Research Challenges for Information Society∗ Marek Makowski International Institute for Applied Systems Analysis, Laxenburg, A-2361 Austria http://www.iiasa.ac.at/∼marek e-mail:
[email protected]
Abstract The paper summarizes selected problems related to the fast proliferation of networked computing which is recognized as a beginning of the information era, and the corresponding future needs of Information Society. One domain of new research challenges pertaining to the needs of Information Society, namely modeling is discussed in more detail. Keywords: Information Society, the internet, information, modeling, multi-criteria model analysis.
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Introduction
Computing and access to the internet are no longer domains of scientists working in few research fields that require intensive computations and therefore have been and continue to be the driving force for the development of high performance computing hardware and software. Such scientists used to be the only intensive users of computing resources for the first about three decades of the existence of computers. Recently, computers are used almost everywhere and computing literacy belongs to basic education. Wide availability of affordable hardware (including an easy access to the internet) combined with the liberalisation of telecommunication will soon make Information Society (IS) a reality. However, what this reality will look like depends to a large extent on understanding the current situation and the appropriate actions that should be taken in order to exploit the opportunities provided by science and technology to the benefit of societies.
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Information Society today and tomorrow
2.1
Current situation
How much research depends on computing is best illustrated when a network is down. Such rare situations clearly show that the internet and intranets have become a backbone of almost all activities in most research institutions. The following elements illustrate the use of the network in research: ∗
To be published in the Proceedings of the Conference “Research for Information Society” by the Institute of Telecommunication, Warsaw, Poland.
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• More and more collaborative work is done via the internet. For example, computing is done in remote locations, data used for models are prepared, processed and verified, papers are prepared by authors using the internet, e-mail replaced to a large extent traditional letters. • Electronic publishing is replacing traditional publishing. Practically all papers are available in electronic form. Many journals are available in electronic (and thus cheaper) versions. Some research institutions (e.g. IIASA) have stopped to provide hard copies of reports and of internal memos, electronic signatures are becoming more popular; thus electronic versions of scientific papers and of documents gradually replace hard-copy versions. • Conferences are being organized by using Web-enabled applications directly linked with data bases1 . This replaces the traditional way of organizing scientific meetings, thus greatly improving efficiency and resulting in substantial savings, not only of money but also of time of the organizers and participants. • The recently developed DecisionNet by Bhargava, Krishnan and Mueller (1997) illustrates capabilities of configurable Web-based systems that can be customized to meet specific modeling needs. DecisionNet adopts an electronic market-based approach to model based decision support wherein various elements (such as data, models, solvers) are offered by various providers on demand. Networked computing has recently not only substantially influenced the research but also business and leisure time. Following examples briefly illustrate this trend: • The number of the internet users increased during the last four years from the estimated 3 millions to about 100 millions. The internet traffic (measured in time on-line) doubles recently every 100 days. • E-commerce is by far the fastest growing type of business. National Panasonic applied several years ago virtual reality to the design of a kitchen (obviously using their appliances), and at this time this was one of very few direct applications of computing to commerce. Nowadays, one can not only do various “standard” shopping by the internet, but also cars can be assembled according to the specification worked out between the customer and the dealer; electronic banking is becoming more popular (for example, CityCorp in Warsaw provides and manages credit cards without personal contact with the customers. Considering the advances summarized above one must not forget the serious problems and limitations. Many of them are presented in other papers in this volume, therefore, we provide only one example. During the rescue action after the accident in the mine in Lassing (Austria) in Summer’98 it was impossible to quickly find information about an appropriate equipment in the available data bases. It turned out later that this information was widely available, hence the problem was “only” the lack of an appropriate organization of data bases which are available to rescue teams. This is just one more argument to show that we have a long way to go before all critical information will be properly organized.
2.2
Future development of hardware
One should learn from history and consider what has been known and forecasted 30, 20 and 10 years ago about computing hardware. In order to illustrate this problem, we 1
Examples of such an approach can be found at URL: www.iiasa.ac.at/~marek.
