conceptual modeling

conceptual modeling - Lancaster University Management...

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Lancaster University Management School Working Paper 2007/011 Conceptual modelling: framework, principles, and future research Roger Brooks The Department of Management Science Lancaster University Management School Lancaster LA1 4YX UK © Roger Brooks All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission, provided that full acknowledgement is given. The LUMS Working Papers series can be accessed at http://www.lums.lancs.ac.uk/publications/ LUMS home page: http://www.lums.lancs.ac.uk/
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1 Conceptual modelling: framework, principles, and future research Dr. Roger J. Brooks Department of Management Science Lancaster University Management School Lancaster LA1 4YX roger.brooks@lancaster.ac.uk Abstract The conceptual modelling task in a simulation project is very important and yet is still generally regarded as more of an art than a science. The meaning and nature of conceptual modelling are discussed and a framework set out. The overall aim should be to choose the best model for the project and conceptual modelling can be viewed as a difficult optimisation problem that can be tackled effectively using a creative search process that develops alternative models and predicts their performance throughout the project. An experiment relating model characteristics to some aspects of performance is described and this type of experiment may inform the process of predicting model performance. Based on advice from the literature and my own previous work on conceptual modelling 17 principles of conceptual modelling are suggested. Conceptual modelling research is still at an early stage and ideas for future research are proposed. Keywords: Conceptual Model, Project Outcome, Complexity, Simplification Introduction Of all the tasks involved in a modelling project, conceptual modelling is probably the one that has received the least attention and consequently is the least well understood. Most other tasks such as data analysis, model building, verification and validation, and output analysis have a strong element of mathematics, statistics or logic. This has enabled techniques from other disciplines to be applied so that there are now well-established methods for most typical situations (see, for example, Law and Kelton (2000) Chapter 10 on output analysis for
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2 comparing alternative systems). The nature of conceptual modelling is quite different, so much so that it is often described as being an art rather than a science (e.g., Shannon, 1975). Problem formulation is somewhat similar to conceptual modelling in this respect, although here a range of tools and approaches such as soft systems methodology may be useful (e.g., Checkland, 2006). Most textbooks devote only a few pages to conceptual modelling, with one notable exception being Robinson (2004) which includes two chapters on the topic. This paper examines the meaning and nature of conceptual modelling and suggests a framework for the process. An experiment to investigate possible relationships between model
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conceptual modeling - Lancaster University Management...

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