Category: Science

Summary: Evaluating whether adaptive sampling improves early warning in a bistable system driven by red noise at short observational delays but becomes worse at longer delays.

Bistable systems can tip between alternative states, and noisy observations make it hard to detect the approach to a transition in time. This experiment asks whether adaptive sampling helps more than fixed-rate monitoring when delays are short, and whether that advantage reverses once observation delays become large.

The code simulates repeated bistable trials under colored noise, then compares adaptive and fixed sampling using lead time, miss rate, and false-positive rate. By organizing outputs around delay-dependent differences, it looks for a regime inversion rather than a one-sided win for any single strategy.

That is useful for monitoring design in noisy environments. Real sensors often face both correlated disturbances and reporting lag, so the important question is when adaptivity improves warning and when it simply amplifies stale information.

Method: Repeated bistable-system simulations with red-noise forcing and delayed observations, comparing adaptive and fixed sampling policies across batches of conditions.

What is measured: Lead-time difference, miss-rate difference, false-positive difference, short-delay adaptive advantage, long-delay adaptive penalty, and delay-driven regime inversion.