Game theoretic mechanism design applied to machine learning classification

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. Numerous approaches have been developed ranging from neural network models striving to replicate neurophysiology to more abstract mathematical manipulations which identify numerical similarities. Nevertheless a common theme amongst the varied approaches is that learning techniques incorporate a strategic component to try and yield the best possible decision or classification. The mathematics of game theory formally analyzes strategic interactions between competing players and is consequently quite appropriate to apply to the field of machine learning with potential descriptive as well as functional insights. Furthermore, game theoretic mechanism design seeks to develop a framework to achieve a desired outcome, and as such is applicable for defining a paradigm capable of performing classification. In this work we present a game theoretic chip-fire classifier which as an iterated game is able to perform pattern classification.