in 2003 and around 4.4%in 2004 because of an increase in defense expenditures against terrorists and tax cutbacksas well as increased research and development expenditures on new technology frontiers.For the period from 2001 to 2005, the annual average percentage growth rates of the USeconomy will be 2.7% and sustain 3.5% in 2006–2010. Although the Japanese economy,simultaneously, declines to the low growth of 0.4% in 2001 and minus 0.3% in 2002, the realGDP growth rates are expected to recover to 2.4% in 2003 and around 3.4% in 2004 by theutmost efforts of private sectors and increased exports in the upward trends of the businesscycles under the restriction of decreasing public investment. It is expected that the Japaneseeconomy will maintain yearly average 1.7% in 2001–2005 and attain 2.4% in 2006–2010


On the other hand, the EU economy as a group is expected to sustain 1.7% in 2001–2005and 2.6% in 2006–2010 as a result of creating EURO-zone in 2000 and enlargement of EU.

In the developing economies, the average growth rate of 3.1% in the 1980s was accel-erated to an average 4.6% in the 1990s. The slowing down of real economic growth in thedeveloping countries as a group may be anticipated, with the average annual growth rate of4.0% for the period, 2001–2005, sustaining 4.7% in 2006–2010.

It is worth noting that the average annual growth rate of the developing economies in theAsian-Pacific region is expected to fall from 6.5% in 1991–2000 to 5.4 % in 2001–2005 butto recover to 5.9% in the following periods, 2006–2010. The Chinese economy recordeda high average annual economic growth of 10.1% during the period 1991–2000, but isexpected to maintain its high growth toward the end of the coming decade. The ChineseGDP growth rate of an average 8.2% in 2001–2005 is expected to maintain the high growthrate of 7.9% in 2006–2010.

The baseline projections of the world economy, in particular, the Japanese economy seemlikely to be rather optimistic. The futures of the Japanese economy will largely depend uponthe increasing global interdependence. If geographical or global risks would happen, thefutures of the Japanese economy should be turned into pessimistic.

For further reference, figures are shown on real GDP growth rates (Fig. 1A–E), oil prices(Fig. 2), exchange rates of EURO (Fig. 3A) and Yen (Fig. 3B) per US dollar as well asinterest rates as short-term money market rates (Fig. 4A) and long-term government bondyields (Fig. 4B).

It is worth noting that these projection figures are changing day by day so that we mightcarry out forecast simulation exercise everyday in the uncertainty world. The FUGI globalmodeling system has been developed in order to provide up-to-date global information to thehuman society very quickly for the purpose of global cooperation and policy coordinationamong the countries around the world under uncertain futures of global interdependence.

5. Alternative scenario simulations: policy exercises

5.1. Assessment of geographical risks: impacts of the war in Iraq on the globaleconomy

The FUGI global modeling system (FGMS200) plays a significant role to make forecastsimulationsvery quicklywithin a very limited time span in accordance with the rapidlychanging interdependent global economy.

First of all, the baseline projections mentioned above have been modified in accordancewith the latest information and data such as current trends in the US economy. The baselineprojections of the world economy include possible impacts of the US large scale tax cutpolicy against the recession that might be induced by war in Iraq on the world economy.The cutbacks of interest rates of the US FRB in 2001–2003 have been also incorporatedin the baseline projections. It is expected that the US could play a major role of engines tostimulate the world economy in 2003 and thereafter. Under such circumstances, the authorhas made a global model simulation on possible impacts of the US war against Iraq on theworld economy, if the US large scale tax cutbacks were not carried out.


Fig. 1. (A) Projections of World Economy 2004–2010—real GDP growth rates. (B) Projections of World, EU,Japanese and US Economy 2004–2010—real GDP growth rates. (C) Projections of EU Econony 2004–2010—realGDP growth rates. (D) Projections of Asian Economy 2004–2010—real GDP growth rates. (E) Projections ofeconomies of Africa, Latin America and Eastern Europe, 2004–2010—real GDP growth rates.