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1991 Memory 16-64 MB CPU speed 20 MIPS Disk space 200 MB Price 10000$ Network throuput 10 MB/sec
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1996 64-256 MB 200 MIPS 1 GB 3000$ 100 MB/sec
2001 256-1 GB 1000 MIPS ?? below 1000$ 1GB/sec
Table 1: Forecast (made in 1991) of capabilities of a typical workstation.
present in Table 1 a summary of a panel discussion held in 1991 concerning the future of computer hardware, see Makowski (1994). The participants of the discussion knew that the hardware forecasts had been too pessimistic, therefore they gave rather optimistic predictions. Data given in this Table 1 characterize the forecast of the computing capabilities corresponding to a typical workstation in 1991. The prices in Table 1 do not refer to a typical workstation, but to a configuration with computing power equivalent to the 1991 configuration. Many researchers who were confronted with this forecast in 1991 did not consider it realistic. Today, the development of hardware seems to be even faster than forecasted seven years ago; capabilities and prices of a PC today outperform those forecasted for a workstation just seven years ago. Certainly, the development of the hardware (including network) will not be a bottleneck for the IS.
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Needs in the near future
Mobility of people and goods will increase considerably (it has doubled in Europe in 19751995 and will double again until 2025). This will call for new methods of the organization of work (including teleworking) and of production, as well as economic, safe and environmentally friendly transportation systems (the latter being already recognized as one of the main EU challenges). For example, there are about 4 millions teleworkers in the EU today (which is about 3% of the workers). However, teleworking is not equally popular in all EU countries (about 33% of teleworkers are in the UK). This will continue to grow fast: the EU estimates over 10 mln teleworkers and 40% of business to be trading electronically in the year 2000. This will not only greatly increase the demand for various services and for teleworking. Advanced technologies for accessing, filtering and analyzing information (including technologies for the representation, creation and handling of knowledge) are needed for an efficient use of the explosion of information. Therefore the IS will not only need networked computers (including advanced ultra-high performance computing and advanced high-flow networks), but also well organized and reliable information which should include computerized models of various problems that need to be analyzed not only by administrations, industry and scientific community, but also - to a fast growing extent - by individual citizens. There will also be a demand for an intelligent access to the results of research. One of the most efficient channels of such an access can be an active analysis of various analytical models, e.g. by creating and analyzing scenarios (contrary to reading reports).
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Risks and pitfalls
IS creates a huge market for services, software and hardware. On the other hand, there is a well recognized general trend (often referred to as “megamania”) to fuse small companies into bigger ones. This in turn creates several risks and pitfalls for societies. One of them is well characterized by a story due to Quinn (1998): If I were the head of Gas & Electric, the first thing I would do is to declare that we sell energy systems, not power, and that customers tell us that they want a familiar energy environment wherever they go. The first step would be to integrate a smart fridge into the overall energy system as it is the first appliance opened by most users and real-time monitoring of beer temperature increases satisfaction with the energy environment for 78% of all customers. Customers would be free to use other fridges, even making someone else’s their default appliance. However, if they try to remove the Gas & Electric fridge their TV and air conditioner might not function properly. When a circuit fails in an older home we would repair it with a service pack that also installs our fridge, eventually introducing all customers to our energy environment...
