Final answer:
In decision theory, persons who are incorrectly predicted to fail but then succeed are termed as false negatives. This concept is critical for evaluating the accuracy of predictive models and making effective decisions. It's important to understand and reduce these errors in decision-making contexts.
Step-by-step explanation:
In decision theory, persons who are predicted to fail but who succeed are known as false negatives. This term refers to the outcome where a predictive test incorrectly indicates the absence of a condition or trait (failure) when it is actually present (success). For instance, if a student is predicted not to pass an exam based on certain criteria but ends up passing, that student is considered a false negative relative to the prediction.
Content loaded in decision theory is essential to understand the different outcomes that result from predictive models or tests. False negatives and their counterparts, false positives, true positives, and true negatives, are critical concepts that help to analyze the accuracy of these models. It is important to minimize the occurrence of false negatives and false positives for more effective decision-making processes.