Reflexive prediction

Reflexive prediction occurs when a model's predictions alter the behavior of the agents it predicts, thereby changing the data-generating process and potentially invalidating the model's own assumptions. The phenomenon is a special case of feedback topology in which the output of a system becomes an input to itself through the mediating behavior of strategically aware agents. The classic example is a credit-scoring model: if borrowers learn that the model penalizes certain behaviors, they will alter those behaviors, and the correlations the model originally discovered will decay. Reflexive prediction is not merely a technical problem of model drift; it is a governance problem about what happens when algorithmic systems are deployed in environments where the governed can react to the governor. The concept connects to the broader problem of performative modeling — systems that do not merely describe the world but actively reshape it.