Introduction: The Paper Incident That Almost Was

Imagine you’re on call.

It’s 2:13 a.m. You notice a few odd log entries, a barely perceptible uptick in error rates, and a support ticket with wording that doesn’t fit the usual pattern. Nothing screams incident yet. You feel a faint discomfort, a tiny hunch. You scribble a short note in the incident channel: “Seeing something weird; might be nothing.” The thread dies. By morning, a real outage has unfolded around exactly what you noticed.

That throwaway message? That was a paper incident—a story that could have become an early warning, but didn’t. Like a weather vane in still air, it pointed in the right direction, but nobody turned it into action.

This post is about how to treat those tiny on‑call hunches as a “story weather vane”—a weak but meaningful indicator of where the incident wind might blow—and how to combine them with structured methods (Symbolic AI, Qualitative Physics, and soft sensors) to build reliable early‑warning systems.

1. Where Early Warnings Come From: Inside and Out

Early warning signals rarely arrive with a siren and a red banner. They tend to surface as:

Internal early signals

• Slightly elevated error or retry rates
• Slower database queries in a narrow slice of traffic
• Subtle drift in configuration, permissions, or routing
• Recurring but “minor” tickets about the same edge case
• On‑call engineers’ comments like “this feels off”

External early signals

• Reports of new cyber‑attacks or exploits targeting similar tech stacks
• Background fraud or abuse patterns in your industry
• Geopolitical events, terrorism, or conflict affecting your suppliers
• Extreme weather or earthquakes threatening key data centers
• Supply chain disruptions, telecom outages, or regional power instability

High‑risk industries—nuclear, aerospace, automotive, process plants, telecom, and others—have long studied these weak signals. What they’ve learned transfers directly to modern digital operations: the signal is often there before the incident, but it’s faint, scattered, and easy to ignore.

2. Why Tiny Hunches Get Ignored

If the signals are there, why do we so often miss them?

Psychological reasons

• Normalcy bias – “We’ve seen weird blips before; nothing happened.”
• Ambiguity aversion – If it’s not clearly a problem, we’d rather wait.
• Fear of false alarms – Nobody wants to be “the person who always cries wolf.”
• Diffusion of responsibility – “Someone closer to this system will jump in.”

Organizational reasons

• No clear threshold for action – “When is it worth page‑escalating?” is fuzzy.
• Fragmented information – Logs in one tool, metrics in another, hunches in Slack.
• Cultural penalties for being wrong – If early warnings that don’t pan out are criticized, people will stay quiet.
• Metrics obsession – If only high‑severity, high‑confidence issues are rewarded, weak but important signals stay invisible.

To turn tiny hunches into early action, you have to tackle both sides: human psychology and organizational design.

3. From Hunches to Structure: Symbolic AI and Qualitative Physics

Human intuition is great at noticing “something’s off,” but not always at explaining why or what to do. That’s where systematic methods help.

Symbolic AI: Making causal structure explicit

Symbolic AI represents knowledge as symbols and relationships: components, constraints, causes, and effects. Instead of opaque correlations, you encode:

• System topology (which component depends on which)
• Failure modes and their typical symptoms
• Constraints (what must be true for safe or correct operation)

When a weak signal shows up—say a small change in error patterns—Symbolic AI can:

• Map the symptoms to likely failure modes
• Ask logical "what‑if" questions (e.g., If this valve is sticking, what else should we see?)
• Suggest focused checks or temporary safeguards

Qualitative Physics: Reasoning without perfect numbers

In early‑warning situations, you rarely have precise data. You have trends and relative changes: rising, falling, unusually spiky. Qualitative Physics is about reasoning with that kind of information.

Instead of “pressure is 4.2 bar,” you use categories:

• Pressure is low / normal / high / increasing
• Flow is steady / intermittent / reversed

This style of reasoning is powerful in domains like nuclear safety, aerospace, and industrial process control, because incidents often begin as small qualitative shifts that only later cross numerical thresholds.

