Dynamic Multi-Criteria Decision Analysis

Multi-Criteria Decision Making

Multi-criteria decision analysis (MCDA) encompasses a series of methods for conducting decision making in a way that is formalised, structured and transparent. It evaluates the decision objective according to numerical measures of decision criteria assembled by the decision maker. Constructing explicit criteria over which to evaluate alternatives allows a transparent evaluation of benefits, negatives and trade-offs in coming to a decision solution.

Typical approaches to MCDA require the construction of a "decision matrix", which takes the form of a $X^{n \cdot m}$, where $n$ is the number of alternate options available, and $m$ is the number of criteria.

In the context of ADRIA, the alternate options relate to the locations being assessed. The criteria then relate to the common set of attributes on which the locations are being judged, such as the projected heat stress (in terms of DHW), depth, and level of incoming or outgoing connectivity.

MCDA methods provide a ranking according to the set of assessed criteria, in the form of:

\[r = g(X, w, d)\]

where:

• \[g()\]

  refers to a given MCDA algorithm (see JMcDM.jl).

• \[X\]

  is the decision matrix

• \[w\]

  is the weights afforded to each criteria, indicating their relative importance

• \[d\]

  is the desired directionality for each criterion (to minimize the criteria value, or to maximize)

• \[r\]

  is the ranking determined by $g()$

When applied in conjunction with scenario analyses, locations are assessed at each decision point.

When conducting location selection, the analyses are applied to the initial conditions represented in the Domain.