Extended Abstract

Large-scale systems—technical, social, biological—are governed not only by the dynamics of their components but by the alignment of uncertainties across those components. In site reliability engineering (SRE), operators know that failures rarely emerge from one metric alone; they occur when many signals become unstable together. In philosophy, traditions of karma describe interdependence: local actions ripple outward to affect the whole. In AI safety, Geoffrey Hinton has suggested that advanced systems will need a maternal instinct—an intrinsic bias toward protection and stability.

Despite arising in different contexts, these observations converge on a common mathematical substrate: entropy and its correlations.

1. Ted’s Law of Karma

Ted’s Law of Karma states:

The covariance structure of entropy streams reveals the shared fate of interdependent systems.

Formally, each observable metric (x_i(t)) produces an entropy stream (H_i(t)). At time (t), the covariance matrix (\Sigma_H(t)) of these entropy values can be computed. The eigenvalues (\lambda_j(t)) of (\Sigma_H(t)) then characterize the degree of alignment of uncertainty across subsystems. A spike in the dominant eigenvalue (\lambda_1(t)) corresponds to the emergence of a systemic mode of risk.

2. Method (Operationalization)

  1. Signal collection: Metrics are gathered from heterogeneous sources (databases, telemetry, logs, alerts, communication).
  2. Entropy measurement: For each stream, entropy is computed over rolling windows (Shannon or spectral).
  3. Covariance analysis: Entropy streams are assembled into (\Sigma_H(t)); eigen-decomposition yields systemic modes.
  4. Detection & biasing: When (\lambda_1) exceeds baseline thresholds, the system emits “alert-coming” signals or biases behavior toward protective actions.

This framework has been prototyped in infrastructure monitoring (MongoDB change streams, Dynatrace metrics, Splunk alerts, email notifications). Results show that eigenvalue spikes anticipate incidents minutes to hours in advance.

3. Implications

  • For SRE: Covariance of entropies provides a principled early-warning system, outperforming threshold-only alerts by detecting alignment of risk rather than isolated anomalies.
  • For Complex Systems: The approach suggests a general mechanism for emergent cascades: systemic failures are preceded by eigenmodes of entropy alignment.
  • For AI Safety: Maternal instinct can be operationalized as sensitivity to entropy covariance. Instead of learning only outcome-based rewards, an agent can be trained to dampen actions when systemic uncertainty aligns, embedding a protective bias.

4. Future Work

  1. Formalization: Extend Ted’s Law into an information-theoretic theorem within the field of statistical physics or information geometry.
  2. Cross-domain validation: Apply the method to ecosystems, economies, and social networks to test generality.
  3. AI safety integration: Implement entropy-covariance sensitivity as a protective reflex in reinforcement learning agents.
  4. Governance: Explore how human interventions (manual SRE actions) can be logged as training labels for protective behavior, closing the loop toward autopilot systems.

5. Conclusion

Ted’s Law of Karma provides a compact, general statement: shared fate is visible in the covariance of entropies. Operationalized in infrastructure today, it offers a potential pathway to implementing maternal instinct in autonomous systems tomorrow. This bridge between information theory and protective bias could form the basis for safer, more self-governing AI.

📄 Download the full preprint: /papers/ted-law-karma.pdf

Citation:
Strall, T. (2025). Ted’s Law of Karma: Covariance of Entropies and Maternal Instinct.
Zenodo. https://doi.org/10.5281/zenodo.17015425

Preprint links:

Keywords: entropy, covariance, eigenmodes, karma, maternal instinct, AI safety, systemic risk