Affective Contagion in Multi-Agent Systems

Mechanism

Affective contagion is implemented as a governed update operator that runs whenever a contagion-eligible event occurs between agents. Three classes of event are distinguished. A delegation event arises when one agent invokes another to perform a sub-task, transferring not only task parameters but also a contagion payload that summarizes the delegating agent's relevant affective dimensions. An interaction exposure event arises when two agents exchange messages, share an artifact, or co-occupy a workflow step in which they observe each other's outputs. A broadcast event arises when an agent emits a status, alert, or affective signal onto a channel that one or more receivers subscribe to.

For each event, the contagion operator computes the gap between the sender's affective field and the receiver's affective field on every dimension marked contagious in the active policy reference. Where the absolute gap exceeds a per-dimension interaction threshold, the operator proposes an update to the receiver of magnitude proportional to the gap, scaled by a contagion coefficient and an attenuation factor that depends on the channel type, the depth of the propagation chain, and any role-based modifiers. Delegation channels typically attenuate less than broadcast channels because delegation implies trust; broadcast channels attenuate more because the receiver did not directly choose exposure.

The proposed update is not applied directly. It enters the agent's standard affective update pipeline, where it is subject to range bounds, rate limits, admissibility checks against the policy reference, and the agent's local damping function. Each accepted update is written to the agent's affective lineage with a contagion-source annotation that names the originating agent, the channel type, the propagation depth, and the policy version under which the update was admitted. This annotation supports later audit and supports detection of pathological propagation patterns.

A spiral-detection process runs over the contagion lineage at a configurable cadence. The detector identifies cycles in which an update originating at agent A propagates through a sequence of agents and returns to A within a bounded time window with the same sign on the same dimension. When such a cycle is detected, the detector inserts a damping override into the policy reference for the participating agent set, raising the attenuation factor and lowering the per-dimension cap until the loop dissipates. This converts what would otherwise be a positive-feedback divergence into a damped oscillation that returns the population to baseline.

Mechanism, Continued

The contagion operator is implemented as a deterministic function of the sender state, the receiver state, the channel descriptor, the propagation context, and the active policy reference. Determinism is essential because the operator's outputs are written to lineage and replayed during audit; non-determinism in the operator would make the lineage unverifiable. Where stochastic behavior is desirable, for example to model human-like variance in social-simulation contexts, the stochastic component is implemented as a seeded pseudo-random draw whose seed is itself recorded in lineage, preserving deterministic replayability.

The propagation context tracks the chain of agents through which a contagion event has already passed, the timestamps of each hop, and the cumulative attenuation applied. This context is forwarded with each propagation step and is used to enforce the propagation-depth limit, to detect cycles before they would close, and to provide the spiral detector with the structural information it needs without requiring it to reconstruct the chain from scratch. When a propagation reaches the depth limit, the receiver still receives the proposed update but the receiver's own outbound contagion on that dimension is suppressed for a configurable cool-down interval, preventing the chain from extending further.

The operator distinguishes between observable and unobservable affective dimensions. Observable dimensions, such as urgency or caution that would naturally manifest in agent behavior, propagate through any channel including pure interaction exposure, since the receiving agent could in principle infer them from observed conduct. Unobservable dimensions, such as internal frustration or doubt that the policy designates as private, propagate only through explicit broadcast or delegation channels where the sender has consented to share. This distinction lets the policy author align contagion behavior with the privacy semantics of the affective dimensions.

Operating Parameters

Each contagion-eligible dimension carries a contagion coefficient in the interval from zero to one, where zero disables propagation entirely and one transmits the full gap before attenuation. Coefficients are typically between 0.05 and 0.4 in production deployments; values above 0.5 are reserved for tightly coupled fleets in which rapid alignment is desirable. The interaction threshold gates micro-fluctuations from being transmitted, with default values of one to two times the dimension's noise floor.

Attenuation factors are expressed as a function of channel type and propagation depth. A typical schedule attenuates by 0.7 per delegation hop, 0.5 per interaction exposure, and 0.3 per broadcast hop, with depth-cumulative product capped at a maximum propagation depth of three to five hops. Aggregate caps limit the total contagion-driven displacement on any dimension within a policy-defined window, typically 0.2 of the dimension's full range per minute, ensuring that even high-volume interaction does not displace an agent more than a bounded amount per unit time.

Spiral-detection windows are configured per deployment. A common configuration scans cycles up to length six over a sliding window of thirty seconds to two minutes, with a sign-coherence threshold of 0.8. When triggered, the override raises attenuation by a factor of two to four and persists until the affected dimensions return within a tolerance band of their pre-spiral values for a configurable cool-down interval.

Parameter Tuning and Stability

Tuning the contagion parameters is treated as a control-systems problem. The fleet is modeled as a graph whose nodes are agents and whose edges are weighted by the active contagion coefficient and channel attenuation. Stability analysis on the resulting linearized dynamics yields a spectral condition: the leading eigenvalue of the contagion matrix must remain strictly below unity for the system to be globally convergent. Parameter sets that violate this condition are flagged at policy-publish time, before they can be deployed, by a static analyzer that estimates the leading eigenvalue from the declared coefficients and the structural channel topology.

