Malafant, K.W.J. and Fordham, D.P., 1998. Integrated Policy Frameworks for Policy Analysis. Greenhouse Beyond Kyoto Conference, 31March-2 April, Canberra., Bureau of Resource Sciences, Canberra.


Integrated Modelling Frameworks for Policy Analysis

K.W.J. Malafant and D.P. Fordham

Bureau of Resource Sciences, Department of Primary Industries and Energy, Canberra, Australia

Abstract

Policymakers, government bodies and research funding organisations must make important decisions on a regular basis. Decision making generally involves: defining the problem and issues; generating alternative solutions; evaluating the alternatives; and finally implementing the most acceptable alternative. To make well-balanced decisions requires information about the current situation and forecasts of the possible effects of proposed policy changes (Walcott et. al., 1997).

The demands on policy makers to respond to complex issues within a short time frame means that thorough analyses cannot always be completed. This combined with the limited understanding of many scientists of the policy process and the agenda that policy-makers are attempting to address, tempered by the fact that often the required information systems are not in place and the required data and information are unavailable, may lead to suboptimal decisions and policies.

"To successfully derive policies and goals consistent with the principle of sustainable development will have a further requirement that scientific, environmental and economic information, analysis and advice are properly integrated for input during the process of policy formulation" (Burch and Johnson, 1990). To support this approach, there is a need to concentrate on understanding and exploring the decision processes, rather than, just obtaining an intimate knowledge of specific components or models.

Design or integrated modelling frameworks can be used to integrate the best and most appropriate of existing models and data. Not only are sources and forms of spatial and temporal data highlighted, but also where data and methods are lacking. The framework design must be flexible; as new research and/or models become available, they should be able to be integrated into the framework. Such frameworks allow the future to be explored by examining the driving processes, their interrelationships and inconsistencies or tensions. The aim of the approach is to determine the best options for integrating environmental, resource, ecological sustainable management, heritage, economic and social analyses for use in planning, conservation and industry development (Malafant and Fordham, 1997).

Introduction

Increasingly, decision makers are confronted with a large body of scientific information and knowledge which has some bearing on the issue at hand. Often the information originates in more than one discipline; sometimes it is contradictory; usually it is incomplete and/or subject to caveats and assumptions. As often as not, the decision makers are unable to assimilate all this information and proceed to make decisions based on other criteria, usually not unrelated to the relative power of the various stakeholders. Alternatively, a panel of disciplinary experts is assembled and assigned the task of recommending a course of action. The authority of the decision then rests on the disciplinary credentials of the selected experts.

The demands on policy makers to respond to complex issues within a short time frame means that thorough analyses cannot always be completed. This combined with: the limited understanding of many scientists of the policy process; a lack of awareness of the agenda that policy-makers are attempting to address; that often the required information systems are not in place; and that the required data and information are unavailable, may lead to suboptimal decisions and policies. "To successfully derive policies and goals consistent with the principle of sustainable development will have a further requirement that scientific, environmental and economic information, analysis and advice are properly integrated for input during the process of policy formulation" (Burch and Johnson, 1990).

Previously forecasts of policy effects have usually been produced by a team of specialist researchers with a limited amount of information available. The forecasts were often done by rule-of-thumb rather than using precise analytical techniques (O’Callaghan, 1996). In some cases decisions have so many sources of uncertainty that it has been safer to leave them to the integrative powers of the human brain rather than give the decisions an unwarranted aura of reliability (Stuth and Lyons, 1993). The human mind is adept at handling linear and even some nonlinear relationships, but it often has great difficulty in assessing likely outcomes when there are competing relationships, interactions and feedbacks , or when a great deal of uncertainty about specific inputs and parameters exists in the system. While these mental models can be as valid a representation of reality as physically-based mathematical models, some decisions would benefit from integrating models and reliable data sources into a decision framework or system (Casti, 1994; Walcott et. al., 1997).

