Data exploration with reflective adaptive models

Adaptive models of systems seek to emulate the processes giving rise to the data observed in the system. The process is often termed learning from examples, or data-driven information processing. An important issue regarding such modeling is the active selection of data by the modeling process, or exploration. If exploration depends on the current state of the model it is termed reflective. In this paper we consider the issue of exploration in theory, and in practice in the form of a simple example, which enables us to identify general properties of the exploration types, and to comment about when exploration would be profitable.