errordriven

Errordriven is an adjective describing approaches, processes, or systems that are guided predominantly by error signals or discrepancies between expected and actual outcomes. In this sense, errors serve as the primary source of information that drives adaptation, updating, or decision making.

In machine learning and artificial intelligence, error-driven learning refers to methods where a quantitative error measure guides updates to models. Supervised learning uses labeled data to reduce prediction error; neural networks adjust weights to minimize loss via backpropagation. In reinforcement learning, prediction errors or temporal-difference errors act as signals that shape policies and value estimates. Some learning algorithms are explicitly described as error-driven because their updates depend directly on observed errors rather than purely on exploratory signals.

In cognitive psychology and education, error-driven learning posits that people learn by detecting and correcting errors. Discrepancies between expected outcomes and real feedback trigger hypothesis revision and strategy change. The salience and frequency of errors can influence learning rate, attention, and cognitive control.

In software engineering or process management, errordriven development is less standardized but may describe practices that prioritize rapid localization, reporting, and remediation of defects. Teams may adopt error-driven monitoring and debugging workflows, using error data to guide feature work or architectural improvements. However, the term is not widely established as a formal methodology and may be used inconsistently.

See also: error-driven learning; supervised learning; reinforcement learning; error analysis; debugging.