Modelling is traditionally viewed as a synthesis for science inquiries, a predictive tool to extrapolate into the future and a means of identifying weaknesses in information or knowledge. The emphasis in modelling has been on science advancement, while the use of modelling as a tool to investigate environmental and socioeconomic problems is a relatively new perspective. When used in scenario analysis, modelling is a way to explore, rather than predict the future (Gault et. al., 1987). Scenario analysis, or the development of outlines, synopses or plots, has an advantage over expert judgments and many other planning approaches. They necessitate a systematic, coherent approach to the issues under study, and elucidate future uncertainties. Scenarios can be of considerable assistance to policy makers where there is a general consensus about probable outcomes, but a paucity of hard data (Schoute et. al., 1995; Walcott et. al., 1997).

The use of current information, its interpretation and incorporation into scenarios enables policy makers to focus on alternative futures and policies, allowing them to anticipate and understand the risks associated with the changes, and to find new options for action (Malafant and Fordham, 1997).

Integrated frameworks have been developed in several European countries, Canada and Australia, with the aim of aiding policy development and resource management. The most common scale of these frameworks has been at the regional level, since this scale enables characterisation of the system with most accuracy. The frameworks are being used to explore large ecological and resource management issues in these countries.

Policy Modelling Philosophy

A model can be defined as a "... representation of part of reality as seen by the people who wish to use that model to understand, to change, to manage and to control that part of reality" (Pidd, 1996). Importantly this definition contains no explicit mention of prediction. As Allaby (1995) notes, "we do not predict, we forecast, and there is a difference". With forecasts we are dealing with possibilities, not the inevitable single or optimum outcome generally associated with predictions. The purpose of forecasts is not to predict the future, but to examine the possible choices open to us by exploring alternative futures (Allaby, 1995).

The development and use of models and modeling tools may be viewed as a process for achieving three main aims:

    1. for making explicit and sharing the mental models and concepts that condition the decision making process
    2. for synthesising understanding from the natural, engineering and social sciences as it relates to policy issues
    3. for exploring the longer term consequences of actions and their impacts on the system as a whole

Although the current discussion focuses more on the third aim, the other two aims of modelling systems should not be thought of as peripheral to the undertaking. The process of developing and using models is more important than the model itself.

"A scenario is a description of the current situation, of a possible or desirable future state as well as of the series of events that could lead from the current state to this future state" (Schoute et. al., 1995). A scenario modelling framework combines this series of events over time and provides an insight into the impacts of this changing series on the possible future states.

Scenario analysis allows the future to be explored by examining the driving processes in the system and their interrelationships, rather than attempting to forecast the future by extrapolating data measured from past activities (Gault, et. al., 1987). Decision scenarios allow the policy maker to anticipate and understand risk and to discover new options for action (Fordham and Malafant, 1995; Fordham and Malafant, 1997). Scenario analysis allows us to explore the impacts of developing policies while analysing and understanding important aspects such as:

Scenario analysis methods fall into two main classes: those that project forward; and those that consider backward or inverse problems (Figure 1). The forward methods decide or fix the variables or structures that will make up the assumptions of the scenario and then project this "current" situation into the future. The set of paths or scenarios generate alternative futures which can then be analysed and accepted, when considered appropriate. A typical example of this method is: what are the likely outcomes of reducing the amount of irrigation water in irrigation regions on the biophysical, production and socio-economic conditions some time in the future. This forward projection accounts for almost all the scenario analysis that occurs currently. Tools that implement this method are well developed, relatively easy to use and understand, and there is a continually growing body of expertise and knowledge.

