# Biomimetic model for computing missing data imputation and inconsistency reduction in pairwise comparisons matrices

## TL;DR

This paper introduces a biomimetic model that fills in missing data and reduces inconsistencies in pairwise comparison matrices, inspired by biological processes.

## Contribution

The novelty lies in using a biomimetic approach with three biological-inspired phases for imputation and inconsistency reduction.

## Key findings

- The biomimetic model reliably converges to a consistent solution.
- The method effectively identifies and repairs inconsistencies in pairwise comparisons.
- The algorithm successfully fills in missing data through an iterative process.

## Abstract

A biomimetic model is presented to compute missing data imputation and reduce inconsistencies in pairwise comparisons matrices. The proposed regeneration method emulates three primary phases of a biological process: identifying the most damaged areas (by identifying inconsistencies in the pairwise comparison matrix), cell proliferation (filling in missing data), and stabilization (optimization of global consistency). An iterative algorithm is employed to correct inconsistencies and compute missing data imputations within the pairwise comparison matrix. The results demonstrate that the biomimetic approach is robust and reliably converges to a consistent solution.