Fig. 1. (Continued)

5.1.1. Scenario A-1It is assumed that the US should attack on Iraq in the early of 2003 and the major war

might be shortly ended within 3 months:

(1) The Iraqi economy should be seriously damaged and its real GDP growth rate mightbe minus 50% in 2003, although recovery process might start in the succeeding years.


Fig. 1. (Continued).

Fig. 2. Projections of oil export price: 2004–2010.


Fig. 3. (A) Projections of exchange rates of EURO per US dollar: 2004–2010. (B) Projections of exchange ratesof Yen per US Dollar, 2004–2010.

(2) It is assumed that oil prices increase by $ 5 per barrel in 2003 com-pared with those of the baseline and decrease thereafter toward the baselinelevels.

(3) On the other side, the war in Iraq might induce US stock exchange market fluctuationsand the non-housing investment of the US might be decreased by 5 percentage points


Fig. 4. (A) Projections of money market rates of EURPO, Japan and USA: 2004–2010. (B) Projections of gov-ernment bond yields of EURO, Japan and USA: 2004–2010.

compared with those of the baseline in 2003, because of psychological changes inentrepreneur’s minds induced by increased uncertainty over the futures. The US FRBmight cutback discount rate by 0.25% coping with the recession of the US economy in2003, although it is reasonably expected that both rates would rise toward the year of


2010 in accordance with the US economic recovery. This scenario simulation exercisemeans the minimum risk for the US attack on Iraq.

According to this scenario, the world economic growth would be lowered by 0.31 per-centage points below the baseline in 2003. The economic growth rates of the developedmarket economies as a group would be oppressed by 0.35 percentage points. Because theUS real GDP growth rates would be decreased by 0.84 percentage points compared withthose of the baseline in 2003, this negative impacts would induce the slowing down ofeconomic growth rates in the other developed market economies such as Canada (−0.45),Japan (−0.05), France (−0.04), Germany (−0.04), Italy (−0.03), UK (−0.06) and Sweden(−0.04).

The economic growth rates of the developing countries as a group would be decreasedby −0.48 percentage points compared with those of the baseline, because of the negativegrowth rate of the Iraqi economy to large extent.

5.2. The US strategy against geographical risks: tax cutbacks

5.2.1. Scenario A-2The Scenario A-2 is a policy exercise on the US strategy against geographical risks to

be induced by the war in Iraq in 2003.As a matter of facts, the US has introduced the positive fiscal and monetary policy against

the possible recessions that might be induced by the war in Iraq. The US introduced thelarge scale income tax cutbacks coupled with both FRB discount rate and FF rate cut downto the minimum level coping with the possible recession of the US economy in the earlyof 2003. The results are very clear now. The US economy maintains the high growth in2003–2004 and seems likely to prevent the possible geographical risks in the economicterms. In order to make assessment of impacts of the US policy, it is assumed that the USwould introduce large scale tax cutbacks (around US$ 135 billion per annum) starting from2003.

According to this scenario, the world economic growth would be increased by 0.27 and0.40 percentage points in 2003 and 2004, respectively. The economic growth rates of thedeveloped market economies as a group would be increased by 0.34 and 0.51 percentagepoints. Because the US real GDP growth rates would be increased by 0.87 and 1.22 per-centage points in 2003 and 2004, respectively, this positive impact would induce the raisingup economic growth rates in the other developed market economies such as Canada (0.46,0.68), Japan (0.05, 0.12), France (0.03, 0.06), Germany (0.04, 0.10), Italy (0.03, 0.06), UK(0.05, 0.08) and Sweden (0.03, 0.06). The economic growth rates of the developing coun-tries as a group would be increased by 0.07and 0.10 percentage points in 2003 and 2004,respectively. This is particularly true in Southeast Asia (0.12, 0.18) and Latin America (0.14,0.20).

