"If you have to forecast, forecast often"

Fiedler’s aphorism is a call to humility and iteration under uncertainty. The world changes, information arrives unevenly, and models are always partial. A single, static prediction calcifies into dogma; repeated forecasts turn prediction into a learning loop. By revisiting estimates as new evidence appears, forecasters avoid staleness, reduce error, and keep confidence intervals honest. The act of forecasting often institutionalizes Bayesian updating: start with a prior, absorb fresh signals, revise, and communicate both the new point estimate and the uncertainty around it.

Frequent forecasts also improve decisions. Resource allocation, risk hedging, and policy choices benefit from current beliefs, not month‑old assumptions. Regular updates shorten feedback cycles, making it easier to course‑correct before small deviations become crises. They create accountability too: more forecasts mean more opportunities to score accuracy, diagnose bias, and recalibrate. This cadence combats anchoring and hindsight bias, trading bravado for calibration. In practice, weather services, supply-chain teams, investors, and public-health agencies live by this rhythm, hurricane tracks, demand curves, earnings guidance, and epidemic projections all strengthen when refreshed and empirically scored.

There is a caution: forecasting frequently does not mean chasing noise. Revision should follow clear rules, thresholds for material change, consistent methods, and attention to signal-to-noise. Communicating ranges and scenarios, not just single numbers, further guards against overreaction. A simple toolkit helps: keep a forecast log, quantify probabilities, track Brier or absolute errors, set regular update intervals, and automate data ingestion where possible. Viewed this way, forecasting often is less about producing more numbers and more about cultivating an adaptive system. It acknowledges uncertainty without surrendering to it, turning prediction from a one‑off performance into continuous learning that improves accuracy, trust, and decision quality over time.