Sex-related invariance across cultures in an online role-playing game

This study examines the Social Dominance Orientation of players of the online roleplaying game World of Warcraft. The World of Warcraft offers an opportunity to investigate social dominance and biological sex differences in an environment where there is no cultural dominance of one sex over another. Social Dominance Orientation has been found to be different between males and females, with males scoring higher. However, this might be the consequence of social context. To this end sex differences between male and female players were investigated in the World of Warcraft environment, as well as the effects of chosen character sex. Player sex and character sex were found to have effects on Social Dominance Orientation. These results add further support to claims that Social Dominance Orientation has the characteristics of a sexually selected disposition to acquire resources and out-compete rival groups.

Blending adaptive governance and institutional theory to explore urban resilience and sustainability strategies in the Rome Metropolitan Area, Italy

Adaptive governance is an emerging theory in natural resource management. This paper addresses a gap in the literature by exploring the potential of adaptive governance for delivering resilience and sustainability in the urban context. We explore emerging challenges to transitioning to urban resilience and sustainability: bringing together multiple scales and institutions; facilitating a social-ecological-systems approach and; embedding social and environmental equity into visions of urban sustainability and resilience. Current approaches to adaptive governance could be helpful for addressing these first two challenges but not in addressing the third. Therefore, this paper proposes strengthening the institutional foundations of adaptive governance by engaging with institutional theory. We explore this through empirical research in the Rome Metropolitan Area, Italy. We argue that explicitly engaging with these themes could lead to a more substantive urban transition strategy and contribute to adaptive governance theory.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Playing immersive games on the REVERIE platform

REVERIE (REal and Virtual Engagement in Realistic Immersive Environments) [1] is a multimedia and multimodal framework, which supports the creation of immersive games. The framework supports the creation of games integrating technologies such as 3D spatial audio, detection of the player’s body movement using Kinect and WIMO sensors, NPCs (Non-Playable Characters) with advanced AI capabilities featuring various levels of representation and gameplay into an immersive 3D environment. A demonstration game was developed for REVERIE, which is an adapted version of the popular Simon Says game. In the REVERIE version, a player tries to follow physical instructions issued by two autonomous agents with different degrees of realism. If a player follows a physical instruction correctly, they are awarded one point. If not, they are deducted one point. This paper presents a technical overview of the game technologies integrated in the Simon Says demo and its evaluation by players with variable computer literacy skills. Finally the potential of REVERIE as an immersive framework for gaming is discussed, followed by recommendations for improvements in future versions of the framework.