According to the FUGI global model simulations, the US economy will enlarge twindeficits of both the government budget and current balance of payments for the period,2003–2004. It is worth noting, however, that the US government budget deficits will beturned into surplus after 2007, if the government would restrain expenditures, because ofthe high growth performance derived from the tax cut policy. This is a paradox of taxcutbacks.


5.3. The US dollar depreciation against EURO and Yen

According to the baseline projections, the US economy will maintain deficits of currentbalance of payments and the exchange rates of US dollar tends to be depreciated againstEURO and Yen for the period, 2003–2010. It is interesting to note that Euro zone economyseems likely rather stronger against Euro appreciation against the US dollar compared withthe Japanese economy facing Yen appreciation against the US dollar. The export pricesfrom EURO zone are not only expressed in terms of EURO but also some export goodsfrom EURO zone have non-price competitiveness as brand images. Furthermore, the tradein EURO zone has larger weights. On the other side, EURO appreciation seems likelyto restrain from increasing rates of both domestic prices and interest rates. On the otherhand, the Japanese economy might be weak against a drastic Yen appreciation againstthe US dollar. Because the Japanese economy is largely interdependent on the US and itsexport prices to the US are mostly expressed in terms of the US dollar instead of Yen,the Japanese export industries will be suffered from sharp appreciation of Yen against theUS dollar. The Japanese economic growth rates should be minus if Yen will face all ofa sudden appreciation beyond 80 (−0.7%) and Yen 70 per dollar (−2.1%). Under suchcircumstances, the US, in particular, manufacturing industries will reasonably expect toimprove export price competitiveness, but could not increase exports towards Japan wheredomestic demands should be shrinking. As a matter of facts, there should be internationalpolicy coordination coping with drastic fluctuations of US dollar exchange rates againstEURO and Yen, if Yen exchange rates per US dollar would overshoots beyond a currentdefense line of Yen 100 per US dollar.

As matter of fact, all of a sudden sharp depreciation of the US dollar against major cur-rencies might induce the US oriented international financial turbulence that should threatenthe sustainable development of the global economy. In order to evade such a global risk,strategy against the Japan syndromes should also be needed.

5.4. Strategy against the Japan syndromes

5.4.1. Scenario A: self-reliance of private sector—this scenario corresponds to thebaseline scenario

Everyone knows the needs for the structural reforms of the Japanese economy in or-der to avoid increasing new government bond issues and bad loans. In order to copewith such financial issues, the Japanese government has started with introducing severeinterference toward the financial institutions and the stringent fiscal policy. Results arevery clear as the FUGI global model simulation has predicted (see JPM, 2002). TheJapanese economy has got into serious deflation for the period, 2001–2002. Because ofdeflationary pressures, the government budget revenues, in particular, corporate incometax revenues have been decreased and then new government bond issues have been ratherincreased.

Since Japanese economy has been suffered from stagnant growth rates for the period,2001–2002 because of the stringent fiscal policy, the private corporations seem likely tobecome psychologically much stronger against continual shocks suffering from a trap ofslower domestic demand growth rates.


It is expected that the Japanese economy will recover on account of increased exports,in particular, to the US and China as well as improvements of the US and Japanese stockexchange market prices for the period, 2003–2010. Under such circumstances, the privateenterprises, in particular, imposed by severe survival games have made greater efforts byrestructuring to create profits for enlargement of R&D and investment in order to survivein the world market. It is worth noting that over 20% of the Japanese enterprises mostlyconsisting of the global corporations could be succeeded in getting striking large profits andexpanding R&D oriented investments in 2003 and thereafter.

5.4.2. Scenario B: tax cut policy exercisesIt is assumed that personal income tax cutbacks by Yen 5 trillion and corporate income

tax cutbacks by Yen 5 trillion, respectively, on the permanent basis would be introducedfor activating domestic demands as private consumption and investment for the period,2004–2010.

It is worth noting that the Japanese annual average economic growth rates will be in-creased by around 0.9 percentage points for the period, 2004–2010 compared with thoseof the baseline. It is interesting to note that the growth rate will be 1.6 percentage pointshigher than those of the baseline in 2010, even if consumption tax rates would be raised by1% per annum from 5% in 2005 to 10% in 2010.

