Amadeus: When Models of People Meet

With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is whether it is possible to model agents after humans and, in turn, use these agents to carry out synthetic interactions that predict their real counterparts, or even interactions at a larger scale such as groups or societies. In this paper, we test a more controlled version of this question through chess. We use 8 elite chess players, seal their direct pairwise games, learn each player independently using different methods, and then compose the resulting models on the withheld dyads. To evaluate the generated interactions, we use two measurements: opening-family total variation distance and win-draw-loss (WDL) total variation distance. M1 reduces WDL-TV while leaving opening-family TV largely unchanged, whereas M2 substantially reduces opening-family TV while having little effect on WDL-TV. An additional post-hoc method combining components of the other two retains improvements across both measurements. These results suggest that independently learned models can recover aspects of previously unseen interactions, and that recovery across different behavioural aspects need not be mutually exclusive.