Policy-Bounded Affective Updates

Mechanism

Every affective update originates from a triggering event, passes through a proposing module, and arrives at the affective state object as a proposed delta on one or more dimensions. The bounded update mechanism interposes a validation pipeline between the proposal and the application. The pipeline references a policy field carried by the agent's configuration object, and the policy itself is a signed artifact whose authority chain terminates at a root governance key.

The validation pipeline performs five checks. The range check compares the proposed post-update value of each affected dimension against the policy's per-dimension floor and ceiling and clamps any value that exceeds the bound. The rate check compares the proposed change magnitude against the maximum allowed change per unit time for that dimension and either throttles or rejects the update if the rate is exceeded. The trigger-admissibility check verifies that the triggering event class is in the admissible set for each dimension being updated; an event class outside the admissible set causes the update to be rejected for that dimension regardless of magnitude. The authority check verifies that the proposing module is on the authority list for the affected dimensions, with the authority list bound to module identities and not to module names. Finally, the decay-consistency check verifies that the proposed update is consistent with the dimension's decay schedule, rejecting updates that would re-elevate a dimension that policy specifies as decaying.

Updates that pass all five checks are applied atomically. The applied update is recorded in the agent's affective lineage along with the triggering event identifier, the proposing module identifier, the policy version under which the update was validated, and the pre- and post-update values of each affected dimension. Updates that fail any check are also recorded, with the failure reason and the rejected proposal, so that the lineage captures both successful and rejected attempts.

Operating Parameters

Per-dimension range bounds are typically set as fractions of the dimension's normative range, with safety-critical dimensions such as urgency or aggression bounded more tightly than exploratory dimensions such as curiosity. Rate limits are expressed as maximum delta per second or maximum delta per number of intervening events, whichever is more restrictive in a given deployment. The trigger admissibility set is encoded as a mapping from event class to dimension subset, allowing fine-grained control over which classes of stimulus can move which parts of the affective field.

Authority lists are encoded as cryptographic identities rather than role labels so that an attacker who compromises a non-affective module cannot impersonate an affective updater. Decay schedules are specified per dimension as a target value and a half-life, with the validation pipeline checking that proposed updates do not contradict the schedule's current expected value beyond a tolerance band. The tolerance band is itself a policy parameter that can be tightened in production deployments and relaxed in development environments.

Policy versioning is a critical parameter. Each policy artifact carries a version identifier and a signature, and the validation pipeline records both. Lineage replay against a different policy version produces a different decision trace, which is the mechanism by which policy changes are themselves audited: a deployment can replay historical events under a proposed new policy and observe how many additional updates would have been rejected or admitted.

Alternative Embodiments

In a soft-bounding embodiment, range violations produce clamping rather than rejection, on the rationale that the proposed update reflects real signal that should not be discarded. In a hard-bounding embodiment, range violations produce rejection, on the rationale that an out-of-bound proposal indicates either a malformed proposer or an adversarial event. Mixed embodiments apply soft bounds to interior dimensions and hard bounds to safety-critical dimensions.

A two-stage embodiment splits validation between a fast inline check and a slower out-of-band check. The inline check enforces range and rate; the out-of-band check enforces trigger admissibility and authority through cryptographic verification of the proposing module's identity. Updates that pass the inline check are applied provisionally and the agent operates on the provisional state; if the out-of-band check fails, the update is rolled back and downstream consumers receive a compensating notification through the lineage.

A capability-token embodiment replaces the authority list with bearer capability tokens. Each proposing module presents a token signed by the policy authority that explicitly enumerates the dimensions and event classes the module is permitted to affect. The validation pipeline verifies the token signature and checks that the proposed update is within the token's authorization. This embodiment is preferred in deployments with many heterogeneous proposing modules because it decouples authority distribution from policy publication.

A multi-signature embodiment requires that high-magnitude updates carry endorsements from multiple proposing modules before they can be applied. This is useful where a single triggering event should not be sufficient to drive the affective field to extreme values, even if the event is genuine.

Composition With Cryptographic Governance

The bounded update mechanism is meaningless without an authenticated policy. The cryptographic governance subsystem provides the policy distribution channel: policies are published as signed artifacts, distributed through the governance log, and verified against the agent's anchored root key before being loaded into the validation pipeline. An attacker who can write to the agent's local storage cannot substitute a permissive policy because the substitute would not verify against the root.

The lineage records emitted by the bounded update mechanism are themselves committed to the governance log, producing a tamper-evident audit trail of every affective state change. A consumer of the agent's outputs can independently verify the trail by replaying it against the published policies, which is the property that supports formal safety analysis and regulatory compliance: the safety claim is not that the operator trusts the agent but that the operator can verify the agent's affective behavior against a public, signed contract.

Composition with the affect-modulated traversal subsystem closes the loop. Because the affective field that enters traversal is always the output of bounded updates, the traversal lineage that records the affective snapshot at the time of selection transitively records authenticated provenance. A forecast that depends on a traversal that depends on an affective state can be traced through to the signed events and policies that produced it.

Threat Model And Failure Modes

The bounded update mechanism is designed against a specific threat model. The primary adversary is an external actor capable of injecting events into the agent's input stream, including events crafted to trigger extreme affective updates. The range and rate checks defeat single-event amplitude attacks and short-burst manipulation sequences. The trigger-admissibility check defeats type-confusion attacks in which an event of one class is wrapped to appear as an event of another class. The authority check defeats lateral-movement attacks in which a compromised non-affective module attempts to issue affective updates.

A secondary adversary is an insider with the ability to modify the agent's local configuration. The cryptographic anchoring of the policy defeats local-substitution attacks: a permissive policy installed locally will not verify against the root key and will be rejected during policy load. A tertiary adversary is a software-supply-chain attacker that compromises the proposing module itself; this case is addressed by the multi-signature and capability-token embodiments, which constrain the authority of any single compromised proposer.

Failure modes that the mechanism does not address are explicitly noted. The mechanism does not defend against an adversary that can mint signatures from the root governance key, because such an adversary can publish arbitrary policies. It does not defend against semantic adversarial events that are within range, within rate, within admissible triggers, and within authority but nevertheless exploit the agent's downstream behavior; defense against semantic attacks is the province of the discovery and forecasting subsystems and is out of scope here. It does not address denial-of-service attacks that exhaust the validation pipeline; denial-of-service is handled at a lower layer.

Distinction From Prior Art

Generic input validation, schema enforcement, and rate limiting are well-established in software systems but operate on flat data without the semantic structure of affective dimensions and without policy-anchored authority. Computational models of emotion in agent architectures typically expose unbounded update interfaces, on the assumption that the proposing modules are trusted by construction. Cryptographic policy frameworks exist for access control and configuration management but have not been applied to the modulation of agent internal state in the manner described.

The distinguishing combination is the binding of a structured affective control surface to a signed policy with five orthogonal constraint axes, applied atomically, recorded in tamper-evident lineage, and composed with downstream subsystems that consume the lineage as evidence.

Scope Of This Disclosure

This article discloses the mechanism, operating parameters, alternative embodiments, and composition properties of policy-bounded affective updates as one component of the broader cognition system. Variations in constraint axes, validation order, embodiment form, and policy distribution mechanism are within scope provided that every state change is bound to a signed policy reference, every change is recorded in lineage, and the resulting affective trajectory is independently verifiable against the published policy.