Combined, Symbolic AI + Qualitative Physics let you:

• Treat vague observations (“slightly higher timeouts from region A”) as structured evidence
• Infer possible underlying causes and likely trajectories
• Decide whether a hunch deserves immediate action, monitoring, or dismissal

4. Soft Sensors: Seeing the Hidden Variables

Many of the most important indicators of trouble are not directly measured:

• True stress on a mechanical component
• “Security posture” of a service in real time
• Fraud probability for a user session
• Chemical concentrations that are expensive or slow to measure

Soft sensors solve this by inferring hidden or hard‑to‑measure variables from other available data. They’re models (statistical, machine‑learning, or hybrid with symbolic rules) that:

1. Take in live data (temperature, vibration, logs, pressure, requests, etc.)
2. Estimate an unmeasured state (corrosion level, attack likelihood, nitrate concentration)
3. Continuously update that estimate as new telemetry arrives

Soft sensors transform scattered, noisy signals into:

• A continuous estimate of risk or state
• An early trend (e.g., “nitrate level is rising faster than usual; likely to exceed safe limits soon”)

These methods are actively used in:

• Nuclear – inferring core states from limited instrumentation
• Mechanical & process industries – estimating wear, fouling, or reaction concentrations
• Aerospace & automotive – virtual sensors for loads, battery health, or component fatigue
• Telecom & electronics – predicting link degradation or component failure

The same pattern applies to digital operations: soft sensors over logs, traces, and metrics can estimate “incident risk” or “intrusion likelihood” long before classic alerts trigger.

5. A Real‑Time Example: Forecasting Nitrate (NO₃⁻) Levels

Consider water treatment or environmental monitoring. Direct measurement of nitrate (NO₃⁻) may be slow, expensive, or intermittent. Waiting for lab results can mean missing the window to intervene.

A soft‑sensor‑based early‑warning setup might:

1. Collect live data – flow rates, pH, temperature, turbidity, conductivity, historical nitrate samples.
2. Train a model – map these easily measured variables to nitrate levels.
3. Run in real time – continuously estimate current nitrate concentration.
4. Forecast forward – project near‑future nitrate levels based on current trends and process dynamics.

Now, a tiny hint—a small deviation in temperature and flow—can translate into a clear warning: “Estimated nitrate will exceed the threshold within 3 hours unless process X is adjusted.”

This is the same pattern you want in your on‑call world: small anomalies in telemetry, interpreted through structured models, become concrete early warnings with actionable time horizons.

6. Blending Human Intuition with Structured Tools

The most effective early‑warning practices don’t replace humans; they amplify them.

Treat on‑call hunches as first‑class data

• Log every “might be nothing, but…” observation in a structured way.
• Ask: What was seen? Where? Under what conditions? Not just who said it.
• Use these hunch logs in post‑incident reviews to see which were predictive.

Add Symbolic AI and Qualitative models on top

• Encode key components, dependencies, and known failure modes.
• Define qualitative variables (low/normal/high, rising/falling) for critical metrics.
• Let the system propose plausible explanations and additional checks when weak signals appear.

Deploy soft sensors for continuous risk estimation

• Identify critical hidden states (security risk, capacity margin, hardware stress, chemical levels).
• Build soft sensors to estimate these from your existing telemetry.
• Track not just snapshots, but trends and forecasted crossings of safe limits.

Close the loop with culture and process

• Reward early calls even when they turn out to be false positives.
• Define graduated responses: observe, investigate quietly, raise internal heads‑up, then full alert.
• Use incident reviews to improve symbolic models and soft sensors—not to blame the person who had the hunch.

Over time, your organization learns which weak signals matter, your models become sharper, and your weather vane stops spinning aimlessly and starts pointing reliably.

Conclusion: Building Your Own Story Weather Vane

Every organization already has early warnings. They live in:

• Offhand comments in on‑call channels
• Small anomalies in telemetry
• External events that “probably won’t affect us”

The challenge is not inventing new signals; it’s hearing and interpreting the ones you already have.

By:

• Tackling the psychological and organizational reasons tiny hunches get ignored
• Using Symbolic AI and Qualitative Physics to reason about ambiguous signals
• Deploying soft sensors and real‑time prediction over your telemetry

…you can turn scattered hints into a coherent early‑warning system.

Think of every “paper incident story” as a test of your weather vane. Did it catch the wind early enough for you to act? If not, what structure—models, sensors, or cultural norms—was missing?

The next time you feel that faint late‑night discomfort, imagine it as the first flick of the vane in a changing wind. With the right tools and habits, that tiny hunch can become the difference between a near‑miss and a headline‑making outage.