The changing context of modern and emerging ICT scenarios calls for a radical re-thinking of the way how people’s needs, aims and capabilities are backed by the use of technology, and respected by technological advances. While ICT research has made a considerable advance in the addressed research issues from pure systems-oriented to nowadays user-centred questions, a whole new epoch of research agenda is arising when considering ICT research as it impacts society as a whole, rather than a single user. To the best of our knowledge, SOCIONICAL for the first time systematically raises research question at the confluence of humans acting with and based on AmI (and beyond) technology. Emergent phenomena of induced behaviour, impacted dynamics, changing processes and the complexity of interaction are becoming the more noticeable, the more a society is ready and eager to use, underpinned by, or reliant to ICT. Socio-technical systems are striving to become the next grand challenge in ICT research, searching for an understanding of the mutual interplay of technological advances in ICT, and the dynamics of social systems, like the problems of human communication, the mobility of humans and goods, the enabling of individual lifestyles and independent living, the dynamics of markets and economic growth, the distribution of wealth, welfare, unemployment or health, to name a few.

Towards this grand challenge SOCIONICAL is proposing an empirical data based approach, bringing together models that have been developed and matured in their respective disciplines over several decades (mathematical statistics, statistical mechanics, econo-physics, thermodynamics, sociophysics, econometrics, phenomenology, etc.), and involving the analysis apparatus of simulations for model evaluation and validation based assessment. In doing so, SOCIONICAL crosses the boundaries between different scientific disciplines (physics, computer science, mathematics, social sciences and even humanities), addressing modelling techniques, simulation experiments and analytical methods from fields that might not be available in the original discipline to which the system belongs. This cross discipline combination of complexity research methods approach promises significant advance over the state-of-the-art:

• To the best of our knowledge, no attempt has been made to model the dynamics of emergent phenomena in very large AMI based socio-technical systems at the confluence of analytical methods and empirical data sets. SOCIONICAL will deliver a modelling methodology that – as opposed purely analytical models and opposed to pure descriptive statistics of empirical data – foster an integrated analysis.
• The model size SOCIONICAL is addressing goes way beyond cardinalities of 10⁵ – 10⁷ entities with almost total mutual interdependencies (giving 10¹⁰ – 10¹⁴ constraints), and will harness the complexity of such systems based on simulation based evaluation. Contemporary, highly advanced parallel and distributed simulation engines cannot cope with such system complexities.
• Harnessing models of such complexity poses challenges to modelling skills and data management not seen in the respective disciplines before. SOCIONICAL will provide techniques encoding expertise and modelling skills combined with data management strategies, so as to allow for a wide engagement of research communities in the SOCIONICAL methodology
• The process of identifying types of models and the assessment of their suitability to represent realistic socio-technical systems has not been studied before. SOCIONICAL will develop a quality-of-model framework, which, based on empirical evidence based reasoning, will quantify the “degree of support” the model gives to the result obtained from the evaluation of the model.
• The planned framework that integrates large scale complex systems simulations of sociotechnical systems with a user emerged in a virtual reality environment as just another ‘simulation agent’ will be a major advance towards realistic modelling of large scale sociotechnical systems. This “mixed level” simulation allows comparing the behaviour of the simulated agents with real humans in the same ‘environment’. It also allows to infer behavioural models from the real socio-technical user interactions in the AmI systems, and to extrapolate from these to the large scale through the simulation models.
• To engage a broad research audience from the scientific community in the various disciplines it is ultimately important to ease the access to the SOCIONICAL methodology. Innovative algorithms and their substantiation in software frameworks and toolsets will be packaged into the SOCIONICAL framework, ready for access and use by other research communities.
• Ultimately, SOCIONICAL – as opposed to classical research methods based on models and in isolated and controlled environments – attempts to consider the reality as the test-bed for its investigation. This is a radical change in the style of how natural and social scientists understand research, apparently however, the phenomena SOCIONICAL is investigating presumably cannot be studied successfully when imposing abstractions from reality. Sociotechnical systems do happen in reality.