Overmodeling

Overmodeling is the practice of building models that are more complex or detailed than is warranted for the intended task. It can occur in statistics, machine learning, control systems, and business process modeling. An overmodeled system may fit historical data very well but generalizes poorly to new data or unseen conditions.

Common drivers include a desire to capture every observed variation, the availability of large feature sets, data dredging, misalignment between model scope and decision needs, and incentives that reward apparent accuracy over robustness. Overmodeling can also arise when model development proceeds without explicit constraints on complexity or without rigorous validation across diverse scenarios.

Consequences include overfitting, increased data and computation requirements, reduced interpretability, longer development and maintenance cycles, and brittleness when conditions change. The resulting model may be accurate on past data but unreliable in deployment, and it can obscure causal insight with spurious patterns.

Indicators of overmodeling include a large number of predictors with marginal effects, high variance in estimates, degraded performance on out-of-sample tests, and a mismatch between model complexity and the available data. Complexity measures like information criteria can help, but they must be interpreted in the modeling context.

Mitigation strategies emphasize parsimony: restrict features to those with a plausible causal or predictive role, use regularization, hold-out validation, and compare against simpler baselines. Incorporating domain knowledge, simplifying the model scope, and predefining evaluation criteria help ensure the model remains fit for purpose. Regular reviews during deployment can catch overmodeling as conditions evolve.