Mind · Strategy
StoppingRule
Stop integrating stressor handling into a design once it survives a fresh round of unseen simulated stressors, or once further integration costs more than it buys; tested by experiment, not proven from a probability distribution
Definition
Mechanism
To make a design robust without predicting the future, imagine many possible ExogenousStress events, including absurd ones, and change the design so it survives each, integrating the changes into one structure. The question is when to stop. A statistical stopping rule assumes a distribution and stops when estimates are accurate enough; here no probabilities are assigned, so that rule cannot apply. Instead, after integrating one round, test the design against a new round of unseen simulated stressors. Complex systems tend to settle into a small number of stable states that each survive many different inputs, so a design that survives enough stressors starts surviving ones it has never met: it has generalised, or reached criticality. When most of a fresh round is survived, stop. Cost is the second dimension: integration stops earlier if adding more costs more than it protects, and building partly integrated functions, as a FractionalMetaModel does, lowers that cost so the generalisation point can actually be reached.
Preconditions
- Stressors can be simulated and the design's survival of each can be judged
Invariants
- No probability is assigned to stressors; the rule is checked by experiment
- Stopping is signalled by survival of stressors not used in the design
- Integration cost is weighed alongside generalisation
Risks
Failure modes
Stopping after the stressors used in design are survived, without testing unseen ones
Ignoring integration cost so the process never reaches a usable design
Re-introducing probability estimates and missing possible but unestimable stressors
Relationships
Builds on
References
Physics
An external event or change in requirements that the current shape does not handle and that forces it to change
An implementation that is at once one working model and a partial implementation of several others, so that some functions can be completed or removed with a small change
Nothing in this library builds on this pattern yet.
Reference
