In the public goods game, players can be classified into different types according to their participation in the game. It is an important issue for economists to be able to measure players’ strategy changes over time which can be considered as concept drift. In this study, we present a method for measuring changes in items’ cluster membership in temporal data. The method consists of three steps in the first step, the temporal data will be transformed into a discrete series of time points then each time point will be clustered separately. In the last step, the items’ membership in the clusters is compared with a reference of behaviour to determine the amount of behavioural change in each time point. Different external cluster validity indices and area under the curve are used to measure these changes. Instead of different cluster label comparison, we use these indices a new way to compare between clusters and reference points. In this study, three categories of reference of behaviours are used 1- first time point, 2- previous time pint and 3- the general overall behaviour of the items. For the public goods game, our results indicate that the players are changing over time but the change is smooth and relatively constant between any two time points.
Fattah, Polla, Uwe Aickelin, and Christian Wagner. “Optimising rule-based classification in temporal data.” arXiv preprint arXiv:1607.05913 (2016).