A NOTE ON A MODEL BUILDING TECHNIQUE FOR AGRICULTURAL AND BUSINESS SYSTEMS
Post on 02-Oct-2016
A NOTE ON A MODEL BUILDING TECHNIQUE FOR AGRICULTURAL AND BUSINESS SYSTEMS
C. L. Pugh Wye College (University of London)
This note reports on a research project carried out mainly during the period 1970-73 at the University of Reading to develop a method of building non- optimising simulation models of agricultural systems which avoids the use of conventional computer programming and rystems analysis procedures.
The project took as its starting point two premises: firstly that the simulation modelling approach offers the possibility of a more realistic and flexible model than do the analytical techniques (usually linear programming) because of the restricting assumptions contained in the latter; and secondly that whereas in the linear programming approach the existence of the constraint matrix format provides a useful practical framework in spite of the limiting assumptions, practical application of the simulation approach is restricted by its very generality, since no framework or general-purpose procedure exists to assist the model builder. It was proposed to develop a method of model building which hybri- dised the two methodologies : to retain the generality and freedom from assump- tions of the simulation model, while providing a data matrix format coupled with a general-purpose computer algorithm to allow model construction to take the form of matrix building, without the need for the conventional computer pro- gramming stage.
A general purpose modelling program The result of this project is the Reading University Business Modelling Program (RUBUMP), which is a general-purpose data-dependent model building tech- nique for constructing non-optimising models in matrix format. I t is described in detail in Griffis (1973) and Pugh (1973), but a short description of the concepts will be given here. A users manual (Pugh, 1976) gives detailed instructions and specifications for the model builder.
The method takes the form of a general-purpose computer program which processes models submitted to it in the form of a data matrix. The program makes no attempt to find optimal activity levels, but rather, takes activities one at a time in a sequence prescribed by the model builder, and calculates for each either the maximum possible_level or the minimum necessary level (again the criterion is given by the model builder) according to the constraints acting on it. The result thus takes the form of a set of capacity activity levels and an associated set of resource totals.
Because no analytical algorithm is used, the functions implied by the co- efficients can be made more complex by the addition of several coefficients per
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cell,* and activities may be linked internally;? furthermore, stochastic co- efficients and non continuous activities can be included. One cycle of the model, leading to a set of activity levels and resource totals, can thus be structured by the user to perform a wide variety of complex calculations to represent the system to be modelled. Cycles are linked by a recursive mechanism which creates input for a subsequent cycle from previously generated results, and a user-controlled report generator allows output to be structured into a suitable report format between cycles.
Thus, using this more flexible matrix format, models may include complex and non-linear relationships such as fertiliser response functions, capital set-up costs, and integer levels for non-divisible factors. Where necessary the coefficients can be made stochastic, and the sequencing procedure allows chains of dependent calculations to be included within one cycle, with subsequent activities using previously computed results.
The process of matrix model building is facilitated by the report generator, which allows the model builder to collate and order complex results into a simple and comprehensible form, and by the data coding system, which allows the maximum flexibility in entering, checking, modifying and storing data models.
The method developed has proved capable of modelling a wide variety of business and agricultural situations. It has been shown to be particularly well suited to the building of business models for use in forecasting and planning, especially where the nature of the model requires only a relatively small number of cycles to represent the system (e.g. Pugh, 1973; Griffis, 1973; Devisch, 1973; Zuckerman, 1974; Rountree, 1973). Particular use has been made in the con- struction of business games for teaching and research (Pugh, 1973; Griffis, 1973). Three highly complex farm models have been used as the basis for the National Farm Management competition (FARMSCAN) (Griffis, 1974, 1975, 1976), a business game run on a national basis during 1974, 1975 and 1976, with approxi- mately 100 participating teams each year,
Advantages of the RUBUMP approach The advantages claimed for the method all stem from the fact that models can be constructed and operated using the data matrix format, with no need for conven- tional programming and analysis procedures.
The main benefits from this procedure are as follows: The economic saving both of time and expertise through the elimination of the programming stage of model building. The fact that a matrix with a particular format is used provides the model builder with a formal framework for his model which simplifies the process of system analysis which must precede the programming stage. The fact that the model always exists in data form rather than in procedural computer language simplifies the process of modifying, correcting and running models. Data are often made available in a form well suited for inclusion in matrix coefficient form.
* Yi = Qrj + Arj (&f ir ) where Yi is the use (or supply) of resources to row i
and Qrj, Pij and Arj are matrix coefficients.
Xj is the level of activity j
t The calculated level of activity j , (Xj), may be inserted as a linear coefficient in a subsequent
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These advantages remove a considerable restriction on the application of simula- tion models, since they allow the research or advisory economist with limited programming expertise to undertake the construction and use of models from conception to completion without involving the specialist skills of programmer and systems analyst. This advantage is reinforced by the fact that the skills in- volved in matrix model building are both widespread, as a result of the popularity of linear programming, and arguably more readily learnt by the novice than the less structured skills of computer programming and systems analysis.
Thus it is concluded, from the wide range of systems that have been modelled using the RUBUMP approach, that the system is technically capable of building large and complex models, in the form of data matrices, for processing by a single general-purpose computer program. It is seldom possible to prove definitively the advantages of one approach over another for particular purposes, since each situation is unique and offers a particular set of characteristics to the model builder. It is contended, however, that the RUBUMP approach can offer ad- vantages, both organisational and economic, to the model builder in agricultural economics for whom systems analysis and programming expertise are limiting factors.
References Attenborough, J. (1973). The Growth of a Suckler Herd and Fattening Enterprise. Unpublished
B.Sc. dissertation, University of Reading. Devisch, N. R. (1973). An Examination of Short-Term Management Problems in Belgian Pig
Farming. Unpublished MSc. thesis, University of Reading. Griffis, D. 0. (1973). A Method of Building Simulation Models in Matrix Format with Illustra-
tions of its Application. Unpublished M.Phil. thesis, University of Reading. Griffis, D. 0. (1974). FARMSCAN -The National Farm Management Competition. Partici-
pants manual for 1974. Griffis, D. 0. (1975). FARMSCAN -The National Farm Management Competition. Partici-
pants manual for 1975. Griffis, D. 0. (1976). FARMSCAN - The National Farm Management Competition. Partici-
pants manual for 1976. Pugh, C. L. (1973). An Approach to Business Simulation as an Aid to Farm Business Decision
Making. Unpublished Ph.D. thesis, University of Reading. Pugh, C. L. (1976). A Users Guide to RUBUMP Model Building. Farm Business Unit, Wye
College, University of London. (In press.) Rountree, J. (1973). A Comparison of Systems of Beef Production in Ireland. Unpublished
M.Sc. thesis, University of Reading. Zuckerman, P. S. (1974). Assessing the effect of new technology on smallholder production. In
Planning Agriculture in Low Income Countries, University of Reading, Department of Agricultural Economics, Development Study No. 14.