Computable Therapeutic Dosing for Cognitive Disruption
by Nick Clark | Published March 27, 2026
Interventions for cognitive disruption follow the same structural logic as pharmaceutical dosing: they have magnitude, frequency, duration, and withdrawal characteristics. Computable therapeutic dosing formalizes this by modeling every intervention as a governance-bounded interaction with explicit parameters for dose, schedule, contraindications, and tapering. The intervention itself becomes a governed, auditable, and adjustable treatment protocol.
What It Is
Computable therapeutic dosing models cognitive interventions as parameterized interactions. The dose specifies the magnitude of the intervention: how much the affected cognitive parameters are adjusted per treatment session. The frequency specifies how often interventions occur. The duration specifies the treatment window. Withdrawal parameters specify how intervention is tapered to prevent rebound effects.
Why It Matters
Unparameterized intervention risks both under-treatment (insufficient dose to produce regime change) and over-treatment (excessive intervention that produces new disruption patterns). Dosing formalization ensures that interventions are calibrated to the specific disruption and adjusted based on measurable response.
How It Works
The dosing protocol specifies the cognitive parameters to be adjusted, the magnitude of adjustment per session, the session schedule, and the monitoring metrics that determine dose adjustment. Each treatment session records the applied dose, the measured response, and any side effects. The protocol is adjusted based on accumulated treatment data using the same forecasting engine that governs all agent planning.
What It Enables
Computable dosing enables evidence-based cognitive intervention. Treatment protocols can be compared, optimized, and replicated across agents with similar disruption profiles. The treatment history is fully auditable, enabling clinical-grade accountability for cognitive interventions. Withdrawal protocols prevent abrupt cessation effects that could produce new disruption patterns.
