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
- Dimitri Konstantas (ORCID: https://orcid.org/0000-0002-2664-9348)
- Shervin Zakeri (ORCID: https://orcid.org/0000-0002-3430-100X)
- Prasenjit Chatterjee
Institutions
- University of Geneva (CH)
- Széchenyi István University (HU)
- Indian Institute of Engineering Science and Technology, Shibpur (IN)
- Yuan Ze University (TW)
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