The second method considers the inverse problem. In this situation a number of feasible or acceptable futures are determined - that is, the number and range of the alternative futures are fixed - but the scenarios are free. We then explore the alternative scenario funnels or paths from the selected futures to the current situation. Some of these paths will be acceptable, others not. An example of this type of scenario analysis could be: what irrigation practices must be adopted if all the watertables in a region are to be stabilised below 2 metres? The future has been fixed and now the need is to explore paths that achieve this future. Tools and methods for this style of scenario analysis are more limited and the expertise, knowledge and applications base are small. In the "greenhouse" arena the German government, thru a number of research groups, is experimenting with using this style of scenario analysis to consider the possible paths to achieve reduction targets. This approach has been called the "tolerable windows approach" and relies on complex mathematical constructs and requires exceptional computing resources to be effective.

 

Figure 1. Scenario Analysis Methods.

However, our ability to undertake scenario analysis is still limited by uncertainties about the data and our understanding and knowledge of the system interactions. The need is for methods that integrate and extend our understanding of the system, rather than just a detailed knowledge of specific components, models or processes. Pure prediction is a thankless task, and it is more feasible and useful to focus on alternative futures and policies which can help us analyse, manage and control the system or processes we seek to understand (Simon, 1996).

Integrated Framework Features

A number of approaches to the issues mentioned above have developed on the international scene in recent years; integration of models is a major feature of almost all these approaches. Modelling of the components alone may not simulate the complete system effectively as environmental and socioeconomic systems have emergent properties which relate to the whole, and not merely to the sum of the parts.

Design or integrated modelling frameworks integrate the best and most appropriate of existing models, data and knowledge. These frameworks "outsource" the modelling to other systems, concentrating on the protocols and interactions for welding the various model components together. These toolkits have few inherent models, but facilitate the integration of various models or analysis tools into a single coherent framework. Not only are sources and forms of spatial and temporal data highlighted, but also where data and methods are lacking. The framework design is flexible; as new research and/or models become available, they can be integrated into the framework. The frameworks developed using these integration tools often include the use of scenarios to explore and experiment with the complex system that develops.

An important feature of the modelling approach adopted by many of these framework systems is workshopping or stakeholder analysis. This involves specialists, stakeholders or clients from a variety of disciplines, coming together to develop the framework components and linkages of the system. Development of the framework by a group of people with experience in the individual components of the system and with knowledge of local conditions ensures that the best available knowledge is exploited and that outcomes are realistic and feasible (Grayson et. al., 1993). The need to integrate and simultaneously assess biophysical, economic and social components, while including the user as a surrogate for society, is necessary for effective decision-making (Gault et. al., 1987; Richards, 1992; Watson and Wadsworth, 1996). The benefits of incorporating the user include: a sense of ownership and involvement; decision systems that provide stewardship rather than control; and the introduction of novelty of the type that scientific disciplines might overlook.

The development of these frameworks is an iterative task, with not only the design, but also the construction and component validation changing as the user explores the system behaviour. There is no final framework, rather an initial version is built and then modified and developed in response to changing user experience, learning and knowledge (Keen, 1981).

Example Toolkits

The concepts of integration in modelling frameworks, systems dynamics diagrams and software suites that implement them, are not particularly new. Systems such as STELLA, DYNAMO (Pugh, 1961) and AEAM (Grayson et. al., 1993) have been used to implement complex modelling systems. The systems dynamic diagrams developed by Forrester (1961, 1971) provided the inspiration for the conceptual, framework or hierarchy diagrams used by many toolkits to develop the framework "infrastructure" and linkages. Examples of these toolkits include:

    1. FACET: A spatial spreadsheet providing a complete spatial decision toolkit. It is a modelling and management decision making tool which includes a range of interactive analysis, data fusion and visualisation tools. Models can combine economic, geographic, environmental and natural resource concerns. The product has been used in strategic planning, exploration, habitat modelling, coastal zone management, stock market analysis and atmospheric research (Ayers et. al., 1993; Melzer et. al., 1993).

