Fake Constants and Fake Variables: Adding Dynamics to POTS
When you analyze a system, you make assumptions. Some things you treat as constants – fixed, unchanging. Others you treat as variables – free to move. But many assumptions are wrong. What looks constant is actually changing slowly (a fake constant). What looks variable is actually trapped by hidden constraints (a fake variable).
The POTS framework (People, Objects, Time, Space) helps spot these fakes – but only if you add dynamics. You have to ask: how does each parameter really behave over time, across spaces, through different people, and among objects?
Fake Constants – Hidden Drift
A fake constant is something you assume never changes, but it does. The drift is often slow, so you miss it – until it breaks the system.
Floating LiDAR example – battery capacity: Many models treat "battery capacity" as constant. In reality, it drops 5-10% per year (Time). After two years, the "low battery" warning gives 10 days instead of 30. The crew (People) arrives too late. The constant was fake.
Another – LiDAR laser transmission: Design assumes constant laser output. But salt spray (Space: marine) deposits on the window over 3 months (Time). The laser diode degrades with thermal cycles. Actual transmission drops 15%. The onboard algorithm (Object) interprets lower backscatter as clean air – so it reports wind speeds maybe 0.3 m/s low. The analyst (People) trusts the number. The constant was fake.
Fake Variables – Hidden Constraints
A fake variable is something you treat as freely adjustable, but real-world constraints lock it down. The constraints often emerge from POTS dynamics.
Floating LiDAR example – mooring line scope (chain length to water depth ratio): The designer thinks scope is variable – pick any value between 3:1 and 6:1. But the specific site (Space) has a 5m tidal range (Time: twice daily). At low tide, a 4:1 scope becomes 2.5:1 – the anchor lifts, buoy drifts. The seabed (Space) is soft mud – requires at least 4:1 to hold. The vessel (Object) has a winch limited to 500m of chain. The regulator (People) requires a 1.5× safety factor on holding capacity. After all constraints, only one scope works: 4.7:1. The variable was fake. Fix: treat scope as a derived parameter, not a design choice. Use dynamic mooring analysis that includes tide and seabed.
Quick Method: Spotting Fakes
| If it looks constant but... | It's a fake constant → Model as f (Time, Space, People, Objects) |
| If it looks variable but... | It's a fake variable → Map constraints from POTS dynamics |
For any parameter, ask two questions:
"Is this really constant across all POTS dimensions?" If no – model its drift.
"Is this really free across all POTS dimensions?" If no – find the dynamic constraints.
Putting Dynamics into POTS
A static POTS analysis lists people, objects, times, spaces. A dynamic analysis adds:
For each People: how do their needs change over Time? (Technician in summer vs. winter.)
For each Object: what degradation curves (Time) or environmental responses (Space) create fake constants?
For each Time: what new constraints emerge that turn variables into fake ones?
For each Space: what local fixed conditions act as hidden constants?
Why This Matters
Fake constants hide risk. Your LiDAR reports false wind speeds. Your battery dies early. Fake variables waste money – you design for flexibility you never have.
Build a POTS dynamic model. Use continuous self-diagnostics (laser output monitors, battery impedance tracking) and periodic calibration against a reference. Update the model with field data. That's how you turn fake constants into managed variables, and fake variables into design boundaries.
Stop pretending. Model the drift. Respect the constraints. Survive real life – not just in the PowerPoint.