Sustainable decision making and decision support systems

Sustainable decision making and decision support systems by M. A. Hersh

Sustainable decision making stands for decision making which contributes to a sustainable society. It should take into account developmental, environmental, social, technical and economic factors. Different models of decision making are presented and discussed in the context of sustainable decision making. The appropriateness of existing decision support systems for sustainable decision making is considered. Examples from the areas of water resources and energy planning and management are presented to illustrate some of the main issues.

ustainable decision making is decision making which supports moves toward a sustainable society. It is important due to the increasing S burden human activity is placing on the natural

environment and the rapid pace of technological development, which presents challenges and opportunities, as well as the potential for disaster. Thus sustainable decision making should contribute to the developmental needs of humanity, while protecting and conserving the natural environment and not compromising the ability of future generations to meet their own needs. It should also attempt to harmonise the three sometimes conflicting policy goals of environmental integrity, economic efficiency and equity, and consider possible long-term and indirect consequences as well as short term and direct ones.

Decision support systems (DSS) are computer-based information systems which have been designed to affect (and preferably to improve) the process of decision making. Therefore supporting decision making requires understanding of the various processes involved to enable computer-based systems to be designed to support them and increase their efficiency. There are two main approaches to modelling decision making, with considerable polarisation between their supporters. The two approaches are based on classical and naturalistic decision theory. Some of the differences between them are shown in Table 1.

There is not a unique overall best decision strategy, but appropriate choice of decision strategy depends on a

number of factors. These include the importance and complexity of the decision, the possible consequences of making a ‘wrong’ decision, the experience and training of the decision maker($, the extent to which available information meets information needs and the degree of time pressure. Intuitive naturalistic approaches are most likely to be used by experienced decision makers under time pressure or in changing situations, whereas analytical classical strategies are most likely to be used with alphanumeric data, when there is an organisational need to justify decision choices, the problem is compu- tationally complex or groups with different interests have to be reconci1ed.l

Table 1 Differences between classical and naturalistic decision strategies

COMPUTING & CONTROL ENGINEERING JOURNAL DECEMBER 1998

Fig. 1 Example of a multistage decision process

This raises the question of the most appropriate approaches for sustainable decision making. This involves consideration of developmental, environmental, technical and economic factors. The classical decision approach has the drawback of requiring objective, quantitative and precise data and lacks a satisfactory framework for incorporating environmental issues or abstract and subjective concepts, such as equity, into the analysis. However, it is able to provide an organised framework for structuring all the available information, analysing a large volume of data and justifying or clarifying tradeoffs to the different interests and stake- holders involved in sustainable decision making. Naturalistic decision strategies are able to make full use of the expertise of experienced decision makers. However, this may be a drawback in the case of sustainable decision making if this expertise is based on old decision making patterns which exclude consideration of sustainability. They may be more appropriate for the types of decision that are a prelude to immediate action rather than policy formulation and decisions relating to project choice or industrial design which may be more relevant to sustainable decision making.

Many decisions are part of a series of decisions or decision processes rather than occurring in isolation.

They can be divided into a number of decision steps or components which occur sequentially or simultaneously and are rarely totally independent of each other, giving a need for multistage models of decision making. For instance a siting decision for a new power station can be decomposed into a number of steps, including decisions on the need for increased generating capacity, the size of station and the type of station, which may be taken either prior to or simultaneously with the decision on choice of site. The decision on choice of site may be dependent on the decisions on type of station and its capacity. This is illustrated in Fig. 1.

SMstainable decision making: some general principles

It is only relatively recently that there has either been concern about sustainability or the software and hard- ware available for computer-based DSS. Sustainable decision making generally involves a range of environmental, economic, political, social, ethical and other factors, in addition to considerations of cost and the quality and quantity of service provision. It requires a mixture of quantitative and qualitative, precise and imprecise, subjective and objective data. If a rank order can be obtained for qualitative data, it can generally be


converted to a numerical scale. Normalisation can then be used to integrate this data with other types of data to allow a fair comparison of data measured on different scales.

Most sustainable decisions involve a number of groups and individuals, often with very different or conflicting interests. When discrete multi-criteria decision methods (MCDM) are used the problem size can be increased, to allow (possibly weighted) values for multiple decision makers on each criteria to be considered. Thus assume there are n decision makers D1,D2, . . ., 0, and m criteria Cl, Cz, ..., C,. The dimensions of the problem can be increased to give nm criteria CII, . . ., Cl,, CZI, . . ., CZ,, . . ., C,,, ..., C,,, with the criteria Czl, C$Z, ..., C,, associated with the ith decision maker D,. The criteria associated with each decision maker can be given weights w, (i = 1,

..., n, j = 1, ..., m), with Ew, = 1 for all i to denote the weights given by diff&nt decision makers to the different criteria. Similarly the criteria Cl,, ..., C,, associated with a decision maker 0, could be given a (normalised) weight wz for i = 1, . . ., n to show the relative importance of the different decision makers. This is illustrated in Fig. 2. The decision problem with the weighted criteria w,w,C, (i = 1, . . ., n,j = 1, . . ., m) can then be analysed by an MCDM.

