When Physicists Say “Prediction”: 2 Retrodiction and Curve-Fitting

The first mutation of prediction is subtle and often overlooked: retrodiction. Here, a theory is used to account for data already observed, fitting curves and adjusting parameters to reproduce known outcomes. The practice is legitimate and widespread — but it quietly shifts the meaning of prediction.

In classical terms, prediction implies forward-looking anticipation. Retrodiction looks backward, yet when presented in modern physics, it is often recast rhetorically as predictive success. A theory that fits past data with remarkable accuracy is treated as evidence of its predictive power, even though no temporal commitment is involved.

Curve-fitting and parameter tuning exemplify this shift. The numerical agreement between theory and known measurements is celebrated. Success is measured by the degree of fit, by the coherence of the model across data points. What is invisible in this celebration is that the theory has done no anticipatory work; it has not engaged the world beyond the dataset it has already absorbed.

Yet the rhetorical power is strong. Retrodiction provides a veneer of prediction while bypassing the risks and constraints of genuine temporal engagement. The theory appears robust, flexible, and empirically grounded — even when the anticipatory element is absent. In effect, retrodiction functions as a surrogate predictive badge, maintaining authority without fulfilling classical expectations.

This structural mutation matters because it sets the stage for further transformations. If prediction can be recast after the fact, it can be stretched, bent, and eventually decoupled from temporal or experiential accountability entirely. The bridge from theory to phenomenon begins to erode, but without the appearance of failure.

Understanding retrodiction as a first step clarifies how modern scientific discourse sustains legitimacy. It shows that predictive claims are not automatically synonymous with engagement with the world — and it prefigures the more internalised and abstracted forms of prediction we will encounter in later parts of this series.