Idealisation is the process of finding simple representations of the real-world whilst conceptualising a model. There are three ways to limit complication in a model of a complex real-world: by focussing the scope of the modelling process onto a clearly defined issue; b yidealising elements of the real-world during model conceptualisation; and by simplifying the implemented simulation program. Careful idealisation has the greatest potential for increasing model tractability whilst generating insights during the model design process. The Forest Land Oriented Resource Envisioning System (FLORES) project deals with social forest landscapes which are highly complex. Benefits of idealisation are demonstrated using six examples from this modelling work. These examples encompass issues dealing with land tenure, forest management, economic values, social diversity, communication and collaboration. Each example illustrates a different method to achieve an idealisation which yields insights relevant for policy players. A number of lessons about idealisation are also identified: (1) sometimes it is only possible to recognise what is key by omitting it; (2) an effective idealisation is not just achieved by leaving things out, or adding them back in; it can also be achieved by restructuring the representation; (3) it is important challenge the use of different units where consistency is possible; (4) it is easier to keep a simple model simple, than to make simple modifications to a large model. Similarly, it is easier to generate insights with a simple concept for a sub-model than with a simple modification to an existing model; and (5) even the most useful idealisations may have a limited shelf-life.
Haggith, M.; Prabhu, R. Unlocking complexity: the importance of idealisation in simulation modelling. Small-scale Forest Economics, Management and Policy (2003) 2: 293-312.