We will develop Complexity Science based modelling, prediction and simulation methods for large scale socio-technical systems. We focus on the specific example of Ambient Intelligence (AmI) based smart environments. A key component of such environments is the ability to monitor user actions and to adjust its configuration and functionality accordingly. Thus, the system reacts to human behaviour while at the same influencing it. This creates a feedback loop and leads to a tight entanglement between the human and the technical system. At the same time there is dynamic, heterogeneous human-human, human-technology, and technology-technology communication leading to ad-hoc coupling between components and different feedback loops. The project will study global properties and emergent phenomena that arise in AmI based socio-technical systems from such local feedback loops and their coupling on two concrete scenarios: transportation and emergency/disaster.SOCIONICAL takes a parallel, multi facetted research approach. Thus, we will investigate analytical methods, complex networks based representations, and agent based models. The advances in modelling and prediction will be verified by large scale, distributed simulation driven by real life data. We will develop a methodology by which a small number of instrumented users can be realistically integrated in a large scale simulation as additional 'agents', experiencing the system and driving it. A separate WP is devoted to the integration of different approaches into a coherent framework. Another ensures generalization.To take into account all technological, psychological and social dimensions and realistic diversity of behaviours we have assembled a multi disciplinary consortium with separate WPs for technology analysis and the modelling of human technology interactions.SOCIONICAL has a WP devoted to the development and dissemination of guidelines and recommendation for businesses and policy makers.