Selfforecast

Selfforecast refers to a forecast produced by the subject of the forecast about its own future state or behavior, rather than by external observers. The term is used across disciplines to describe both a methodological approach and the output of that approach. It often implies a degree of self-reference, self-assessment, or self-prediction that can inform planning, decision making, or behavior.

In psychology and behavioral sciences, selfforecast is commonly a self-prediction exercise in which individuals forecast their own actions or outcomes—such as study time, exercise adherence, or goal attainment—with the aim of guiding behavior, increasing commitment, or revealing biases. In organizational contexts, teams or individuals may generate selfforecasts of performance to set targets, monitor progress, and enhance accountability.

In economics, finance, and related fields, selfforecast can refer to forecasts produced by agents or institutions about their own future actions or outcomes, and to comparative assessments against external forecasts. In game theory and agent-based modeling, selfforecasts of future strategies can influence present decisions and strategic interactions.

In data science and machine learning, selfforecast ideas appear in methods that use past forecasts or internal model checks to calibrate predictions, detect drift, or improve self-consistency of forecasting systems. Evaluation uses standard metrics such as forecast accuracy, calibration, and probabilistic scoring.

Critics warn of biases, overconfidence, and strategic misreporting in self-generated forecasts, while proponents argue that explicit selfforecasts can improve accountability, learning, and planning. See also self-fulfilling prophecy, forecast accuracy, calibration, predictive modeling.