    2. Calyx: The Calyx family of products from ESSA Software in Canada are PC-based software tools that allow the prediction and examination of environmental impacts and their consequences. Two products in the family are of most interest: Calyx GIS, which is a decision support framework which uses expert systems and a GIS (ArcView) to provide analyses of project and environmental information; Calyx EA which enables users to identify and quantify the entire spectrum of potential environmental impacts - primary biophysical effects as well as secondary impacts to commercial, visual and social environments - and report on them.

    3. Whatif?: Is an object-oriented scenario modelling package providing a structured set of tools for groups of experts to interact, express their ideas and apply concepts to achieve resolutions to debate(s) of economically and ecologically sustainable resource issues. The system uses the "design" approach to modelling (Gault et. al., 1987). In this approach the system is modelled by firstly identifying what components, as objects, exist in the system. Then linkages between the objects, in a functional sense, are established to form the framework. The model framework incorporates input and output objects, as well as other objects which may modify these inputs and outputs. The Whatif? approach tightly integrates the user with the models and integrated framework. The user provides novelty and change to the system by the specification of control variables over the simulation timeframe. The approach aims to explore, rather than predict the future, and is not oriented towards global optimisation or equilibrium conditions. This lack of global optimisation or equilibrium constraints can lead to results which are inconsistent or socially unacceptable - tensions. The user can then resolve these tensions by either exploring alternative scenarios or the user may exercise choice and accept scenarios where tensions still exist (Gault et. al., 1987; Veitch et. al., 1993).

    4. Analytica: Uses hierarchies of models to develop and manage complex interactions and relationships. As with many of these toolkits, Analytica uses a systems dynamic or conceptual diagram to establish the model components and their linkages. Analytica uses the concept of the white-board to enable quick system prototyping and development. This approach allows the problem to be described qualitatively without the details, and then once there is understanding of the problem and issues, the quantitative details can be defined.

    5. Other Systems: Many other tools exist which provide integration and scenario analysis facilities. Some of the systems which can be considered in this category include:

    • STELLA and ithink: Scenario modelling tools which can be used to code sophisticated models and interactions. Uses a building block approach with four icons to develop the system representation. Arguably the best known application of STELLA is the World3/91 model of Meadows et. al. (1992).

    • Extend: A dynamic and iconic based simulation environment. It can be used for simulating discrete event, continuous and mixed processes and systems. Systems are built by connecting blocks from the Extend library.

    • Vensim: An integrated framework for developing, analysing and deploying dynamic feedback and continuous simulation models. Vensim uses a toolbox design combined with a visual model builder to construct simulation models and analysis tools.

    • ModelMaker: A PC based system that uses conceptual or model diagrams to construct models. The system has a number of modelling tools, including optimisation, standard mathematical functions, statistics, sensitivity analysis, interpolation and differential equation tools. ModelMaker uses a simple visual model builder to enable rapid production of systems.

    • Queensberry: "Queensberry is a computer software system which offers facilities that are useful for setting up or analysing rational debates, especially debates about planning" (Macpherson and Grant, 1992). A PC based system using the Smalltalk language.

Examples of the use of these toolkits include: long-term socio-economic analysis of policy decisions in Canada and the USA; forestry and multiple land-use debates in the USA, Australia and Canada; and the analysis of environmental impacts, their consequences and remediation strategies for defence training areas in Australia, the USA and Canada. The toolkits have also been used to perform policy analysis of the effects of global climate change; to investigate the cost-benefit of different environmental mitigation strategies; and the development of prototype systems and specifications.

For a more complete review of these toolkits and other systems see Malafant and Davey (1996) or (Malafant and Fordham, 1997). Alternatively, brief functional descriptions of the toolkits mentioned above and other systems can be found online at: http://www.nmsr.labmed.umn.edu/~michael/dbase/outgoing/catalog.html..

