MENTAL MODEL · M6603

Evaluation Based on Distance from Average Solution

DecidemediumDecision Science

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Updated 2026-08-10

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INTRODUCTION

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CORE DEFINITION

Evaluation Based on Distance from Average Solution (EDAS) is a multi-criteria decision-making method that scores alternatives against the average solution rather than an idealized best. The procedure builds a decision matrix, computes each criterion's arithmetic mean to form the average solution, derives a positive distance (PDAS) and a negative distance (NDAS) for every alternative, weights and sums those distances, then normalizes them into an appraisal score. The alternative with the highest PDAS and lowest NDAS ranks first. Its defining condition is that criteria must be classified as benefit or cost types, since the two directions enter the distance formulas differently.

SCAFFOLDING EFFECT

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Reduce cognitive load

- Use mean benchmarking: compare each option against the average rather than an unreachable ideal. - Use direction labels: tag every criterion as benefit or cost before computing distances. - Use weight sensitivity: rerun the ranking with shifted weights to see how stable the top choice is.

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Anchor fast decisions

Anchoring on the average solution keeps every alternative inside the observed range, so scores stay interpretable and outliers cannot dominate the way they can when distances are measured from an ideal point. Benefit criteria reward positive deviation from the mean and penalize negative deviation, while cost criteria invert that logic. Summing weighted distances converts many incommensurable criteria into one appraisal score, which then yields a ranking. The method is compensatory, so a strong criterion can offset a weak one, and the weight vector effectively decides the outcome.

MINIMUM ACTION

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Practice this model in one real situation:

Build the decision matrix with alternatives as rows and criteria as columns. 2. Classify each criterion as benefit or cost and assign weights that sum to one. 3. Compute the average solution as the arithmetic mean of each criterion column. 4. Calculate PDAS and NDAS for every alternative and multiply by the weights. 5. Normalize the weighted sums into appraisal scores and rank the alternatives.

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Source support: Explicit

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en.wikipedia.orghttps://en.wikipedia.org/wiki/Multiple-criteria_decision_analysisZH · Explicit
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