The story was cut due to the space limitation but even its first part makes it clear how dangerous a de facto monopoly in a domain of the information technology can be. The history of evolution clearly shows that a variety is a must for survival and this should also apply to assuring continuation of variety in services, software and hardware providers for the IS. Another negative effect of the megamania is the concentration of the research. Large multinational companies tend to concentrate R&D and therefore they typically close smaller research centers, especially if they belonged to the bought companies. While the concentration of the research is justified by the economy of scale, it results in substantial decreasing of research opportunities in some countries which in turn has a negative effect not only on the local labour market but also on higher education (because a demand for application driven research is one of the main driving forces for the quality of universities). Finally, the last problem we want to point out is the availability and quality of information. On the first glance this should be no problem because the internet provides technical possibilities for “posting everything”. However, the already huge and still growing amount of information that can be posted by practically everybody creates a number of problems. We flash only two of them: • Before the information era there used to be a natural mechanism for filtering information. This obviously had well known negative effects. However, the flood of information creates other problems for most of the internet users, namely lack of efficient mechanisms for selection of the needed (and not biased by providers) information and a verification of its quality. • Unfortunately, the internet provides also new opportunities for various criminal activities such as access by children to pornography, proliferation of criminal knowledge (e.g. detailed instructions for constructing bombs), invasion of privacy, misuse of intellectual properties. The problems tackled in this section are just a small subset of all problems that require fast and efficient international cooperation in order to implement measures which can assure a difficult balance between the freedom to distribute and access of information on the one hand, and on the other hand the negative effects that such an unlimited freedom will certainly cause to all ISs.
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Research challenges
There is a broad spectrum of research challenges prompted by needs of IS which are outlined in Section 2.3. We will briefly discuss only one area of these challenges which are related to the failure of a proper use of the OR potential, summarized by R. Ackoff in his already classical articles (Ackoff, 1967; Ackoff, 1979). Many European research institutions develop analytical models that are of a broader interest. However, there are no standards for a model specification and analysis. Therefore, not only a lot of work is required for the development of models but also the analysis of a model is often restricted to a limited number of approaches (such as simulation, optimization, multicriteria model analysis) and requires specific expertise. Hence, models are typically used only by small groups of specialists. To understand why a standardization is a possibility, we have to recall the evolution of database management theory and technology. The data management revolution occurred in response to severe problems with data reusability associated with file-processing approaches to application development. The need to share data resources resulted in the development of the DBMS, which separates data from the applications that use data. Models could not only be developed in a much more efficient way but they could also be used in a more efficient way, if standards for model specification and analysis were agreed upon. If we think of model management as the modeling counterpart to data management, then model management is at about the same stage of evolution as data management was during its transition from file processing to database processing and - recently - to the data warehouse phenomenon. The ability to capture data from multiple operational source databases and retrieve it efficiently across many different dimensions has always been a key issue for timely delivery of useful information needed for actual decision making. The result of the comparison of data bases and analytical models clearly illustrates the challenge, namely what moved database technology forward is what we need to move model management forward: voluntary, de facto standardization around a rigorous, principled representational formalism of great generality. Standardized interfaces to a model analysis in turn would allow both institutions and individuals much broader access to a vast amount of information and knowledge that is currently available mainly for researchers and experts. Non-specialists will not learn many ways of analyzing various models but many may be willing to learn a user-friendly way of analysis of various models that can be made publicly available. Then a broad access to various models can greatly contribute to education of societies and can help in public discussions of various issues, like social security reforms, population ageing, climate change, air quality, etc. Finally, a standardization of models is a de facto must for integration of models. The importance of integration of models can be illustrated by the European air quality model by Amann, Bertok, Cofala, Gyarfas, Heyes, Klimont, Makowski, Sch¨opp and Syri (1998), which is being developed at IIASA in collaboration with several European institutions, and which is used in intergovernmental negotiations on implementation of policies aimed at improving the air quality. The implementation of such a complex model would not have been possible without the prior development of various methodologies and tools for model based decision support. Such methodologies and tools have also been developed within the long-term collaboration between several Polish research institutions and IIASA, and have been tested on various practical problems (including regional water quality management, agro-ecological land use planning, urban land use planning, various engineering applications) and are presented in (Wierzbicki, Makowski and Wessels, 1999).
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Advances in methods and tools for specification and analysis of analytical models have allowed for implementations of such models to various policy making problems for which traditional crisp optimization and/or simulation models do not offer enough support. Modern model based decision support explores a cluster of enhanced traditional methods combined with multicriteria model analysis (MCMA). MCMA is known for its advantages in examining trade-off between various conflicting criteria thus helping to identify attainable goals and decisions which lead to achieving such goals. MCMA can be considered as an important enhancement of the well known goal programming technique. However, MCMA provides also a natural way for a more comprehensive model examination, which includes techniques like soft simulation, inverse simulation and soft constraints. A public domain modular software tool for MCMA has been developed by Granat and Makowski (1998). The MCMA tool can be easily applied to an analysis of any linear or mixedinteger core model. The structure of the decision support system which uses the MCMA tool for the AEZ (Agro-Ecological Zone) core model analysis is illustrated in Figure 1.