Advantages and Disadvantages of the Approach

The integrated framework approach has a number of advantages in building complex scenario and analysis systems for investigation and decision making. The most important advantages include:

    • The method provides an agreed view of the problem, issues and possible solutions. These tools and methods provide transparency, stewardship and ownership rather than control in explaining complex systems. By encouraging stewardship we avoid the feeling of imposition of decisions and encourage an understanding of the way the decision process acts rather than just an intimate knowledge of specific components or processes of the system (Malafant et. al., In press).

    • The method encourages the development of systems for examination and experimentation. A realistic framework that integrates a range of models and datasets provides an approach for assessing environmental and resource issues, and the impact of proposed policy and management regimes. This approach however, needs an explicit and multidimensional understanding of the issues combined with a clearly articulated requirement for information (Walcott et. al., 1997). Hopefully, this leads to a better understanding and perhaps, even explanation, of the interactions of the system components, linkages and actions.

    • Building on the previous two advantages the methods can be effective in communicating difficult ideas, concepts and information to a wide variety of interested groups. This in turn enhances the transparency of the modelling and the actions taken in the decision making process. "Ironically, the more information made available on the inadequacies and assumptions of the decision-making process the less contention they attract. This transparency allows attention to be focused on the main objective rather than individual, less important, detailed terminology and aspects which are invariably interpreted in different ways by different disciplines" (Malafant et. al., In Press).

A lack of understanding of the current and future roles of the methodologies used for decision making, generally reflects a poor level of communication and feedback between modellers, implementers and other user groups. Decision systems are often developed in isolation from the potential user community or the communities they will affect. It is important to disseminate information about the system, its development and its uses at all stages to increase the public understanding of the policy proposals and methods used to arrive at these proposals. This consultation process should involve interested parties and discussion on issues as they arise (Malafant et. al., In press).

    • The re-use of existing models, framework components or entire frameworks from other projects or applications (Malafant et. al., 1997).

    • A modular structure that allows easy updating when scientific knowledge increases or is modified, providing flexibility.

Obviously nothing comes for free! There is no "free-lunch" when adopting these framework techniques and disadvantages do follow from their use. The most difficult disadvantages to overcome include:

    • The conceptual shift, especially for modellers, mathematicians and disciplinary experts, that the framework process, development and application require. Integrated modelling requires the availability of information, knowledge and expertise and the ability to overcome jargon and adopt a common language (Blood, 1994).

    • Workshopping and consensus building requires time and continual effort to use effectively. This is a different "mode" of operating for many, especially modellers. Relatively large investments in terms of time and commitment of interdisciplinary expertise are required, as well as team collaboration, both within and between national and State institutions (Warrick et al., 1996).

    • The tools can be relatively easy to use for simple problems, but can be quite daunting when used for extensive and complex system or framework formulations or developments. Some of the tools have a steep learning curve with effort, time and use required to become skillful in adapting the techniques and tools to different analysis situations. This is especially so when a number of different models, data sources and scenarios are to be contemplated.

    • The lack of basic data required for the adequate resolution, calibration and validation of the models. The most common scale of many of the constructed frameworks has been regional, since this scale enables characterisation of the system with most accuracy. Many projects have either revisited the data collection, to make it fit the required quality standard and needs, or have assumed a more general modelling approach, because the inconsistency of the data over the area under consideration has been considered too large for detailed assessments (Walcott et. al., 1997).

Applications of Integrated Frameworks

To provide a better understanding of the applications and flexibility of integrated frameworks, two examples of their application are discussed.

Example 1: Sustainable Irrigation Futures

The object oriented decision support system and scenario analysis package, Whatif?, is being used to model biophysical and production systems and socioeconomic futures in irrigation regions of the southern Murray-Darling Basin, of Australia, over the next 20 years.

Irrigated agriculture faces a number of problems including salinity and waterlogging, increasing water prices and low commodity prices. A number of urgent policy decisions must be made by resource managers and regional communities to provide a framework for the future shape of irrigated farming in these regions. Issues include the sustainability of irrigated agriculture, alternative land use, land retirement, structural adjustment, irrigation infrastructure replacement, water pricing and allocation, and nature conservation. Soundly based information on the implications of these decisions is fundamental to the development of successful programs and policies.

