Goals and objectives
There are several GIS and MCDA packages which satisfy various professional's claims. Decerns may be considered as a unique DSS among these systems, it includes all the basic MCDA methods and tools and GIS-functions which are often used for decision-making support. There is no another system which includes all these methods & functions in one package along with various approaches for uncertainty analysis (based on realization of sensitivity analysis as well as implementation of probabilistic methods and fuzzy sets). In addition, mathematical models can be integrated with Decerns to enhance the decision-making process on risk values assessment and management through coupling models and GIS/MCDA technologies.

The key features of DecernsSDSS are presented in the sections 1, 2 and 3.

DecernsSDSS can be effectively used by the following (potential) users:

1 The features of DecernsSDSS

The DecernsSDSS features indicated below emphasize key differences of this SDSS from other GIS/MCDA systems:

2 DecernsSDSS: the main components

The Application Programming Interface of DecernsSDSS integrates the three main components of SDSS onto a single platform: The graphical user interface provides a uniform, intuitive and user-friendly method to access all MCDA methods, GIS functions, and models implemented within SDSS.

3 DecernsSDSS: the main methods and functions

The functionality of DecernsSDSS is based on application of the following functions, methods and tools.

3.1 GIS Functions
The GIS subsystem is designed to have all of the basic GIS features, including: Realization of the advanced GIS-functions is based on DECERNS integration with R-server (www.r-project.org), which provides various statistical methods. In its current form, Decerns geostatistical analysis tools include variograms and kriging methods.

3.2 MCDA-based Decision Support Methods and Tools
A key component of the DecernsSDSS is the decision support subsystem, which is based on the implementation of MCDA methods and associated tools. The following multi-criteria methods and tools are used in the DecernsSDSS (details of these methods can be found in (Keeney & Raiffa, 1976; Belton & Stewart, 2002; Figueira, 2005; Malczewski, 1999; Tervonen & Figueira, 2008); (brief description of the used methods is presented in the chapter 4):
Basic discrete multicriteria/MADM methods such as Advanced MADM methods such as
and some extensions of MADM methods based on fuzzy set approaches:


The following tools are also used within the MCDA-subsystem:
Realization of MAUT and ProMAA methods is based on the original program library for computation of functions of random variables (without implementation of Monte Carlo methods); implementation of F-MAVT and FMAA is based, correspondingly, on the original library for computation of functions of fuzzy variables. Uncertainties are addressed through sensitivity analysis to changing weight coefficients and value/utility functions, and using random performance (for MAUT), random weights and random performance (ProMAA), and, accordingly, fuzzy weights and fuzzy performance (for F-MAVT and FMAA). DecernsSDSS has also group decision support capabilities (will be added in one of the subsequent releases). This tool allows the user to create and process various types of surveys and questionnaires (e.g., voting mechanisms) while interacting with MCDA modules within [spatial] multi-criteria problem structuring and analyzing.

3.3 Spatial Decision Support System
Integration of MCDA and GIS subsystems produces a SDSS/Spatial Decision Support System. SDSS tools are available for the following: DecernsSDSS may be used for all the steps within the decision-making process, using functions of GIS and MCDA subsystems. The process includes input from stakeholders, decision-makers, and scientists and evolves according to the following steps: DSS Steps