Figure 1: The structure of a Decision Support System for the Land Use for Sustainable Agricultural Development Planning. In order to properly use various possibilities offered by modern methods of model analysis a model has to conform to certain requirements. This leads to the notion of “core (substantive) model”. A definition of a core model separates the model specification from the model analysis which provides similar advantages like those used by the DBMS, which separate data from the applications that use data. Moreover, such an approach is not only important for a proper model analysis but it also results in computational problems which are often dramatically easier to be solved than equivalent (from the mathematical programming point of view) problems that correspond to the traditional way of a model specification and solution. Such an approach has been applied to the development of several models for analysis of various policy options. The paper by Fischer, Granat and Makowski (1998) documents a model for land-use planning and provides a detailed description of the problem specific core model generator (included in the structure illustrated in Figure 1) and can be considered as a tutorial example of a core model specification and analysis.
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Conclusions
While substantial advances have recently been achieved both in in science and technology, there is still a number of research problems which need to be solved in order to support a rational use of the fast growing amount of information and possibilities offered by networked computing. In particular, a number of models that are being developed can be used much more broadly not only by researchers but also by administration and individual citizens. One can distinguish the following reasons for a more broad using analytical models (illustrated by relevant models): • Understanding what is coming and what will be the consequences, e.g., aging population, social security reform, global warming. • Understanding choices, e.g.: European air quality vs costs necessary to reduce emissions, catastrophic bonds vs taxpayers money to cope with financial consequences of natural catastrophes. • Looking for advices, e.g.: alternative allocations of individual funds for the third pillar of the social security reform in Poland. However, in order to broadly use analytical models one has to agree on a standard for a model specification and for an interface to its analysis. The introduction of such a standard for data processing resulted in a qualitative improvement of using data in virtually all organizations. Now, it’s time to make a similar jump for analytical models.
References Ackoff, R.: 1967, Management misinformation systems, Management Science. Ackoff, R.: 1979, The future of operational research is past, Journal of OR Society. Amann, M., Bertok, I., Cofala, J., Gyarfas, F., Heyes, C., Klimont, Z., Makowski, M., Sch¨ opp, W. and Syri, S.: 1998, Cost-effective control of acidification and ground-level ozone, Fourth interim report, International Institute for Applied Systems Analysis, Laxenburg, Austria. Bhargava, H., Krishnan, R. and Mueller, R.: 1997, Decision support on demand: On emerging electronic markets for decision technologies, Decision Support Systems 19(3), 193–214. Fischer, G., Granat, J. and Makowski, M.: 1998, AEZWIN an interactive multiple-criteria analysis tool for land resources appraisal, Interim Report IR-98-051, International Institute for Applied Systems Analysis, Laxenburg, Austria. Available on-line from http://www. iiasa.ac.at/~marek/pubs. Granat, J. and Makowski, M.: 1998, ISAAP – Interactive Specification and Analysis of Aspiration-based Preferences, Interim Report IR-98-052, International Institute for Applied Systems Analysis, Laxenburg, Austria. Available on-line from http://www.iiasa. ac.at/~marek/pubs. Makowski, M.: 1994, Design and implementation of model-based decision support systems, Working Paper WP-94-86, International Institute for Applied Systems Analysis, Laxenburg, Austria. Available on-line from http://www.iiasa.ac.at/~marek/pubs/. Quinn, J.: 1998, Replies to Gates, The Economist, July 25th, p. 8. Wierzbicki, A., Makowski, M. and Wessels, J. (eds): 1999, Model-Based Decision Support Methodology with Environmental Applications, Kluwer Academic Publishers, Dordrecht, Boston, London. (to be published).
