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Collective adaptive systems

Collective Adaptive System (CAS) as a broad term refers to todays observable indications of a deployment of Information and Communication Technologies (ICT) at a very large scale (e.g. 3.5*109 personal computers, 6.2*109 smart phones, 1.1*109 cars on planet). Taking such a massive ICT deployment, it is not just considered possible, but already a reality that these are programmed to operate cooperatively as very large scale collectives. As for example, smartphone apps have demonstrated globe-spanning cooperative sensing applications, liquid democracy applications, logistics and traffic management applications, energy management applications, etc. One essential aspect of such globe-spanning ICT collectives is that they often exhibit properties typical 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 observed on the level of the collective (macro-level), it (v) it usually arises from competition and cooperation among the individual elements (micro-level). The overall behavior of the collective (macro-behaviour) is the result of (vi) a huge number of decisions made every moment by many individual entities (micro-interactions), possibly leading to (vii) the emergence of unexpected phenomena. A critical challenge for CAS is to design and implement collectives able to operate in a more or less autonomous way, with little or no human interaction. In this endeavor, self-adaptation and self-management have emerged as two interrelated facets of CASs. Self-adaptive collectives in a top down strategy evaluate their own global behavior and change it when the evaluation indicates that they are not accomplishing what they were intended to do, or when better function or performance is possible. Here a challenge is to identify how to change the behaviors of the individuals so as to achieve the desired collective improvement. In self-managing collectives, individuals interact locally, typically according to a small set simple rules. Achieving a desirable collective behaviour is attempted with a bottom up strategy, in that local interaction policies are adopted so as to steer global behavior. Self-organisation is the process of enabling a system to change its organisation in case of environmental changes without explicit external command. We can distinguish between: weak self-organisation where from an internal point of view, there is re-organisation under an internal central control or planning; and strong-selforganisation (also referred to as "emergence"), where there is re-organisation with no explicit central control, either internal or external [1]. Building upon the mechanisms of self-adaptation and self-management, we may ask the questions on the potentials and opportunities of turning massively deployed systems to a globe spanning superorganism of socially interactive elements [2]. While the individual elements are of heterogeneous provenance and typically act autonomously, we can assume that they can (and will) self-configure into large scale cooperative collectives, either with or without human governance [3,4]. We may not assume a common objective or central control, but rather volatile network topologies, codependence and internal competition, non-linear and non-continuous dynamics, and sub-ideal, failure prone operation. The recent literature has referred to these emerging massive collectives as "superorganisms" [5], since they exhibit properties of a living organism (like e.g. 'collective intelligence') on its own. In order to properly exploit such superorganisms, we need to develop a deeper scientific understanding of the foundational principles by which they operate.