The Painted Wolf Decision Optimizer

This study introduces the Painted Wolf Decision Optimizer (PWO), the first deterministic, bio-inspired decision framework for discrete multi-criteria decision making (MCDM) derived from specific observed decision behaviors of African wild dogs, including quorum sensing, dominance hierarchy, collective voting, and experience-based learning. Unlike conventional nature-inspired metaheuristics that rely on stochastic search across continuous domains, PWO defines a new class of Discrete Bio-Inspired Decision Operators. It formalizes key ethological mechanisms of Lycaon pictus: quorum sensing, hierarchical dominance, and reinforcement-based learning. Additionally, it encodes the principle of survival-through-precision, demonstrating how coordinated strategic alignment can outperform structural dominance under resource constraints, inspired by the high hunting efficiency of African wild dogs. PWO integrates three cognitive weighting components: subjective collective preferences (sneeze-based voting), objective data variability (entropy weighting), and experiential reinforcement (pack memory). These are fused via the Mathematical Compromiser, a convex operator that assigns internal trust based on signal stability rather than fixed weighting rules. Applied to European EV gigafactory location selection, PWO reconciled tensions between cost-driven executive preferences and sustainability-based performance indicators, identifying Spain as the most robust alternative. Sensitivity analysis across the dominance spectrum (D = 0 → 1) and multiple episodes confirmed ranking stability without rank reversal. The Markovian update formalizes longitudinal learning for future multi-episode applications. Beyond discrete selection, PWO functions as a diagnostic and competitive resilience mechanism, revealing whether decisions are shaped by leadership authority, structural necessity, historical trends, or precision-based survival logic. It provides a transparent and strategically adaptive architecture for sustainable governance and high-stakes competitive decision environments.

Authors

Institutions

Publication Details

Journal
Computers
Published
2026-07-16
DOI
https://doi.org/10.3390/computers15070452
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Painted Wolf Decision Optimizer

Dimitri Konstantas, Shervin Zakeri, Prasenjit Chatterjee
Computers
Evolutionary Game Theory and Cooperation
article

The Painted Wolf Decision Optimizer

Dimitri Konstantas, Shervin Zakeri, Prasenjit Chatterjee
article en

Abstract

This study introduces the Painted Wolf Decision Optimizer (PWO), the first deterministic, bio-inspired decision framework for discrete multi-criteria decision making (MCDM) derived from specific observed decision behaviors of African wild dogs, including quorum sensing, dominance hierarchy, collective voting, and experience-based learning. Unlike conventional nature-inspired metaheuristics that rely on stochastic search across continuous domains, PWO defines a new class of Discrete Bio-Inspired Decision Operators. It formalizes key ethological mechanisms of Lycaon pictus: quorum sensing, hierarchical dominance, and reinforcement-based learning. Additionally, it encodes the principle of survival-through-precision, demonstrating how coordinated strategic alignment can outperform structural dominance under resource constraints, inspired by the high hunting efficiency of African wild dogs. PWO integrates three cognitive weighting components: subjective collective preferences (sneeze-based voting), objective data variability (entropy weighting), and experiential reinforcement (pack memory). These are fused via the Mathematical Compromiser, a convex operator that assigns internal trust based on signal stability rather than fixed weighting rules. Applied to European EV gigafactory location selection, PWO reconciled tensions between cost-driven executive preferences and sustainability-based performance indicators, identifying Spain as the most robust alternative. Sensitivity analysis across the dominance spectrum (D = 0 → 1) and multiple episodes confirmed ranking stability without rank reversal. The Markovian update formalizes longitudinal learning for future multi-episode applications. Beyond discrete selection, PWO functions as a diagnostic and competitive resilience mechanism, revealing whether decisions are shaped by leadership authority, structural necessity, historical trends, or precision-based survival logic. It provides a transparent and strategically adaptive architecture for sustainable governance and high-stakes competitive decision environments.

ComputersVol. 15(7)
University of Geneva (CH), Széchenyi István University (HU), Indian Institute of Engineering Science and Technology, Shibpur (IN), Yuan Ze University (TW)
Openalex Percentile: Top 4%
Evolutionary Game Theory and Cooperation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.