{
  "$schema": "https://ps-wiki.github.io/schema/v1/term.schema.json",
  "id": "multi-criteria-decision-analysis-based-metrics",
  "title": "Multi-Criteria Decision Analysis-Based Metrics",
  "description": "(MCDA)-Based Metrics.",
  "language": "en",
  "tags": [
    "resilience",
    "metrics",
    "ieee"
  ],
  "related": [],
  "breaking": false,
  "dates": {
    "created": "2025-03-15",
    "last_modified": "2025-06-20"
  },
  "authors": [
    {
      "name": "Jinning Wang",
      "url": "https://jinningwang.github.io"
    }
  ],
  "content": {
    "sections": [
      {
        "order": 1,
        "id": "definition-in-an-ieee-technical-report-by-doe-gmlc",
        "title": "Definition in an IEEE Technical Report by DOE GMLC",
        "type": "definition",
        "source_keys": [
          "chiu2020resilience"
        ],
        "page": "p13",
        "body_md": "> MCDA metrics generally try to answer the question, “what is the current state of the electric system’s resilience, and what enhances its resilience over time?”\n> These metrics can be used to assess the system’s baseline resilience relative to other systems.\n> They typically include categories of system properties beneficial to resilience, such as robustness, resourcefulness, adaptivity, and recoverability.\n> The application of these metrics requires following a process to review to what degree these properties are present within the system under analysis.\n> This usually involves collecting data through surveys, developing weighting factors, and performing calculations to obtain numerical scores.\n> MCDA metrics are used to calculate a resilience index (RI) that accounts for about 1,200 attributes grouped in 350 categories to characterize system resilience.\n"
      }
    ]
  }
}