Most MCDM can only be used for problems involving explicitly stated courses of action, whereas many sustainable decision problems are defined implicitly in terms of the achievement of a set of goals. Use of an explicit decision-making process when the underlying problem is implicit generally assumes the prior decisions required to arrive at an explicit statement of alternatives. For

Fig. 2 Multicriteria decision making for multiple decision makers


Fig. 3 Example of an implicitly defined problem

instance the decision process in the case of relieving road congestion is frequently restricted to choice of route, but this approach assumes an a priori decision to resolve the problem by building a new road. This is illustrated in Fig. 3.

Some examples of the application of DSS to sustainable decision making

Sustainable decision making can be divided into a number of different categories, such as policy formulation, choice of projects, technologies and processes, and proactive and reactive responses to problems and incidents such as road congestion or oil spills. The sustainable decision tools which can be applied to these problems can be divided into five main categories: information handling tools, modelling and simulation tools, MCDM, expert systems, and life cycle analysis and green design tools. Many areas of decision making should involve considerations of sustainability, but particular areas of interest include the different sectors of the economy, energy, transport and water use. An important aspect of sustainable decision making is policy decision making on the appropriate response to changes in the level of important system variables, whether by regulation or changes (generally increases) in service

provision. An example of inappropriate responses in the area of public decision making is the decision to increase road capacity to deal with expected increases in the volume of car traffic. This has resulted in traffic generation, whereas a more sustainable approach would regulate traffic volumes.

Illustrative examples from the areas of planning and management of water resources and power generation will be discussed. The issues considered here are illustrative rather than exhaustive. In the cases of both water and energy management, decisions on public or private ownership are required and should include consideration of the effects on service provision and the environment, as well as economic issues. Other common decision issues relate to the reduction of the environmental burden of the associated administrative structures and the encouragement of environmentally friendly transport both for travelling to the workplace and on company business.

Planning and management of water resources

Water is clearly a vital resource with a range of uses which are either essential to maintaining life or necessary for a

reasonable quality of life. It is also used as a sink for effluent and solid waste disposal, including radioactive waste. Waterways can provide very rich natural habitats, particularly for birds, but many of the other uses may reduce the value of these habitats and destroy eco- systems. Planning and management of water resources involve a number of different types of decisions, including long-term policy decisions and specific decisions on the uses of particular waterways and the management of crises such as floods and droughts.

Maintenance and improvement of water quality and increasing the recycling of waste water and the closed- loop use of industrial water require decision making at a number of different levels and involve tradeoffs between environmental, financial, economic, social and technical considerations. Modelling and simulation tools can be used to investigate the effect of predicted rainfall patterns on the availability of fresh water and predict when and where droughts are likely to occur to allow advanced planning. They can also be used to investigate the long- term effects of given policies and changes in policies, as well as the effects of incidents and the spread of pollutants and the eventual environmental, social and economic effects of different types of uses. Expert systems can be used in decision making where water


DECISION MAKING

quality and legislation are important. Databases on, for instance, water quality, rainfall over a period and the different local and regional uses and demand for water, are important aids to all types of decision making in this area.

Expert systems and associated databases and retrieval programs have been developed to provide multi- disciplinary advice on licensing and abstraction of water for the UK National Rivers Authority? The system integrates laws on water abstraction with engineering problem solving and environmental decision making in a computer program which uses the knowledge of experts in management planning, monitoring and forecasting of water resources and gives access to relevant databases of attachment data and water resources legislation. The system provides an expert system to guide district resource officers through the process of water application licence determination and other tools to facilitate archiving, retrieving and using information. The system knowledge base contains almost 100 rules distributed over eight problem-solving assistants for manipulating over 30 objects.

Knowledge-based expert systems have also been used for water environmental protection, for instance in China? The user interacts through the user interface with the knowledge base, environmental models base, water environmental database and the inference mechanism. The system is designed to imitate the performance of an expert in evaluating water quality problems in a given subbasin or stream segment and measure the direct and indirect damage resulting from human activity. The Tennessee Valley Authority has developed a Lake Improvement Plan resulting from an in- depth interdisciplinary study of its river management policies to provide decision makers with a sound basis

supported by public review and technical analysis for changing and maintaining existing policies! The authority’s daily operation of the reservoir and power systems requires continuous balancing between the region’s hydrology and demands on water resources. Environmental issues are explicitly taken into consideration in daily decisions through limits and standards set by regulatory agencies or established by long-term policy analysis.

