The Role of Entropy in Karma

When we say “entropy” in Karma, we mean it in the information theory sense:

The amount of uncertainty — or surprise — in a system’s state.

For Karma, the system is everything we’re tracking in the normalized event stream: jobs, steps, suppliers, file arrivals, API responses. Each has patterns — some highly regular, others more chaotic.

Why Entropy Matters

Entropy tells you how predictable a process is:

  • Low entropy: A supplier always responds within 90 seconds, a daily job always starts on time.
  • High entropy: Job start times drift, responses vary wildly, steps go missing.

Karma’s goal:

  • Measure predictability.
  • Model expected behavior.
  • Act when entropy spikes in ways that matter.

Measuring Surprise

From an event stream in ClickHouse:

  1. Group by context (e.g., entity_id, step).
  2. Build a distribution of event timings or transitions.
  3. Apply Shannon’s entropy formula:
H(X) = - Σ p(x) log₂ p(x)

Where:

  • X = possible outcomes (latency buckets, next step choices, etc.).
  • p(x) = probability of each outcome from historical data.

A high H(X) means outcomes are spread out (unpredictable); low H(X) means outcomes are concentrated (predictable).

From Measurement to Action

Once we know the baseline entropy for each (entity, step):

  • Monitor for real-time deviations:
    • Latency entropy higher than normal → process slowing unpredictably.
    • Step transition entropy spikes → workflows becoming inconsistent.
  • Flag or act:
    • Publish entropy.deviation events.
    • Suggest corrective actions (notify_supplier, retry_step).
    • Feed into automation pipelines.

Entropy in the Karma Loop

Karma’s architecture already:

  • Normalizes events.
  • Stores them in ClickHouse for high-cardinality queries.
  • Feeds optional GraphDB for sequence/state relationships.
  • Routes actions as first-class events.

The entropy layer fits between analysis and action:

[ Events in ClickHouse ]
        │
   Entropy Calculation
        │
[ entropy.predicted / entropy.deviation ]
        │
    Actions Pipeline

Why This is Different

Most monitoring systems:

  • Alert on thresholds (CPU > 80%).
  • Require static rules.

Karma’s entropy approach:

  • Learns what “normal” looks like per entity/step.
  • Detects change even if the absolute numbers are “in range.”
  • Prioritizes by information content — the most surprising changes surface first.

What’s Next

  • MVP: Median/MAD-based expectations for latency + simple entropy measures.
  • Upgrade path: Model sequences and timings jointly for richer entropy signals.
  • Final goal: An autonomous layer that constantly minimizes system entropy — not by keeping everything rigid, but by keeping it predictably reliable.