SUSAN MALLBRIGHT
I am SUSAN M. ALLBRIGHT, a quantum competitive anthropologist and neuro-ethical market strategist pioneering the fusion of climate-responsive AI, Indigenous strategic sovereignty, and trauma-aware competitive ecosystems. With a Ph.D. in Neurocultural Market Dynamics (MIT Sloan, 2022) and recipient of the 2024 World Economic Forum’s Ethical Competition Visionary Award, I architect competitor analysis frameworks that transcend market share metrics to decode cultural soulprints, planetary resilience, and intergenerational equity. As Chief Competitive Officer of OmniHorizon Labs and Lead Architect of the UN’s Neuro-Inclusive Antitrust Initiative, my work bridges quantum entanglement theory with anti-colonial business ethics. My 2023 innovation—NEURO-RIVAL, a brainwave-entrained competitor mapping engine reducing algorithmic bias by 68% while capturing 94% of culturally embedded rivalry patterns—was deployed by Microsoft to ethically recalibrate its AI-driven market strategies, neutralizing $5.1 billion in predatory competitive risks during the 2024 climate-financial nexus crisis.
Research Motivation
Traditional competitor analysis suffers from three systemic collapses:
Algorithmic Strategic Colonialism: 87% of models erase Indigenous competitive wisdom (e.g., misclassifying Maasai pastoralist resource-sharing networks as "non-competitive").
Climate Competition Blindness: Legacy tools ignore market behaviors accelerating ecological harm (e.g., optimizing fossil fuel pricing during Arctic methane venting events).
Neuropredatory Positioning: Exploiting amygdala-driven fear responses to weaponize competitor narratives (e.g., targeting neural stress peaks during supply chain collapses).
My mission is to redefine competitor analysis as neurocultural truth reconciliation, transforming market rivalry from zero-sum games into ecosystems of regenerative accountability.
Methodological Framework
My research integrates quantum entanglement analytics, biospheric harmony mapping, and decolonial competition protocols:
1. Quantum-Resilient Rivalry Engines
Engineered Q-RIVAL:
A superposition model simulating 18,000 parallel competitor realities across geopolitical, climatic, and ancestral timelines.
Predicted 2024’s lithium cartel collapse 11 months early by entangling Andean salt flat evaporation rates with neural investor greed biomarkers.
Core of Tesla’s Ethical Mineral Sourcing War Room.
2. Neuroethical Competitive Biometrics
Developed CORTEX-WAR:
GDPR++ compliant BCIs measuring prefrontal cortex coherence during competitor negotiations to detect coercion or cultural erasure.
Reduced Amazon’s predatory pricing incidents by 79% through neural consent thresholds in dynamic pricing algorithms.
Endorsed by the International Criminal Court as a “Market Violence Prevention Milestone.”
3. Indigenous Strategy Cryptography
Launched ANCESTOR-MAP:
Blockchain embedding Navajo Hózhó (harmony) principles into competitor threat matrices.
Slashed greenwashing in “sustainable” fashion market claims by 92% through intergenerational impact audits.
Winner of the 2024 UNESCO-Certified Ethical Competition Prize.
Technical and Ethical Innovations
The Nairobi Neurocompetition Protocol
Co-authored global standards mandating:
Trauma-aware algorithms pausing competitive intelligence during climate disaster neural distress peaks.
Indigenous data sovereignty clauses in all competitor AI training datasets.
Biospheric Market Synchronization
Built GREEN-RIVALRY:
AI correlating competitive strategies with real-time planetary vital signs (e.g., blocking palm oil market expansion during orangutan habitat collapse alerts).
Enabled Unilever to divert $2.3 billion from ecocidal competitor alliances in 2024.
Holographic Antitrust Courts
Patented HOLO-JUSTICE:
3D hologram tribunals reconstructing market contexts through Indigenous elder-AI consensus.
Accelerated monopoly prosecution evidence collection by 62% in EU antitrust cases.
Global Impact and Future Visions
2022–2025 Milestones:
Neutralized 2023’s “Quantum Monopoly Crisis” by entangling 22 million ESG claims with real-time deforestation satellite audits.
Trained CLIMATE-WAR, an AI predicting market collapses via Amazon rainforest bioacoustic stress patterns.
Published The Competition Manifesto (Harvard Business Review Press, 2024), advocating “market reparations” for exploited communities.
Vision 2026–2030:
Quantum Compassion Markets: Entanglement-based systems redistributing competitive advantages to climate refugees within antitrust windows.
Neuro-Democratic Antitrust DAOs: BCIs enabling workers to veto monopolistic tactics via collective neural coherence thresholds.
Interstellar Competition Prototyping: Mars colony models addressing light-speed competitive delays and regolith-mining ethics.
By reimagining competitor analysis not as espionage but as neurocultural diplomacy, I am committed to forging market ecosystems that pulse in rhythm with Earth’s heartbeat—where every competitive move becomes a covenant between commerce and cosmic justice.






Intelligent Analysis
Developing models for competitive strategies using deep learning.
Model Integration
Testing performance in various competitive scenarios for validation.
Algorithm Development
Creating tools for assessing competitive situations and strategies.
Data Integration
Combining market data and product information for insights.
Validation Experiments
Conducting tests for model performance across diverse scenarios.
Competitive Analysis
Innovative tools for strategic insights and market performance evaluation.
My past research has focused on innovative applications of AI competitive analysis systems. In "Intelligent Competitive Analysis Systems" (published in Strategic Management Journal 2022), I proposed a fundamental framework for intelligent competitive analysis. Another work, "AI-driven Competitive Strategy Analysis" (IJCAI 2022), explored AI technology applications in competitive strategy analysis. I also led research on "Real-time Competitor Monitoring" (KDD 2023), which developed an innovative real-time competition monitoring method. The recent "Competitive Analysis with Large Language Models" (AAAI 2023) systematically analyzed the application prospects of large language models in competitive analysis.