This paper proposes a Player Age (PA) model with the potential to be generalized to many different games. The model offers insight into the relationship between age and play style. Game developers can use the PA model to gain a better understanding of their target audience, and to optimize adaptive game features (i.e., AI, targeted marketing). In order to become generically applicable, the PA model is based on the literature on life-span developments in physiology and psychology. The PA model states that player age is a linear function of four factors: Speed of Play (-), Performance (-), Preference (+/-), and Time Played (+/-). The model is validated on a data set from Battlefield 3 (FPS). It explains 33.7% of the variance in age (range: 12-65 years) with a standard error of 6.743. To determine the generic quality of the PA model, future work will validate it on games of other genres.
|Number of pages||7|
|Publication status||Published - 1 Jan 2013|
|Event||9th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013 - Boston, United States|
Duration: 14 Oct 2013 → 18 Oct 2013
|Conference||9th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2013|
|Period||14/10/13 → 18/10/13|