Interevent time distributions of human multi-level activity in a virtual world

• We use records of player activities in the massive multiplayer online game Pardus over 1238 consecutive days, and analyze dynamical features of sequences of actions of players.
• We show that the interevent time distributions of actions in the Pardus universe follow highly non-trivial distribution functions from which we extract action-type specific characteristics.
• This study of multi-level human activity can be seen as a dynamic counterpart of static multiplex network analysis.

Studying human behavior in virtual environments provides extraordinary opportunities for a quantitative analysis of social phenomena with levels of accuracy that approach those of the natural sciences. In this paper we use records of player activities in the massive multiplayer online game Pardus over 1238 consecutive days, and analyze dynamical features of sequences of actions of players. We build on previous work where temporal structures of human actions of the same type were quantified, and provide an empirical understanding of human actions of different types. This study of multi-level human activity can be seen as a dynamic counterpart of static multiplex network analysis. We show that the interevent time distributions of actions in the Pardus universe follow highly non-trivial distribution functions, from which we extract action-type specific characteristic “decay constants”. We discuss characteristic features of interevent time distributions, including periodic patterns on different time scales, bursty dynamics, and various functional forms on different time scales. We comment on gender differences of players in emotional actions, and find that while males and females act similarly when performing some positive actions, females are slightly faster for negative actions. We also observe effects on the age of players: more experienced players are generally faster in making decisions about engaging in and terminating enmity and friendship, respectively.