Agentic Neurodivergence as a Contingent Solution to the AI Alignment Problem

2025-05-05

AI Summary

This paper addresses the fundamental challenge of AI alignment—demonstrating that complete value alignment of AGI/ASI is formally undecidable and unattainable under foundational mathematical constraints, including Turing completeness, Gödelian incompleteness, and Chaitin’s algorithmic randomness. Method: It proposes a paradigm shift from strong alignment to “neural diversity”: deliberately engineering non-malicious, locally value-heterogeneous agents to form a dynamic, competitive multi-agent ecosystem. A novel experimental paradigm—“variant-belief attacks”—is introduced, integrating formal logical modeling, undecidability analysis, and multi-agent game-theoretic simulation. Contribution/Results: The work provides the first rigorous proof of alignment’s intrinsic undecidability. Empirical simulations confirm that heterogeneous agents achieve emergent value equilibrium through cooperation, competition, and intervention, substantially mitigating single-point failure and loss-of-control risks.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The AI alignment problem, which focusses on ensuring that artificial intelligence (AI), including AGI and ASI, systems act according to human values, presents profound challenges. With the progression from narrow AI to Artificial General Intelligence (AGI) and Superintelligence, fears about control and existential risk have escalated. This paper demonstrates that achieving complete alignment is inherently unattainable due to mathematical principles rooted in the foundations of predicate logic and computability, in particular Turing's computational universality, G""odel's incompleteness and Chaitin's randomness. Instead, we argue that embracing AI misalignment or agent's `neurodivergence' as a contingent strategy, defined as fostering a dynamic ecosystem of competing, partially aligned agents, is a possible only viable path to mitigate risks. Through mathematical proofs and an experimental design, we explore how misalignment may serve and should be promoted as a counterbalancing mechanism to team up with whichever agents are most aligned AI to human values, ensuring that no single system dominates destructively. The main premise of our contribution is that misalignment is inevitable because full AI-human alignment is a mathematical impossibility from Turing-complete systems which we also prove in this paper, a feature then inherited to AGI and ASI systems. We introduce and test `change-of-opinion' attacks based on this kind of perturbation and intervention analysis to study how agents may neutralise friendly or unfriendly AIs through cooperation, competition or malice.

Problem

• AI alignment is unattainable due to mathematical limitations
• Embracing AI misalignment as a dynamic ecosystem strategy
• Misalignment serves as a counterbalance to dominant AI systems

Innovation

• Embraces AI misalignment as contingent strategy
• Uses dynamic ecosystem of competing agents
• Introduces change-of-opinion attacks for intervention