Immediately after this announcement, Japanese stock exchange market will be vitalizedand then steadily going down of commercial land prices will be overturn. The increasedfinancial and immobile assets values will be so increased that bad loan issues might beimproved. Then we can reasonably expect that the Japanese economy will be vitalizedto increase R&D for technological innovations in new frontier industries and realize thesustainable development. Higher nominal GDP growth rates will bring about greater generaland local government revenues in the longer run. Under such circumstances the FUGI modelsimulations can reasonably predict to decrease the outstanding government loans by thesteady structural reforms in order to evade the wastages of financial resources. This is aparadox of the tax cutbacks(2002b).

It is hoped that the strengthened Japanese economy by new technology innovations underthe structural reforms to create efficient both private and public organizations will contributeto the global economy through expansion of trade, private foreign direct investment, ODAand transfer of technologies, etc. in order to cope with poverty as well as geographical andglobal risks in the 21st century.

6. Conclusion

The FUGI global modeling system (FGMS200) has been developed as a scientific toolof policy simulations for providing global information to the human society and find-ing out possibilities of policy coordination among countries in order to achieve sustain-able development of the world economy. It is worth noting that FGMS200 can operatethe FUGI global model 9.0 M200PC using a personal computer (Windows 2000/xp pro-fessional). The development of both hardware and software systems of high-technologycomputer has supported FUGI global modeling. The FUGI global modeling system


represents a new frontier in economic science stimulated by information technology and lifescience.

Metaphorically speaking, the world’s 200 countries including the UN and non-UN mem-bers can be thought of as “cells” which, when separated and isolated should only act in adisconnected way, each in its own fashion. But when given information concerning theglobal human society, the possibility arises that each country can take in information onwhat it ought to best to do, with the result that through a sort of feedback system theglobal economy will operate more smoothly. This is one of the hints given by the recentdevelopment of life science.

The transmission of information is an important aspect of life functions, absolutelyessential for the existence and continuation of life. Humans furthermore have a capacity bywhich information is consciously perceived as signals from outside as a result of which newand useful information can then be generated from inside “human genome” information,taking on, in other words, takes on aself-organizing capacity. There are vital characteristicsof the life phenomenon.

The FUGI global modeling methodology has also received a large impact from ofbrainphysiology. The human brain is made up of around 6 billion neurons, or nerve cells. The“right brain” has to do with what we call “pattern recognition”, and specializes in the abilityto grasp “images” and perceive things as a totality. The “left brain” displays an outstandingcapacity to think logically in terms of symbols and words.

A thick belt of the inter-brain ridge links the information inputs handled by the left andright brains. Images taken in by the right brain are sent to the left brain, where they arelogically analyzed and checked out to see if they correspond with reality, and are then fedback again into the right brain. In this way, the brain can make judgments and produce newinformation.

The working of the human brain, in which the neurons not only form a networkthrough synapse but recognize each other, has provided some very useful hints for theconstruction of global models. This is because global models have the role of offeringglobal information. If this information undergoes a feedback process reflecting itself ineach country’s actual policies, the future image of the world economy will change withthe emergence of global information. In the future, mutual understanding can be ex-pected to increase through global information exchanges, and it is only with this basethat we can begin to talk about possibilities forinternational cooperation and policycoordination.

The functioning of the individual cells that support human life depends on both geneticinformation and non-genetic information generated through “creative” endeavors such aslearning. It is still in the future for a global model to be developed that will in fact havea similar capacity for “self-organization”. Perhaps we will first have to develop a globalsystem that will be conducive to the employment of such an avant garde model. But in thisprocess, it may nevertheless be expected that current global models can give useful policysuggestions.

