C.40:4.10 - Make human contributions usable in continuing development

Use this branch when a person can recognize, demonstrate or explain something useful that the current search does not obtain affordably. The contribution may change an editable candidate, the examples used to construct it, the examination conditions or which branch deserves continuation. It must reach that particular operation. Asking for feedback and collecting comments do not by themselves change the developed material. Use a sufficient existing construction, a direct professional correction or an ordinary comparison when that finishes the work more cheaply.

Choose a contribution the person can actually supply. Show the candidate in a form that exposes the relevant behavior and permits a consequential response. A person who can perform an example need not be able to write a complete rule; a person who can explain one exception need not be able to implement the whole policy. Name the missing contribution and make its effect inspectable:

These are alternative inputs, not successive stages. Combine them when their results support the same development. A selected demonstration can suggest a rule; a failed rule can expose a missing observation or a better training task. If the required input is unavailable to the later performer, obtain it there or change the candidate. Training cannot manufacture an unobserved fact.

Construct a representation that supports the intended change. For a policy, keep :4.8’s context-to-action meaning and available-input boundary. When people need to inspect and change conditional behavior, an executable rule set can be a useful alternative to an opaque parameterized function. Define the input features and units, permitted conditions, actions and evaluation rule. Conditions can compare a feature with a threshold, two compatible features or a remembered value; introduce products, powers or time offsets only when their meaning and cost are understood. A small table or hand-written procedure may already express the needed behavior.

Specify what happens when several rules match and when none matches. Executing the first matching rule, selecting the strongest action and performing all matching actions are different procedures. Preserve priority, aggregation, ties and fallback when they matter. A printed coefficient called confidence can be merely an evolved decision weight; it has no calibrated probability meaning without its own basis. CMP.12 supplies execution and translation, including binding, control and the needed preservation argument; CMP.10 supplies representation choice when access and update costs matter.

Obtain a candidate by direct construction, finite enumeration or search over those admitted expressions. In a rule search, start with a usable incumbent and any compatible advice. Vary a threshold or feature, add or remove a condition, exchange whole rules, change priority or combine conditions for the same action. Combining conditions with AND narrows where that rule fires; it does not automatically combine the useful behavior of its parents. Reject malformed expressions and evaluate each child’s complete behavior, including interactions and fallback. The local operations must preserve any protected conditions or return their failure; a desirable parentage cannot supply that result.

Separate a hypothesis from a requirement that later variation must preserve. Advice supplied as a mutable seed permits departure. A binding action restriction needs an effective operation that excludes prohibited outputs, a protected part of the representation or examination sufficient for the required claim. A large penalty or repeated demonstration can still permit a violating candidate. If advice conflicts with the current requirement, resolve that conflict with the responsible practitioner before treating either as a training target.

Choose what the rule is being fitted to. Fitting recorded outcomes answers a prediction question; matching an existing program answers a behavior-copying question; obtaining useful outcomes by acting answers a policy question. CMP.7 constructs the selected learner. For an opaque incumbent, query it on relevant inputs and fit a rule set to its returned decisions. Compare agreement with the incumbent separately from correctness against available subject evidence. Queries generated within individual feature ranges can still describe impossible joint situations; keep the query domain and later use meaningful. If original data are unavailable, the incumbent can still supply target outputs, but it supplies no independent ground truth about their quality.

Retain complexity as a separate consideration when a shorter rule set would help inspection or use. Counting conditions or penalizing their number can expose a fidelity–size trade-off. It does not measure whether a particular person understands the rules. Compare plausible simpler alternatives, and retain consequential exceptions even when they are infrequent. Removing a rule that never fired in a sample preserves that sample’s answers at most; the rule may handle a legitimate unseen case. A claimed behavior-preserving simplification needs the corresponding domain argument or bounded comparison. For example, deleting a false condition from a conjunction can make a formerly impossible rule fire; it is not an innocent shortening.

Return the behavior to the contributor and revise what changed. Present a relevant input, the rule or construction actually used, the returned action and its supported consequence. Invite the contribution that can change the next move: correct an input meaning, supply an exception, show another action, choose a trade-off or challenge the objective. A natural-language explanation of a rule must preserve its conditions and action selection. A convincing explanation of an approximate copy is still not an explanation of every original decision.

After an edit, distinguish three returns. A change meant only to make the expression easier to read returns to preservation of behavior. A changed action rule returns to comparison of the policy and its consequences under :4.8 or direct examination. A changed purpose or valued outcome returns to the receiving question, including :4.3 when the problem and available ways now need development together. Compare the revised candidate with the retained one on the affected cases and on still-required earlier behavior. Retain a qualified improvement, repair the failed change or restore the earlier candidate. When actual use is appropriate, observe the action performed and its result; the person’s approval of a displayed proposal is not that observation.

Prepare material that permits a useful contribution. If the initial variants are too trivial or alike for a meaningful human choice, first obtain better starting material through an available generation or variation operation, suitable reusable examples or a short assisted construction. Automatic preparation may use cheap distinctions such as novelty or structural complexity to escape a nearly uniform initial set. These measures guide preparation; they do not establish what the person values. Present a small prepared set and examine whether the person can now identify a consequential difference or make a useful continuation. If not, revise the preparation or use a simpler sufficient construction rather than spending more comparisons on the same uninformative material. When voluntary participation cannot supply this preliminary work, a bounded paid contribution may be an available alternative. Agree what it must produce and examine the result in the receiving use; payment alone does not establish suitability or shared preferences. Count this preparation and human work in the comparison of complete arrangements.

Make continued participation affordable. Ask for a contribution where it can change the continuation: an unresolved serious comparison, a poorly supported case, a disagreement between candidates or a failed attempted correction. Show enough context to make that response interpretable. Small distinguishable batches, recovery of an earlier version and controllable variation can let the person inspect differences without repeatedly starting over. Count the person’s preparation, inspection and correction alongside computation. A fixed adequate rule, a direct question to an expert or an unassisted search can be the better complete arrangement.

When several people continue development, share the actual editable or executable material and the conditions needed to use it. Let a recipient resume from a useful intermediate result and branch for a different question while preserving the original. Different preferences need not be averaged into one objective. Usage counts, repeated playback or clicks can suggest an inquiry, but exposure, defaults and interface behavior can cause them; qualify the preference inference before using them as selection evidence. If a person becomes unavailable or fatigued, stop, use a qualified substitute at its actual scope, or return the missing contribution. A new rater or a learned proxy may change the meaning of the examination.

The useful result is an applied contribution with an examined outcome: changed material, a justified retention of earlier behavior, an improved examination or a supported choice of continuation or stop. The contribution has reached and been examined in its intended operation; a collected comment alone supplies no such result. The method can expose and test human knowledge without transferring the contributor’s whole capability or making every human contribution correct.