The authors propose a small-world network model that combines cellular automata with the social mirror identities of daily-contact networks for purposes of performing epidemiological simulations. The social mirror identity concept was established to integrate human long-distance movement and daily visits to fixed locations. After showing that the model is capable of displaying such small-world effects as low degree of separation and relatively high degree of clustering on a societal level, the authors offer proof of its ability to display R0 properties—considered central to all epidemiological studies. To test their model, they simulated the 2003 severe acute respiratory syndrome (SARS) outbreak.

In the latest versions of massively multiplayer online games (MMOGs), developers have purposefully made guilds part of game environments. Guilds represent a powerful method for giving players a sense of online community, but there is little quantitative data on guild dynamics. To address this topic, we took advantage of a feature found in one of today's most popular MMOGs (World of Warcraft) to collect in-game data: user interfaces that players can modify and refine. In addition to collecting data on in-game player activities, we used this feature to observe and investigate how players join and leave guilds. Data were analyzed for the purpose of identifying factors that propel game-world guild dynamics and evolution. After collecting data for 641,805 avatars on 62 Taiwanese World of Warcraft game servers between February 10 and April 10, 2006, we created five guild type categories (small, large, elite, newbie, and unstable) that have different meanings in terms of in-game group dynamics. By viewing players as the most important resource affecting guild life cycles, it is possible to analyze game worlds as ecosystems consisting of evolving guilds and to study how guild life cycles reflect game world characteristics.

Technocrats from many developed countries, especially Japan and South Korea, are preparing for the human-robot co-existence society that they believe will emerge by 2030. Regulators are assuming that within the next two decades, robots will be capable of adapting to complex, unstructured environments and interacting with humans to assist with the performance of daily life tasks. Unlike heavily regulated industrial robots that toil in isolated settings, Next Generation Robots will have relative autonomy, which raises a number of safety issues that are the focus of this article. Our purpose is to describe a framework for a legal system focused on Next Generation Robots safety issues, including a Safety Intelligence concept that addresses robot Open-Texture Risk. We express doubt that a model based on Isaac Asimov's Three Laws of Robotics can ever be a suitable foundation for creating an artificial moral agency ensuring robot safety. Finally, we make predictions about the most significant Next Generation Robots safety issues that will arise as the human-robot co-existence society emerges.Y.-H. Weng ( ) Conscription Agency, Ministry of the Interior, Republic of China,

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Epidemic simulations and intervention strategy assessments are attracting interest in light of recent and potential outbreaks of infectious diseases such as SARS and avian flu. Universities are using computational modelling and simulation tools to teach epidemiology concepts to students, but integrating domain-specific knowledge and building network-based simulation models are difficult tasks in terms of teacher preparation and learner evaluation. To illustrate challenges to creating network-oriented models in epidemiology education, we introduce an architecture based on demographic and geographic data for building network-oriented epidemic simulation models, and describe our experiences simulating the transmission dynamics of three infectious diseases in Taiwan.

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