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Provide a succinct overview of MCDA
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# Dynamic Multi-Criteria Decision Analysis | ||
# Dynamic Multi-Criteria Decision Analysis | ||
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## Multi-Criteria Decision Making | ||
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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. | ||
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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. | ||
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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. | ||
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MCDA methods provide a ranking according to the set of assessed criteria, in the form of: | ||
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$$r = g(X, w, d)$$ | ||
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where: | ||
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- $g()$ refers to a given MCDA algorithm (see [JMcDM.jl](https://jbytecode.github.io)). | ||
- $X$ 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 $g()$ | ||
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When applied in conjunction with scenario analyses, locations are assessed at each decision | ||
point. | ||
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When conducting location selection, the analyses are applied to the initial conditions | ||
represented in the Domain. |