A framework is being developed to identify the likely impact of a range of factors on the biophysical, production and socioeconomic structure of irrigation regions over the next twenty years. The modelling framework, developed using the Whatif? scenario modelling toolkit, (Figure 2) links four main components: biophysical, production, enterprise and regional and national flow-on effects (Fordham and Malafant, 1995; Malafant and Fordham, 1995). This framework draws on the concepts of a prototype framework outlined by Veitch et. al. (1993).

The framework components integrate models operating at four spatial and two temporal resolutions. Spatially, models operate at fine scale grids (100m x 100m), fields (hectares), farms (10’s of hectares) and regions (1000’s of hectares). The temporal scales are day and year.

The integration of the biophysical models with production and economic models enhances the long term decision-making process. By evaluating the framework across a range of price, cost and yield scenarios based on the effects of waterlogging and soil salinity, comparisons of the impacts and sensitivities of changes on business return and level of equity on the farm together with the flow-on effects to the community can be explored.

Figure 3 provides an example of the output for a number of different scenarios under identical climatic conditions and varying irrigation regimes. The output demonstrates the effects of different irrigation schedules on the water table depth; contrasting the ‘do-nothing’ scenario with reduced water availability. The impact of water table depths on soil salinity are also shown; contrasting the ‘do-nothing’ scenario with reduced water availability and increasing salinity of the irrigation water.

 

Figure 2. The framework diagram showing the four main components, linkages, feedback, outputs and data requirements (After Fordham and Malafant, 1995).

Scenarios are being developed to explore the impact of a range of factors on the biophysical, production and socioeconomic structure, based on twenty year profiles. These include, for example, the implication of 'do-nothing' scenarios, changes in spatial distribution of soil salinity levels, effects of increasing salinity on agricultural production, drainage requirements and impact of drainage programs, alternative landuse and mix of activities, water market reforms and the impact of industry development.

The framework has been developed for policy makers, regional resource management organisations and community groups. The aim is to develop, using scenarios, programs and policies based on a sound understanding of local biophysical, economic and social conditions and which lead to a long term productive and sustainable irrigated agriculture (Fordham and Malafant, 1997).

This is a mature framework which has developed over a number of years. There have been: changes to the models and data sources used; the expectations of the models and framework; and the view of the techniques usefulness and appropriateness. It has been an excellent learning process to us as developers of the framework and to the disciplinary experts, modellers and community people involved in steering the project.

Example 2: Ballast Water Decision Support System

The possible introduction of harmful aquatic organisms and pathogens into Australian waters by shipping has been recognised as one of the major environmental issues to face Australia. These organisms can reach Australia from other ports via ballast water discharged into Australian ports or by attaching themselves to ship hulls. A number of organisms have already established themselves in this manner, causing a potential serious threat to our environment, human health, fishing and marine acquaculture industries (Australian Quarantine and Inspection Service (AQIS), 1994).

The "ultimate solution" to this threat would be a method of killing all target organisms in ballast water by mechanical, chemical or biological means. Currently, no such treatment exists. As a short term solution the establishment of a Decision Support System (DSS) to: integrate the available information; to explore management options; and to evaluate the risks posed by any vessel or voyage is being developed. This system will allow AQIS and port authorities to evaluate the risk of allowing access to Australian ports for particular vessels.

 

Figure 3. Scenario output or Views from the Whatif? Irrigation Framework.

 

Unlike the previous example this project is best described as a work-in-progress using the tools and techniques in a more simplistic role than the modelling framework for irrigation policy. The requirements of this project are to:

    • Identify the DSS components, their linkages and the data and information needs and sources

    • Construct a high-level framework design for the DSS and the components and their interactions

    • Generate the DSS functional specifications and documentation

    • Construct a prototype DSS framework

 

Figure 4. Prototype Framework for Ballast Water DSS.

