MENTAL MODEL · M8313

Variable Weight Comprehensive Evaluation

DecidemediumDecision Science

Included

Updated 2026-08-11

Loading revision record…

INTRODUCTION

English translation pending.

CORE DEFINITION

Variable weight comprehensive evaluation is a technique in multi-criteria decision analysis, developed mainly in Chinese systems engineering and safety-assessment research, and related to the barrel principle that the weakest dimension limits the whole. Its core proposition is that fixed weights let a severe weakness be offset by strength elsewhere, so weights should rise for indicators whose measured values are extreme. The key qualification is that the weighting function must be designed and calibrated, since a poorly shaped function produces unstable weights that swing with small changes in the data.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Use indicator set: define the evaluation criteria and assign their baseline weights. - Use state function: specify how a weight rises as an indicator value worsens. - Use sensitivity compare: run the same case under fixed weights and see what changes.

anchor

Anchor fast decisions

A weighted average is compensatory: a good score on one criterion can pull the total up enough to hide a failing score on another. That suits criteria that substitute for one another, but not dimensions that are hard requirements, as in safety. Making the weight depend on the observed value removes the compensation where it matters, because a poor score lowers its term and increases its influence. The mechanism encodes a judgment about which dimensions cannot be traded, so the weighting function carries the policy rather than the arithmetic.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

Define the evaluation criteria and assign baseline weights to each. 2. Specify a state function describing how weight changes as a value worsens. 3. Compute the dynamic weights from the actual measured values. 4. Aggregate the weighted scores into a single composite total score. 5. Compare against the fixed-weight result and examine the sensitivity.

Check to track your progress (stored locally)

Learning progress0%

account_treeGenealogyexpand_more

menu_bookReferencesexpand_more

Source support: Explicit

link
openalex.orghttps://openalex.org/W2373279711ZH · Explicit
verified

RELATED MODELS