A key observation in the "post digital revolution society" is that information and communication technologies (ICT) has become interwoven with human behaviour, the "fabric of everyday life" and social structures to such an extent, that the separating view of a "physical world" being connected with a "digital world" is ceasing. Today we talk about one "cyber-physical" world (Cyber-Physical Systems, an NSF program develpod by Helen Gill in 2006), referring to a tight entanglements of real world physical objects (things, appliances) and processes (services), with their digital data representation and computations in communication networks (the "cyber"). Embedded, wirelessly connected tiny compute plattforms equipped with a multitude of miniaturized sensors collect data about phenomena, analyze and interpret that data in real time, reason about the recognized context, make decisions, and influence or control their environment via a multitude of actuators. Sensing, reasoning and control, thus, are tightly interconnecting the physical and digital domains of the world, with feedback loops coupling one domain to the other. They implement notions of autonomous adaptive behavior.

Taking the plenty-hood of todays ICT platforms with their computational, sensory, reasoning, learning, actuation and wireless communication capacities (smart phones, autonomous vehicles, digital signage networks, stock exchange broker bots, wearable computers, etc.), it is not just considered possible, but already a reality that these are programmed to operate cooperatively as planet scale ensembles of collective adaptive computing system (CAS). CAS research asks questions on the potential and opportunities of turning massively deployed computing systems to a globe-spanning super-organism, i.e. compute ensembles exhibiting properties of living organisms, like e.g. "collective intelligence" on their own. Essential aspects of CAS are that they often exhibit properties typically observed in complex systems, like (i) spontaneous, dynamic network configuration, with (ii) individual nodes acting in parallel, (iii) constantly acting and reacting to what the other agents are doing, and (iv) where the control tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it (v) has to arise from competition and cooperation among the individual nodes, so that the overall behavior of the system is the result of a huge number of decisions made every moment by many individual entities.

In order to develop a deep scientific understanding of the foundational principles by which CAS operate (see the EU research priority FoCAS, www.focas.eu) we need to address evident foundational research concerns like:

1. Understanding the trade-offs between the potentials of top-down (by design) adaptation means and bottom-up (by emergence) ones, and possibly contributing to smoothing the tension between the two approaches.
2. Understanding the “power of the masses” principle as far as participatory ICT processes are concerned. We need to understand how and to what extent even very simple collective phenomena and algorithms – when involving billions of components – can express forms of intelligence superior than that of traditional AI.
3. Understanding properties concerning the evolutionary nature of CASs, e.g. open-ended (unbounded) evolutionary systems, the trade-off and interaction between learning and evolution, and the effect of evolution on operating and design principles.
4. Understanding the issue of pluralism and diversity increase in complex systems as a foundational principle of self-organization, self-regulation, resilience and collective intelligence.
5. Laying down new foundations for novel CAS theories for complex adaptive systems modeling large-scale socio-technical super-organisms (including lessons learned from applied psychology, sociology, and social anthropology, other than from systemic biology, ecology and complexity science).

In order to develop principles and methods for the design, implementation and operation of globe-spanning CAS we identify systems research concerns like:          

1. Opportunistic Information Collection: Systems need to be able to function in complex, dynamic environments where they have to deal with unpredictable changes in available infrastructures and learn to cooperate with other systems and human beings in complex self-organized ensembles.
2. Living Earth Simulation: The provision of a decentralized planetary-scale simulation infrastructure strongly connected to the worlds online-data sources (search engines, power grids, traffic flow networks, trade centers, digital market places, climate observatories, etc.) is needed as a means to enable a model-based scenario exploration in real time - at different degrees of detail, varying time-scales, integrating heterogeneous data and models.
3. Collaborative Reasoning and Emergent Effects: Reasoning methods and system models are needed that combine machine learning methods with complexity theory to account for global emergent effects resulting from feedback loops between collaborative, interconnected devices and their users.
4. Awareness: Whereas today’s context-aware systems are able to make sense of the activity of single users and their immediate environment, future systems should be able to analyze, understand and predict complex social phenomena on a broad range of spatial and temporal scales. Examples of the derived information could be: shifts in collective opinions and social attitudes, changes in consumer behavior, the emergence of tensions in communities, demographics, migration, mobility patterns, or health trends.
5. Cases: Look at the specifics of design, implementation and operational principles rooted in the very nature of application domains of societal relevancy: e-health eco-systems, fleets of self driving vehicles, reindustrialization (Industry 4.0), physical internet (intelligent logistics), digital economy, energy management and environmental care, citizen science, combinatorial innovation, liquid democracy, etc.

see also: www.focas.eu/manifesto