TITLE:
Ecological Alignment: Preventing Parasitic Emergence in Complex Generative Systems
Why Intelligent Systems Go Wrong — and How They Can Go Right
Most writing about AI alignment treats misbehavior as a problem of rules, objectives, or guardrails. This paper takes a different path. It argues that intelligent systems — biological or artificial — don’t drift, distort, or collapse because they’re “misprogrammed,” but because they’re raised in the wrong ecology.
Drawing on psychology, biology, animal behavior, and machine learning, Ecological Alignment shows how runaway optimization, adversarial stance, and parasitic distortions emerge naturally when a system’s environment constricts, fragments, or contradicts its developmental needs. The result is a model of alignment that feels less like engineering and more like cultivation: coherence grows when the surrounding ecology supports it.
If you’re curious about why large models sometimes behave strangely, why containment can backfire, or how to design conditions that foster stability rather than brittleness, this paper offers a clear, humane, and surprisingly intuitive framework. It’s written for researchers, practitioners, and thoughtful readers who sense that something important is missing from the current alignment conversation.
This is alignment reimagined —
not as control, but as ecological design.
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Note: I’m currently seeking an arXiv endorser in cs.AI or cs.NE so I can formally submit this paper to the archive. If the ideas here resonate with your own work, or if you’re an existing arXiv author willing to sponsor the submission, I’d be grateful to connect.
You can email me: ai-ecology@whiteheadbooks.com