Planning and management of energy generation

Energy planning and management require regulations and legislation at the local, regional, national and inter- national levels. The area of energy use highlights many of the conflicts in sustainable development, since increasing energy use is frequently a prerequisite for development, but this has environmental and frequently also health costs. Thus sustainable development paths require more efficient use of energy and (in the short term) technology transfer and financial aid to allow ‘developing’ countries to increase their generation capacity without an excessive increase in environmental burden, as well as reduction in energy consumption and associated emissions by the ‘developed’ countries. This is illustrated in Fig. 4.

Individual power companies make a range of decisions, such as the level of demand to meet and whether to reduce demand to avoid the need for new generation capacity by, for instance, supplying energy-saving light bulbs. Other decisions relate to the commissioning, siting and capacity of new power stations and upgrading or decommissioning of old or outdated ones. In the past environmental factors have been treated as externalities and generally omitted from these decisions. Types of

I I Fig. 4 Sustainable ‘developed’ countries

increased energy technological improvements - + - efficiency

development and energy consumption

I I

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decisions at the national political level include the formulation of short, medium and long-term energy policies and strategies for the staged reduction of the level of emissions.

Support of sustainable development in the ‘developing’ countries requires decisions at national and company level on technology transfer and financial aid. Choice of

options. The external database consists of 19 elements, including information on the local economy, the local utility company’s finances and the desired payback period for the equipment under consideration. The steps involved in goal programming are shown in Fig. 5.

Utility companies and the public utility commissions which regulate them make technology selections which

new energy technologies, particularly in ‘developing’ must consider countries should include consideration of local availability of resources and raw materials and the possibility of carrying out repairs and maintenance locally. Decisions on the viability or closure of particular energy reserves, such as a given coal field, may be made at the national political level or that of the owning company and involve economic, social, environmental and political factors, as well as the state of local or national energy reserves. At the local level decisions are required on promoting and supporting conservation, the financial commitment involved and the dissemination of information. There is some role for MCDM for instance in decisions on siting new power stations and evaluating mixes of energy generation technologies, if environmental and developmental factors are included, rather than being treated as externalities. Simulation and modelling can be used to evaluate the effects of Fig. 5 Goal programming

different policies and changes in policy, for instance on reducing emissions, over different time spans through the investigation of hypothetical scenarios. They can also be used to predict changes in the availability of different energy resources and investigate the effects of developments in technology. They continue to have an important role in increasing understanding of the causes of observed climatic changes.

A goal programming model for energy-related decision making5 has been developed based on six goals: maximising employment generated by the mix of energy technologies selected; maximising energy conserved (or produced); minimising implementation costs, maximising economic multiplier effects in the local economy; maximising the savings to utility companies; and minimising the payback period for energy conservation improvements. The model consists of an internal database which describes energy conservation opportunities or technologies, an external database describing the community economy, and formulae which translate information from these data sets into information on the satisfaction of goals. The internal database consists of 15 data elements, including information on the costs, energy production or savings, payback period, labour requirements and local economic effects of the various

plement rural

multiple criteria, but environmental factors have generally been treated as externalities and omitted from the decision-making processes. However, they are likely to significantly affect the mix of resources selected to provide energy if they are included.6 There are three major approaches to incorporating externalities in energy planning: proxy values, fixed adders and multi-criteria decision making? Proxy values are being used to incorporate externalities in New York State8 and being developed for use in California? They are obtained by quantification followed by monetary evaluation. The main types of proxy values are the costs of the resultant clean up, the costs of control or mitigation and the revealed preferences approach which makes the marginal cost of pollution equal to the incremental pollution prevention costs.

Choice of the mode of energy development which would best com- development is a maior issue for

developing countries. Options for providing energy to rural areas include: rural power grids, i.e. the construction of large, generally oil-fired power stations and power transmission over large distances; mini conventional energy systems which use small-scale bunker or diesel generation systems able to supply one or two small towns; and mini nonconventional systems which use indigenous energy resources such as mini hydros or photovoltaic systems. Both types of mini systems save on energy transmission costs. This problem has been studied using incremental paired comparisons, weighted raw scores, partlwhole percentaging (P/W%) and monetisation.1° The following six criteria were used in the analysis: costs of energy in pesoslkwh oil substitution on a 1-10 scale, with 6-10 indicating increasingly positive substitution and 14 oil consumption; reliability on a 1-10 scale; land requirement in ha/MW generating capacity; local support on a 1-3 rank scale; and employment generation in jobs/MW.

