An Initial Implementation of the Turing Tournament to Learning in Repeated Two

We report on a design of a Turing tournament and its initial implementation to learning in repeated 2 person games. The principal objectives of the tournament, named after the original Turing Test, are (1) to find learning algorithms (emulators) that most closely simulate human behavior, (2) to find algorithms (detectors) that most accurately distinguish between humans and machines, and (3) eventually, to develop general methodology for evaluating models of human behavior in social sciences based on this idea. In order to test our concept, we developed the software and implemented a number of learning models well known in the literature and developed a few detectors. This initial implementation found significant differences in data generated by these learning models and humans, with the greatest ones in coordination games. These differences indicate that there is a lot of room for improvement of the existing learning models, as well as for development of good detectors that can successfully distinguish between human and machine generated behavior. JEL classification: D83, C63, C92, H41