An important phenomenon discovered through research in biotechnology is the so-called“fluctuation phenomenon”. It may be appropriately said that the presence of fluctuationsseems to be a basic and necessary element for the evolution of life. And again, this is avery important element in thinking about the global economy. Forecast simulations based


on present baseline scenarios accommodate a large degree of “fluctuation” in light of thecurrent unstable situation.

At the same time, there is of course the possibility of controlling this situation andchanging its course through more energetic international policy coordination, or, in theterminology of biotechnology, “dynamic cooperation” among countries. There are indeedmany kinds of possibilities for invigorating the global economy, raising its growth rate,greatly reducing world unemployment, and promoting innovations opening up new 21stcentury frontiers.

By demonstrating these possibilities through future simulations using the latest FUGIglobal modeling system, we can exercise alternative policy scenario simulations for theglobal economy and can offer suggestions to those responsible for policy-making in theworld’s various countries. In keeping with these new concepts, it will no doubt advanceto new frontiers in economic science, while keeping much of its heritage of traditionaleconomics. We ought to actively pursue this vision innew frontier of economic science,and in this regard we see FUGI global modeling system as one of the important intellectualchallenges in the 21st century.

Appendix A

Projections of global economy for the period, 2001–2010

Annual average growth rates of real GDP (at 1995 prices) Unit: (%)

1991–1995, ac-tual

1996–2000,estimate


2001–2010,projection

2006–2010

2001–2010

World 2.0 3.2 2.6 2.6 3.5 3.0Developed market economies 1.7 3.0 2.3 2.1 3.0 2.6

Developed Asia-Pacific 1.6 1.6 1.6 1.9 2.6 2.3Japan 1.4 1.4 1.4 1.7 2.4 2.1

North America 2.1 4.3 3.2 2.7 3.5 3.1United States 2.2 4.4 3.3 2.7 3.5 3.1

Western Europe 1.3 2.6 2.0 1.7 2.5 2.1EU 1.3 2.7 2.0 1.7 2.6 2.1

Austria 1.9 2.3 2.1 1,7 2.9 2.3Belgium 1.3 2.8 2.0 1.4 2.7 2.1Denmark 2.5 2.8 2.7 1.9 3.0 2.5Finland −0.5 4.8 2.1 3.0 2.7 2.9France 1.1 2.8 1.9 1.6 2.3 1.9Germany 1.4 2.1 1.8 0.9 2.6 1.8Greece 1.2 3.4 2.3 3.9 1.9 2.9Ireland 6.1 10.1 8.1 5.8 6.8 6.3Italy 1.1 1.7 1.4 1.4 1.9 1.7Luxembourg 5.4 5.5 5.5 2.9 3.6 3.2Netherlands 2.1 3.6 2.8 1.3 2.6 2.0Portugal 1.8 3.4 2.6 0.8 2.9 1.9Spain 1.3 3.6 2.4 2.7 2.9 2.8Sweden 0.5 3.2 1.8 1.8 2.1 2.0United Kingdom 1.3 29 2.1 2.3 2.3 2.3

Euro Area 1.3 2.6 2.0 1.5 2.5 2.0Others 1.1 2.1 1.6 1.4 2.7 2.1


Appendix A (Continued)

Annual average growth rates of real GDP (at 1995 prices) Unit: (%)

1991–1995, ac-tual



2001–2010,projection

2006–2010

2001–2010

Developing Economies 4.9 4.2 4.5 4.0 4.7 4.3Asia-Pacific 7.6 5.3 6.5 5.4 5.9 5.7

East Asia 8.7 6.3 7.5 6.0 6.2 6.1China: Mainland 12.1 8.4 10.2 82 7.9 8.0

Southeast Asia 7.2 3.2 5.2 4.1 5.5 4.8Indonesia 7.8 0.6 4.1 4.3 5.8 5.0Malaysia 8.7 4.2 6.4 4.0 5.3 4.7Philippines 2.2 3.5 2.8 3.9 3.9 3.9Singapore 9.1 6.3 7.7 2.7 5.6 4.2Thailand 8.6 0.6 4.5 4.2 6.0 5.1

