Adaptive Systems & Learning Dynamics

Primes about how systems and organisms adjust to their environment over time: adaptive feedback and learning (learning curves, predictive coding, metaplasticity, coevolution), and system-level resilience built through diversity and monitoring (complex adaptive systems, diversity, maintenance, systems thinking).

23 primes in this family — primes that sit near one another in abstraction space (k-means over structural-signature embeddings). Each is shown with its short description.

• Adaptation — Systems adjust to conditions.
• Attentional Capacity — Finite pool of selection bandwidth whose exceeded supply degrades processing through interference, slowing, or capture.
• Attractor Selection and Basin Control — System dynamics directed toward stable states via basin manipulation.
• Coevolution — Reciprocal, mutually-selective adaptation between coupled systems.
• Complex Adaptive System — Interacting agents adapt their local rules to experience while their aggregate behavior creates an emergent environment that feeds back on what the agents learn, producing path-dependent organization without central control.
• Diversity — Maintaining functionally distinct types within a system so that variation provides resilience and coverage that uniformity cannot.
• Exaptation — A feature co-opted for a function other than the one it arose for.
• Foresight — Disciplined anticipation of plural possible futures to keep present action adaptive across the range of plausible outcomes.
• Inoculation Theory — An adaptive system is made resistant to a future threat by pre-exposing it to a weakened form plus a successful refutation, so its defensive machinery activates and generalizes in advance.
• Invasive Species — A newcomer enters a system whose native controls are absent or weak against it, and outpaces them to spread and displace incumbents before the system can adapt.
• Learning — Durable, experience-driven update of an agent's internal state that carries forward to alter later behavior or prediction.
• Learning Curve Effects — Unit cost falls predictably with cumulative production experience.
• Learning-Substrate Contamination — An adversary writes future behavior indirectly by selectively contaminating the experience or evidence from which an agent durably updates, so the agent later and faithfully enacts a distortion installed before the decision.
• Maintenance — Sustained preventive work that keeps a system's intended function intact against inevitable degradation, acting ahead of failure rather than repairing after it.
• Metaplasticity — A system's capacity to change is itself modulated by prior activity, so a slow second-order process governs how readily the fast first-order adaptive process can operate.
• Monitoring — Continuously observing a system's state to detect deviation from expected behavior and trigger a response, separating genuine signal from routine noise.
• Pedagogy — Deliberate other-directed structuring of a learner's encounter with content to cause durable change in their capability.
• Predictive Coding — A system predicts its input and propagates only the prediction error.
• Regime Change — A discontinuous flip of a system from one stable operating regime to a qualitatively different one, where the same inputs produce fundamentally different responses on either side of a feedback-driven threshold.
• Stressor Induced Adaptation — Bounded stress that costs short-term performance to build durable long-term capacity (hormesis, progressive overload, desirable difficulties).
• System Archetypes — Recurring configurations of reinforcing and balancing feedback loops that generate the same characteristic system behavior across different domains, enabling structural diagnosis instead of symptom-chasing.
• Systems Thinking — Analyzing a whole through the relationships and feedback among its parts.
• Variation Strategies — Deliberately injecting controlled variation into a system and selecting from the results to explore alternatives, accelerate learning, and gain robustness.