A Second-Order Adaptive Network Model for Collective Emotional Response During Reward-Based Gaming

In this paper, a second-order adaptive self-modelling network model is introduced to model collective emotional response during frequent repetitive reward-based gaming. The model makes use of organizational learning using sharing individual learning experiences over shared gaming experiences. Simulation showed relevant prediction of skill building affected by emotional responses and context factors concerning communication and listening. The dynamics of the model were verified and validated through mathematical verification and parameter tuning, resulting in a computational model with a potential of being a successful cornerstone for frequent repetitive reward-based gaming-related research