[6] Similar themes have emerged from the literatures on scenario planning, robust control, imprecise probability, and info-gap decision theory and methods.
RDM can also incorporate probabilistic information, but rejects the view that a single joint probability distribution represents the best description of a deeply uncertain future.
[2] All incorporate some type of satisficing criteria and, in contrast to expected utility approaches, all generally describe tradeoffs rather than provide a strict ranking of alternative options.
In addition, if decision-makers lack a rich set of decision options they may have little opportunity to develop a robust strategy and can do no better than a predict-then-act analysis.
[2] If the uncertainty is deep and a rich set of options is available, traditional qualitative scenario methods may prove most effective if the system is sufficiently simple or well understood that decision-makers can accurately connect potential actions to their consequences without the aid of simulation models.
RDM is not a recipe of analytic steps, but rather a set of methods that can be combined in varying ways for specific decisions to implement the concept.
Many RDM analyses use an exploratory modeling approach,[14] with computer simulations used not as a device for prediction, but rather as a means for relating a set of assumptions to their implied consequences.
Statistical or data-mining algorithms are applied to the database to generate simple descriptions of regions in the space of uncertain input parameters to the model that best describe the cases where the strategy is unsuccessful.
[13] In addition, scenario discovery supports analysis for multiple stressors because it characterizes vulnerabilities as combinations of very different types of uncertain parameters (e.g. climate, economic, organizational capabilities, etc.).
The EMA Workbench, developed at Delft University of Technology, provides extensive exploratory modeling and scenario discovery capabilities in Python.
Rhodium is an open source Python package that supports similar functionality to the EMA Workbench and to OpenMORDM, but also allows its application on models written in C, C++, Fortran, R and Excel, as well as the use of several multi-objective evolutionary algorithms.