IWRA Proceedings

AbstractIn models of complex systems, interdependencies and heterogeneity of biophysical environment often lead to what are called nonconvexities – an irregular and rugged abstract surface describing the relationship between the parameters of the system and possible outcome states. Interrelated socioeconomic and biophysical processes can be represented at multiple scales which means that we incorporate complexity in natural resource use modelling. Integrating land and water is essential for capturing the dynamics of interrelated biophysical systems and it is itself a complex task. Artificial life techniques are useful for incorporating complexity in ecosystem modelling in general. By incorporating a high degree of social and spatial heterogeneity multi-agent systems could also represent “nested hierarchies” and phenomena emerging across different scales. Artificial Life is also an appropriate approach for capturing spatial phenomena in biophysical modelling. AL allows for the investigation of lower-level mechanisms that might lead to the development of higher-level structural and dynamical features in landscapes. Cellular modelling techniques, such as Cellular Automata (CA) and Markov Models have been applied to landscape modelling as well. The basic units for modelling locally interacting “objects” are cells on a grid, whose transition rules include their previous state and the state of the neighbouring cells. Advanced models use Geographical Information Systems (GIS) to store information about the state of cells in a landscape and feed this information back into the CA. The method of CA can also be used to represent the interactions of humanlike agents in physical or social space. Typically, the agents occupy positions on a two-dimensional grid of cells and the distances between them influence their interactions. Some authors employ a CA framework, which can be directly linked to soil information and hydrology modelling. Biophysical simulation models are usually calibrated at the micro-level whereas economic models operate at a rather aggregate level. Aggregating the biophysical data so as to link it to an economic model implies a considerable loss of statistical information. An integrated multi-agent system can, in principle, be structured so as to perfectly match the scale and structure of available data. This is a very interesting aspect because data disaggregation procedures are currently being developed that will help to infer micro behaviour from aggregate data consistently. Socioeconomic and biophysical data collections at multiple spatial and temporal scales might then be generated and fed into a multiple-agent programming model. The paper will be oriented on summarization of recent results in multi-agent systems application in various sub-areas of water management. As multi-agent systems are very suitable also as a framework for ambient intelligence environment, some first ideas about exploitation of these approaches in water management and especially for river basin management will be presented as well. All the results are based on a series of ongoing mutually interconnected research projects.