Abstract
In this paper we consider the basic ideas, the implementation, and the application of an object-oriented decision support system (DSS), especially for multiple criteria decision making (MCDM). One main idea of the DSS is to utilize the features of different MCDM methods by integration. We discuss several approaches of integration esp. the combination of methods, the use of neural networks, and a generalization of network-like structures. Information acquisition is an essential part for any DSS. Beside aspects of interactive data entry, we discuss the utilization of historical data (especially from former decisions) for learning. By doing so the parameters of a method, some methods, or a network of methods can be tuned and we can answer questions like: Which method should be used? How can the parameters be adjusted? Is it useful to apply different methods and to aggregate their results to build a compromise solution? And how should this be done?
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