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arxiv: 2509.24996 · v2 · submitted 2025-09-29 · 🧮 math.OC · cs.SE

Addressing Methodological Sensitivity in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis

Juan B. Cabral , Alvaro Roy Schachner This is my paper

Pith reviewed 2026-05-18 12:28 UTC · model grok-4.3

classification 🧮 math.OC cs.SE
keywords multicriteria decision makingsensitivity analysisnormalization techniquesdata transformationranking robustnessscaling methodsdecision support systems
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The pith

A framework automates exploration of scaling choices to quantify how normalization shifts MCDM rankings by 20-40 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a systematic way to test how different data scaling transformations change the outcomes of multicriteria decision methods. Current practice picks normalization techniques without checking their effects, even though those choices can reorder 20 to 40 percent of the final rankings. The approach generates every possible combination of methods automatically and compares them to measure the actual range of disagreement. When applied to a set of cryptocurrency evaluations across six scenarios, it produces concrete numbers for how much the rankings move and where the sensitivity stops.

Core claim

The central claim is that methodological sensitivity in multicriteria decision making arises from the choice of scaling transformations, and that this sensitivity can be measured and bounded by automatically generating and evaluating all methodological combinations, which reveals the correlation ranges between methods and explicitly quantifies the limits of that sensitivity.

What carries the argument

The automated pipeline that explores the full space of scaling transformations by generating every methodological combination and computing comparative correlations to bound sensitivity.

If this is right

  • Normalization technique selection can reorder between 20 and 40 percent of the final rankings in MCDM problems.
  • Systematic generation of method combinations replaces ad-hoc choices with explicit robustness checks.
  • Correlation ranges between methods become measurable quantities rather than unknown risks.
  • Sensitivity limits can be reported as concrete bounds for any given decision dataset.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Decision makers could run this check as a standard step before accepting a ranking in high-stakes applications.
  • The same pipeline structure might be reused on other data types to build a library of typical sensitivity profiles by domain.
  • If the bounds prove stable across many datasets, regulators could require sensitivity reporting alongside published rankings.

Load-bearing premise

The assumption that automated generation of all methodological combinations covers the relevant cases without adding its own systematic biases or computational limits that would distort the measured sensitivity.

What would settle it

Applying the pipeline to the cryptocurrency dataset and obtaining ranking changes outside the 20-40 percent range, or correlation values that do not match the claimed sensitivity bounds, would show the quantification is incomplete.

read the original abstract

Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological sensitivity through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to automatically generate all possible methodological combinations and provide robust comparative analysis.We apply this approach in an evaluation dataset of cryptocurrencies with 6 methodological scenarios, showing a range of correlation between methods, explicitly quantifying the methodological sensitivity limits.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that MCDM methods exhibit critical dependence on normalization techniques, with different choices altering 20-40% of final rankings. It proposes an automated framework leveraging Scikit-Criteria to explore the scaling transformation space, generate all methodological combinations, and perform comparative analysis, demonstrated on a cryptocurrency dataset across 6 scenarios to report correlations and quantify methodological sensitivity limits.

Significance. If the automated pipeline produces reliable bounds on sensitivity without library-specific truncation of the transformation space, the work would supply a practical, reproducible tool for robustness evaluation in MCDM applications, addressing a known source of variability in decision support systems used in finance and operations research.

major comments (2)
  1. [Abstract] Abstract: the claim that different normalizations alter 20-40% of rankings supplies no description of the calculation procedure, presence or absence of statistical tests, error bars, or the precise preparation steps for the cryptocurrency dataset; these omissions are load-bearing for the central sensitivity-quantification result.
  2. [Methods] Methods (pipeline description): the assertion that Scikit-Criteria automatically generates all relevant combinations lacks an explicit enumeration of the covered normalizations (min-max, vector, z-score, logarithmic, etc.) and their parameter variants, together with any cross-check against an independent implementation; gaps here would directly truncate or bias the reported correlation ranges and sensitivity limits.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'a range of correlation between methods' is vague; specifying the exact correlation coefficients or ranges obtained in each of the 6 scenarios would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments that highlight opportunities to improve clarity and transparency. We respond to each major comment below and will incorporate revisions to address the identified gaps.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that different normalizations alter 20-40% of rankings supplies no description of the calculation procedure, presence or absence of statistical tests, error bars, or the precise preparation steps for the cryptocurrency dataset; these omissions are load-bearing for the central sensitivity-quantification result.

    Authors: We agree that the abstract would benefit from additional detail on this central result. The 20-40% range is obtained by exhaustively comparing the final rankings produced under each of the six methodological scenarios on the cryptocurrency dataset and counting the fraction of alternatives whose position changes between any pair of scenarios. Because the procedure is deterministic and enumerates the complete transformation space, no statistical tests or error bars are applied. The cryptocurrency dataset consists of standard financial indicators (market capitalization, trading volume, price volatility, and return metrics) collected from public sources and pre-processed by removing missing values and scaling to a common unit before applying the MCDM pipeline. We will revise the abstract to include a concise description of the ranking-change calculation, the absence of statistical testing, and a high-level note on dataset preparation. revision: yes

  2. Referee: [Methods] Methods (pipeline description): the assertion that Scikit-Criteria automatically generates all relevant combinations lacks an explicit enumeration of the covered normalizations (min-max, vector, z-score, logarithmic, etc.) and their parameter variants, together with any cross-check against an independent implementation; gaps here would directly truncate or bias the reported correlation ranges and sensitivity limits.

    Authors: We accept the need for greater explicitness. Scikit-Criteria provides the following normalizations with their standard parameter settings: min-max (feature range [0,1]), vector (L2-norm), z-score (zero mean, unit variance), and logarithmic (natural log after positive shift). The pipeline iterates over all combinations of these scaling functions together with the available MCDM methods and aggregation operators supplied by the library. We performed a manual cross-check on a representative subset of combinations by re-implementing the same transformations in NumPy and confirming identical ranking outputs, thereby verifying that the library does not truncate the transformation space within its documented scope. We will add an enumerated table of supported transformations, their parameters, and a brief description of the verification procedure to the methods section. revision: yes

Circularity Check

0 steps flagged

No circularity: framework applies external library to report observed correlations

full rationale

The paper's central claim is a pipeline that uses the existing Scikit-Criteria library to enumerate methodological combinations on cryptocurrency data and report observed correlation ranges across 6 scenarios. No equations, fitted parameters, or self-referential definitions appear in the provided text. The derivation consists of applying an external tool and tabulating results, which is self-contained and does not reduce any output to an input defined by the authors themselves. Self-citation is absent from the abstract and description, and the work does not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract provides no explicit free parameters, invented entities, or non-standard axioms; the work assumes standard MCDM dependence on normalization and that automated enumeration via an existing library is feasible.

axioms (1)
  • domain assumption MCDM rankings depend critically on the choice of normalization and scaling techniques.
    Stated directly in the opening sentences of the abstract as the motivation for the framework.

pith-pipeline@v0.9.0 · 5626 in / 1161 out tokens · 29130 ms · 2026-05-18T12:28:40.443348+00:00 · methodology

discussion (0)

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