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 and influences it simultaneously. This creates a feedback loop and leads to a tight entanglement between the human and the technical system. At the same time, a dynamic, heterogeneous human-human, human-technology, and technology-technology communication takes place, leading to ad hoc coupling between components and different feedback loops. This project investigates global properties and emergent phenomena that arise in AmI-based socio-technical systems from such local feedback loops and their coupling, using two specific scenarios: transportation and emergency/disaster.

SOCIONICAL follows a parallel, multi-layered research approach. Thus, we will explore analytical methods, complex network-based representations, and agent-based models. Advances in modeling and prediction will be verified through large-scale distributed simulations using real-world data. We will develop a methodology to realistically integrate a small number of instrumented users into a large-scale simulation as additional "agents" that experience and control the system. A separate work package is devoted to integrating the various approaches into a coherent framework. To address all technological, psychological, and social dimensions and the realistic diversity of behaviors, we have assembled a multidisciplinary consortium with separate working groups for technology analysis and human technology interaction modeling. SOCIONICAL has a working group dedicated to developing and disseminating guidelines and recommendations for business and policy makers.