## 4 Discussion
### MCDA as Decision-Making Options for Computational Models
The determination of whether the decision process from the real system is more accurately represented as a ratio assignment or as an approximation should be defendable based on the data known about the system. This will reinforce the credibility of the technique selection and the model construction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ratio** & & & \\
**Assignment Technique** & **Agent Based Modeling** & **Discrete Event Simulation** & **System Dynamics** \\ \hline Direct assignment technique & Known * or accepted * criteria that direct an agent towards their goals or one decision outcome or another & Known or accepted decision path probabilities; Known or accepted resource schedules & Known or accepted coefficient values within ordinary differential equation (ODE), partial differential equation (PDE) or difference equation (DE) \\ \hline Simple multi attribute rating technique (SMART)/ & There exists an accepted least important criterion and the remaining criteria are weighted relative to this option. Each agent population & A least acceptable path is known and the remaining options are weighted relative to this option. Weighting preferences can vary by entity type. & The ODE, PDE, or DE contains a value whose coefficient is known to be least important. Remaining coefficients are weighted relative to this coefficient. \\ \hline  & Order of importance is known/accepted but the most important element is not always the top ranked. Current rankings and known important criterion are used to establish weightings of remaining criteria. & Top ranked path or most desirable schedule are known but do not always remain top ranked during execution. & Coefficient weightings are intended to weight towards a specified most important criterion; however, new weights are generated based on magnitude of change from previous check to incorporate stochasticity. \\ \hline Simple pairwise comparison & No established known or accepted ranking of criteria weightings. Agent compares all available criteria to accumulating weighting scores. & No established known or accepted ranking of criteria & No established known or accepted ranking of criteria (e.g., weightings. Entities or resources compare all available criteria to accumulate weighting scores for path probabilities or scheduling. & No established known or accepted ranking of criteria (e.g., coefficient) weightings. \\ \hline \hline \end{tabular}  
* The term _known_ reflects that a weighting is supported by empirical evidence.
* The term _accepted_ reflects general agreement among the model’s builders and stakeholders.  
\end{table} 
Table 18: Considerations for incorporating ratio assignment techniques into ABM, DES, and SD modeling paradigms. Many simulation platforms natively allow for some instance of an equal or percent-based choice to be implemented within the model, whether these choice options exist at the system level in the form of flows [86], at the process level in the form of path logic [26; 87], or at the individual level to capture the decisions of individual agents [43]. However, the implementation of decisions that are based on the rankings, weightings, or comparisons between multiple attributes is not generally as straightforward of a task. Tables 18 and 19 are intended to inform the model builder of circumstances under which the reviewed approximate and ratio assignment techniques may be of use. Simulation and domain expertise are still required to properly implement and test the technique. The application of a MCDA technique within a simulation should fill a necessary gap, maintain traceability to the model's requirements, and not introduce new gaps or unnecessary challenges to the simulation [88; 89; 90; 82]. How criteria weightings are conceptualized and how they are implemented in practice can vary greatly across modeling paradigms. For instance, consider the equal weighting method from the approximate techniques.