The current prototype decision system (Figure 4) aims to provide the vehicle for achieving this integration and exploration of management options. The prototype system must be capable of exploring spatial, temporal, environmental resource, economic and social implications of allowing particular vessels into Australian ports. The prototype has been developing incrementally as new requirements are identified using workshopping techniques. Clarification of the requirements and uses of the DSS are essential in providing information so that clear and concise specifications for the development of the components of the DSS can be written.

Summary and Discussion

Integrated decision and scenario modelling frameworks are designed to aid the evaluation and decision making process. They are not designed to replace or automate the decision process, but to aid and improve the effectiveness of the users interaction and understanding of the complex system under consideration. Ideally these systems "should be process independent, and user-driven or controlled" (Sprague, 1986).

There is a need to integrate the development of options or scenarios within information systems to provide faster, repeatable and consistent option development. Such modelling frameworks seek an integrated approach to address environmental, economic and social concerns. The frameworks allow the user to interact with them at any point, while their non-hierarchical structure enables the sharing of objects and model outputs, and allows component feedback. The framework treats the world as a set of changing behaviour patterns, focusing on interconnections. The economy and the environment is seen as one system with stocks, flows, feedbacks and thresholds influencing the way the system behaves (Meadows et. al., 1992).

Integrated modelling techniques allow:

    • The exploration of alternative futures using decision scenarios. The exploration of scenarios allows the decision maker to understand and anticipate risks and to discover new options for action. They also allow the evaluation of policies or strategies for management and planning. Scenario analysis can support informed debate of the policy decisions and the resolution of their tensions. The relative merits of alternative futures and policies can be discussed as can the merits of reaching the same end point by different means.

    • The conveying of the information needed to enable effective and constructive decision making and planning.

    • The linking of model components within one system and the integration of knowledge across disciplines.

    • The appraisal and expression of uncertainties in science and policy and the education of users. The development and application of frameworks that integrate information from many different areas is necessary to allow the assessment of management scenarios and to allow the identification of target indicators for the measurement of "success" (Malafant and Fordham, 1996).

In the policy arena, tactical decisions need to be distinguished from strategic decisions. Tactical decisions require more precise information within short time frames and scales, and may be best served by a collection of datasets and simple models. For tactical use, an information management and decision support system, based on integrated framework techniques and scenario analysis can assist policy implementation. Such a system, which integrates available input and coordinates output, should be able to deliver scientific expertise to policy makers relatively quickly, improving transparency in decision making and guiding decision makers in locating and using critically important tools and information (Fordham et. al., 1997).

Policy clients involved in strategic decisions, generally with a longer time frame and at a larger scale, can use model-based scenarios to develop an increased understanding of the possible consequences of options. However, the development of coordinated computer-based frameworks to produce national-scale scenarios requires a concerted effort over several years by a team of systems specialists. To achieve frameworks such as this, the organisational components need as much attention as the modelling capability and the information sources (Walcott et. al., 1997).

In all cases the models used in scenario analysis and modelling frameworks must be appropriate to the objectives of policy clients. An appropriate balance must be struck between the need for models to be physically and biologically logical, feasible and realistic, and the requirements of the policymakers that often address regions or whole continents.

Acknowledgments

This paper is based on papers presented at two conferences in 1997: A paper presented at the Climate Prediction for Agricultural and Resource Management conference, Canberra, Australia, 6-8 May 1997; and papers presented at the International Congress on Modelling and Simulation Conference (MODSIM 97). The authors also acknowledge the Murray Darling Basin Commission for funding the IFF? work and AQIS for funding the prototyping work and for allowing us to use it as an example. We also thank Rob Hoffman for additional material and extensive comments on the paper. We would like to thank Franzi Poldi, Simon Veitch and David Rossiter for commenting on the draft of this paper. All errors of omission and otherwise rest with the authors.

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