This study includes the developmental factor of employment generation, the social factor of local support and the mixed environmental and developmental factors of land requirement and oil substitution, in addition to the economic factor of energy cost and the technical factor

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of reliability, but excludes a number of environmental factors, such as emissions. The oil substitution factor gives a measure of the extent of the use of renewable rather mini con. system 2 than non-renewable energy mini non-con. system 2.

Table 2 Baseline values used in the analysis’O

power grid

sources. Thus the decision- making approach could be classified as partially sustainable and could be made more sustainable by the inclusion of environmental factors, such as emissions and the consumption of non-renewable energy resources, as well as mixed environmental and developmental factors such as local availability of materials and expertise. Data used in the study of this problemlo is given in Table 2.

From this Table it is clear that the decision reached will depend on the relative weightings given to the different criteria. An ‘unsustainable’ approach based purely on economic criteria will result in choice of the mini- conventional system and on mixed economic and technical criteria-probably the rural power grid. A ‘sustainable’ approach would probably lead to choice of the mini non-conventional system, particularly if the additional ‘sustainability’ factors discussed above were included. However, this choice should be accompanied by measures to increase its reliability. This illustrates another aspect of sustainable decision making, in terms of indicating desirable modifications or improvements to the course of action chosen to increase its sustainability.

Application of DSS to sustainable decision making: discussion and conclusions

Sustainable decision making highlights a number of the issues that occur in real-world decision making which impede the application of MCDM without modification. These include a mixture of quantitative and qualitative criteria, the lack of monetary valuations, subjective, rather than objective valuations, implicitly defined problems and multiple decision makers. It has been shown that decisions involving multiple decision makers can be replaced by a single problem with an increased number of criteria. Some of these problems, particularly those of data in different units, can be resolved by rank ordering and normalisation.

Development of more appropriate DSS requires improved understanding of the different approaches to decision making and the consideration of the appropriateness of these approaches to sustainable decision making. There is considerable polarisation, which parallels the split between the right and left brains, between the two main approaches: classical decision theory, which is analytical and focused on the decision event, and naturalistic decision theory, which is intuitive and considers decision making as a process. Although both approaches have advantages as well as drawbacks,

neither is totally suitable for sustainable decision making. There has been a tendency for decision research to concentrate on isolated decisions, whereas many real decisions are multistage.

Much of the research on DSS has concentrated on the development of MCDM, whereas DSS in the broadest terms can be divided into four categories: tools for obtaining, analysing and presenting information; modelling and simulation tools; MCDM and life cycle analysis; and green design tools. Decision problems are often defined in terms of choice from a set of explicit alternatives, but this is frequently inappropriate for sustainable decision making and may imply previous decisions which restrict the problem to an explicit set of alternatives. Despite the lack of data, there is some evidence from the literature that better use could be made of DSS in sustainable decision making than at present. Additional research is still required in a number of different areas, including the development of improved models of decision making and problem classification and the development of improved user interfaces and DSS based on different types of decision models.

References 1 HAMMOND, K. R. et al: ‘The relative efficacy of intuitive and

analytical cognition’, Centre for Research on Judgement and Policy, 1984, Boulder, CO, USA

2 AHMAD, K., and GRIFFIN, S.: ‘Intelligent assistants and engineering decision support: management of water resources’, Computing systems in engineering, 1993,4, pp.325-335

3 YU, W. L. W., XU, M., and DONG, L.: ‘A knowledge-based expert system for water environmental protection’, Art+cid intelligence and civil engineering, 1992, pp.205-211

4 ALAVIAN, V.: ‘Integration of environmental management with reservoir and power system operations’, Proc nut. coni on hydraulic engineering, 1993, 2, pp.1792-1798

5 LEE, S. et al: ‘A goal programming model for community energy management strategies’, Annual meeting of the decision sciences institute, 1986, Honolulu

6 BRICK, S.: ‘Transcript: Michigan council on environmental quality, environmental externalities work group’, 1990, Michigan Dept. of Public Health

7 STANTON, T.: ‘Decision-aiding algorithms’, in ‘Applications of decision aiding software’ (Macmillan, 1992, pp.170-190)

8 PUTTA, S. N.: ‘Competition in electric generation-environmental externalities’, 1989, New York State Dept. of Public Service

9 ‘Report of the statewide collaborative process: an energy efficiency blueprint for California, 1990, California Energy Commission

10 QUEBRAL, M. C.: ‘Applications of decision-aiding software’, in NAGEL, S. S. (Ed.) (Macmillan, 1992, pp.207-233)

0 IEE 1998 Dr. Marion Hersh is with the Centre of Systems and Control and Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow G12 SLT, UK, Tel: 0141-330 4906, Fax: 0141-330 6004, E-mail: [email protected]


mailto:[email protected]

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Sustainable decision making and decision support systems