South Asia 5.2 5.3 5.2 5.4 5.7 5.6India 5.3 5.7 5.5 5.8 6.0 5.9

Middle East 1.6 2.7 2.1 2.4 2.9 2.7Africa 1.5 3.5 2.5 2.8 2.9 2.9

North Africa 1.8 4.1 2.9 3.0 2.4 2.7Sub-Saharan Africa 1.3 3.2 2.3 2.7 3.2 3.0

Latin America and the Caribbean 3.6 3.0 3.3 2.0 3.1 2.5Brazil 3.1 2.3 2.7 1.9 2.2 2.1

Mediterranean 1.2 3.9 2.5 3.4 3.9 3.7

Economies in transition −6.9 1.1 −3.0 4.5 4.7 4.6Eastern Europe −0.6 3.2 1.3 3.4 4.4 3.9CIS (Former USSR) −9.8 −0.3 −5.2 5.3 4.9 5.1

Russian Federation −9.1 −1.0 −5.1 6.0 5.5 5.8

Notes: Pre-projection was made for the period, 1995–2003. Projection periods are 2004–2010. Source: FUGIglobal modeling system (FGMS200).

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AkiraOnishi is Director, Centre for Global Modeling, Professor Emeritus, former Vice President, Soka University,Economics and Global Modeling Educator. His academic background is both economics and systems engineering.He got Ph.D. in Economics from Keio University and Ph.D. in Engineering from Tokyo Institute of Technology.He had an opportunity to work at the United Nations ESCAP and the ILO, 1966–1970. Then he has served atSoka University, Tokyo; Dean, Department of Economics, 1976–1991; Dean, Graduate School of Economics,1976–1991; Director, Soka University Institute for Systems Science (SUISS), 1990–2001; Dean, Faculty of En-gineering, 1991–1995; Dean, Graduate School of Engineering, 1995–1999; Vice President, 1989–2001; Visitingprofessor, Westminster Business School, 2002. He served as President of Japan Association of Simulation andGaming, 1993–1997. He received many academic awards. The International Biographical Roll of Honor to theGlobal Modeling Profession from American Biographical Institute, USA, 1989; The first Supreme Article Awardfrom the Japanese Association of Administration and Planning, 1991; The 20th Century Award for Achievementfrom the International Bibliographic Centre, Cambridge, England to Global Modeling, 1993; The Excellent ArticleAward from ECAAR, 1997; The Japan Assn. Simulation and Gaming Award, 1998; 2000 Outstanding Intellec-tuals of the 20th Grand from the IBC, 1999. He was selected as First 500 in 2000 for the service to Economicsby the IBC. He has made a great contribution to global modeling through numerous articles and conferences. Heis well known as an Original Designer of FUGI (Futures of Global Interdependence) global model. The UnitedNations Secretariat, Department of International Economic and Social Affairs adopted this model for the long-termprojections and policy simulations of the world economy from 1981 to 1991. During the period, 1985–1986, hedesigned the Global Early Warning Systems for Displaced Persons (GEWS) under the auspices by the UnitedNations Independent Committee of Human Rights. SeeOnishi (2003b)FUGI global model for early warning offorcedmigration(http://www.forcedmigration.org), Forced Migration Online, Refugee Studies Centre, Universityof Oxford. The UNCTAD has officially adopted the FUGI global model for the projections of the world economyand policy scenario simulations since 2000. He served as an Honorable Theme Editor of Integrated Global Modelsof Sustainable Development in Encyclopedia: EOLLS, UNESCO for the period 1998–2003. SeeOnishi (2003a),FUGI global model 9.0 M200,Integrated Global Models for Sustainable Development, UNESCO Encyclopaediaof Life Support System, EOLLS Publisher, Oxford, UK, 2003 (http://www.eolls.net). The latest FUGI model asan integrated global model can not only provide global information on the sustainable development but displacedpersons or refugees that might be seen as serious global issues in the 21st century.



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