Modelling Concepts


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, Hamilton, Hoffman and McInnis, 1987). 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, Finke, Veeneklaas and Wolfert, 1995; Walcott, Fordham and Malafant, 1997).

Policy Modelling Philosophy

"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. Decision scenarios allow the policy maker to anticipate and understand risk and to discover new options for action (Fordham and Malafant, 1995; (Malafant and Fordham, 1997).

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). 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).

Scenario analysis methods fall into two main classes: those that project forward; and those that consider backward or inverse problems (Figure 1) (Malafant and Fordham, 1998). 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. For example, what are the likely outcomes of reducing the amount of irrigation water in irrigation regions on the biophysical, production and socio-economic conditions. This forward projection accounts for almost all the scenario analysis that occurs currently.

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. For example, 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.

Figure 1. Scenario Analysis Methods

Integrated Framework Features

Design or integrated modelling frameworks integrate the best and most appropriate of existing models, data and knowledge (Fordham and Malafant, 1997). 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. Specific objectives of the framework concept are to develop an information or decision system which:

An important feature of the modelling approach adopted by many of these framework systems is workshopping or stakeholder analysis (Gault et. al., 1987; Malafant and Fordham, 1995). 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, Blake and Doolan, 1993).

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).

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 (Malafant and Fordham, 1998). The most important advantages include:

The most difficult disadvantages to overcome include:


Decision support systems (DSS) and scenario modelling frameworks are designed to aid the evaluation and decision making process (Malafant and Fordham, 1998). 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. This transparency allows attention to be focussed on the main objective rather than individual, less important, detailed terminology and aspects which are invariably interpreted in different ways by different disciplines. Ideally "a DSS should be process independent, and user-driven or controlled" (Sprague, 1986).

Integrated or decision frameworks are important in forming coherent systems that can convey the information required to enable effective and constructive decision making and planning. They can also act as awareness raising and educational tools. The exploration of alternative futures using decision frameworks allows the policymaker to anticipate and understand risk and to discover new options for action. Scenario analysis provides a way of exploring alternative futures, and 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 (Fordham and Malafant, 1997).


Allaby, M. (1995). Facing the future: The case for science. Bloombury Publishing, London.

Fordham, D.P. and Malafant, K.W.J. (1997). The Murray-Darling Basin Irrigation Futures Framework (IFF?). In: International Congress on Modelling and Simulation Conference (MODSIM 97). McDonald, A.D. and McAleer, M. (Eds). Modelling and Simulation Society of Australia, 2, 643-648.

Gault, F.D., Hamilton, K.E., Hoffman, R.B. and McInnis, B.C. (1987). The design approach to socioeconomic modelling. Futures, February, 3-25.

Grayson, R.B., Blake, T. and Doolan, J.M. (1993). Application of AEAM to water quality in the Latrobe River Catchment. In: Proceedings of Hydrology and Water Resources Symposium, Newcastle, Australia.

Keen, P.G.W. (1981). Value analysis: Justifying decision support systems, Management Information Systems Quarterly, 5(1), 1-16 .

Malafant, K.W.J. and Fordham, D.P. (1995). Decision support systems and visualisation tools for modelling biophysical, production and socioeconomic futures in irrigation regions. Proceedings International Congress on Modelling and Simulation, MODSIM 95, November 27-30, University of Newcastle. Modelling and Simulation Society of Australia, 3, Pp. 236-244.

Malafant, K.W.J. and Fordham, D.P. (1997). Integration frameworks in agricultural and resource planning and management. In: Climate Prediction for Agricultural and Resource Management. Munro, R.K. and Leslie, L.M. (Eds). Australian Academy of Science Conference, Canberra, 6-8 May. Bureau of Resource Sciences, Canberra, Australia.

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

Malafant, K.W.J., Veitch, S.M. and Fordham, D.P. Application of a phased approach to land use decisions and site selection. In Press. Journal of Environmental Management.

Schoute, J.F. Th., Finke, P.A., Veeneklaas, F.R. and Wolfert, H.P. (Eds). (1995). Scenario studies for the rural environment. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Sprague, R.H. (1986). A framework for the development of decision support systems. In: Sprague, R.H. and Watson, H.J. (Eds). Decision Support Systems, Putting Theory into Practice. Prentice-Hall International Inc., New Jersey.

Walcott, J., Fordham, D.P. and Malafant, K.W.J. (Eds). (1997). An agricultural systems framework: A feasibility study for RIRDC. Compiled by L. Karssies and D.H. White. June.

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