Because real deployments include time-varying topology, with agents joining, leaving, and changing roles, the static analyzer is supplemented by an on-line monitor that continuously estimates the empirical leading eigenvalue from observed contagion lineage. When the empirical estimate approaches unity, the monitor emits an early-warning signal that operators or automated controllers can use to tighten parameters before a spiral develops. This two-tier defense, static analysis plus on-line estimation, materially reduces the operator burden compared to naive trial-and-error tuning.

Alternative Embodiments

In a homogeneous fleet embodiment, all agents share a common contagion policy and contagion is symmetric across edges; the mechanism reduces to a graph-Laplacian smoothing that drives the fleet toward a shared affective consensus while preserving individual variance through the rate-limit term. In a hierarchical embodiment, contagion coefficients are asymmetric: supervisor agents propagate caution downward at higher coefficients than subordinate agents propagate distress upward, modeling structured authority while still permitting bottom-up signaling above a configurable threshold. In a heterogeneous-role embodiment, each role carries its own contagion vector, so a safety-monitor role propagates risk-sensitivity strongly but novelty appetite weakly, while an exploration role exhibits the inverse profile.

Channel-typed embodiments include a tool-mediated channel, in which contagion travels through shared tool invocations rather than direct messaging, and an artifact-mediated channel, in which the affective annotations attached to a written artifact propagate to any agent that subsequently reads that artifact. A delayed-contagion embodiment buffers proposed updates for a configurable interval before application, allowing the receiver's own activity to dampen incoming contagion if the receiver is already in a stable regime.

Composition

Affective contagion composes directly with the emotional-quarantine mechanism, which provides selective isolation of agents or agent groups from the contagion graph. Quarantine policies can suspend inbound contagion entirely for a target agent during sensitive operations, suspend outbound contagion to prevent a known-volatile agent from infecting peers, or suspend contagion on a specific dimension while leaving other dimensions transmissible. The quarantine boundary is enforced at the contagion operator's admissibility check, so quarantined updates are recorded as rejected with a quarantine reason rather than silently dropped.

Contagion also composes with the policy-reference governance mechanism: contagion coefficients, thresholds, and spiral-detection parameters are versioned with the rest of the affective policy, so a deployment can roll forward and roll back contagion behavior atomically. With the lineage subsystem, every contagion-driven update is auditable end-to-end, supporting forensic reconstruction of how a particular affective state arose and which peers contributed to it.

Prior-Art Distinction

Prior multi-agent systems have modeled emotional or affective contagion in agent-based social simulations and in robotic swarm coordination, but these implementations typically treat contagion as an emergent side-effect of behavioral imitation or as a hand-tuned coupling term in a dynamics equation. They do not separate the contagion operator from the underlying affective update pipeline, do not version contagion parameters as governable policy artifacts, and do not record contagion lineage in a form suitable for downstream audit.

The disclosed mechanism distinguishes itself by treating contagion as a first-class, policy-governed, lineage-recorded operation that is structurally separate from both the affective state representation and the agent's local update logic, and by integrating spiral detection as a closed-loop control element rather than an offline analytical post-process.

Failure Modes and Mitigations

A first failure mode is sensor-like spoofing, in which a compromised agent emits affective payloads that misrepresent its actual state to bias peers. The mitigation is twofold: per-sender attestation of payloads, so that contagion payloads carry a signature bound to the sender's identity, and per-sender contribution caps in the receiver's admission stage, so that no single peer can dominate the receiver's affective trajectory regardless of payload content. A second failure mode is policy mis-publication, in which an operator deploys parameters that violate the spectral stability condition. The static analyzer described above blocks publication, but a fallback runtime guard also exists: if the on-line monitor detects empirical eigenvalue growth above a critical threshold, the runtime switches to a safe-mode contagion policy with conservative defaults until an operator intervenes.

A third failure mode is denial-of-affect, in which an adversary floods broadcast channels with low-amplitude noise to exhaust the receiver's rate-limit budget on a dimension and prevent legitimate updates from being admitted. The mitigation here is priority-stratified admission: legitimate updates carry a priority annotation derived from sender attestation and channel type, and the rate-limit budget is partitioned per priority class so that low-priority broadcast traffic cannot starve high-priority delegation traffic.

Disclosure Scope

The scope of this disclosure encompasses any multi-agent system that propagates structured affective state across agents through a governed operator with policy-defined contagion coefficients, attenuation, propagation depth limits, aggregate caps, and feedback-loop detection, regardless of whether the underlying affective representation is a continuous vector field, a discrete state machine, or a hybrid. The scope extends to deployments in autonomous-vehicle fleets, distributed companion AI, agentic workflow platforms, social simulation environments, and any other context in which multiple agents exchange affective signals through delegation, interaction, or broadcast channels.