process is structured.
As the tool walks you through, you provide answers to the questions you see below.
The questions are grouped in four sections:
1. Problem typology: Defines the type and structure of the decision-making problem 2. Preference model: Defines what type of model you would like to apply 3. Elicitation of preferences: Defines the type, modality and frequency of model preferences 4. Exploitation of the preference relation induced by the preference model: Defines the strategy used to derive and enrich the decision recommendation
key decision-making features
simple user interface
Here is our intuitive and interactive set of questions. Answering them will lead you to a subset of methods relevant to your problem. Under each question you can find its description, while the description of an answer appears when you move the mouse on it.
Enjoy your journey with the MCDA-MSS!
Your recommended method(s) will automatically appear at the bottom.
The numbers in brackets next to each answer indicate the number of methods that will be recommended if you choose such answer. Please note that this a dynamic process, so the number changes according to the combination of answers that you will give.
Section 1: Here you can define how the problem is framed by (i) choosing the type of decision-making challenge under consideration and (ii) describing the criteria used to assess the alternatives.
Section 2: Here you can define what type of model you would like to apply, accounting for (i) how the input data is used by the method, (ii) comparison of criteria performances, (iii) compensation between the criteria performances, (iv) aggregation of the criteria evaluations, and (v) the capacity of the MCDA methods to deal with inconsistent preference information.
Section 3: Here you can define what type of preferences information you can provide, how and with what frequency.
Section 4: Here you can decide how the preference relation induced by the preference model can be exploited to derive or enhance the decision recommendation.
Once you answer a part or all questions from the four Sections, the method(s) recommended for
your decision-making problem will automatically appear in the list below.
If you click on the ⓘ you will see a description of each method with the answers that you chose in bold.
When present, the column “Missed features” shows the decision-making features that you selected and which are not supported by the recommended methods.
The users of our MCDA-MSS are envisioned to be:
1. Experienced MCDA users (including teachers) who want to use the tool for educational purposes;
2. MCDA methods developers and researchers who want to test, compare, and comprehend the characteristics of the existing MCDA methods and/or to develop new and more advanced ones;
3. MCDA methods developers and practitioners (including consultants and analysts) who want to support actual DMs in applying these methods in real-life Decision-Making Problems.
Background literature on the features included in MCDA-MSS can be found here and here.
“Based on dedicated requests for inclusion and our own selection of valuable candidates, we are planning upcoming updates to our software in consecutive batches (see also Disclaimer 5 below). The first one will include:
|Interval-based PROMETHEE clustering||Click here|
|TOPSIS-hierarchical and non-monotonic criteria||Click here|
|LSP: logic scoring of preference||Click here|
1 Laboratory of Intelligent Decision Support Systems
Institute of Computing Science
Poznań University of Technology
2 Piotrowo Street
2 Environmental Decision Analytics Branch
Center for Environmental Solutions and Emergency Response
U.S. Environmental Protection Agency
45268 Cincinnati (OH)
3 Laboratory for Energy Systems Analysis
Technology Assessment Group
Paul Scherrer Institut
5232 Villigen PSI
4 Decision Engineering for Sustainability and Resilience (DESIRE) Laboratory
Leiden University College (LUC)
Faculty of Governance and Global Affairs, Leiden University
Anna van Buerenplein 301
2595 DG The Hague
+31 (0)70 800 9020
+48 61 665 30 22