Qualitative modelling with imprecise ecological knowledge: a framework for simulation

Ecological understanding is often imprecise and heterogeneous; relationships between different quantities and objects may only be expressed in roughly quantitative or even non-quantitative terms. We argue that there is a need for general time-driven simulation modelling systems capable of utilising these types of understanding, using vegetation dynamics as an example. Although work has gone into developing qualitative models in the past, it has not focussed on the needs of time-driven simulation and there is currently no off-the-shelf solution available. This paper presents a categorisation of the types of knowledge that comprise formal models, then uses the categorisation as the basis for exploring and developing a framework for time-driven simulation modelling with imprecise, heterogeneous knowledge. One of the key concepts presented is the explicit separation of all non-quantitative state variable values from their direction and rate of change. From this concept a general computational method is developed for updating non-quantitative state variables in time-driven simulation. First-order logic is advocated as a suitable representational vehicle and a modelling system that implements the proposed framework is briefly presented. We believe that the framework provides a useful step towards increasing the practical utility of available knowledge.