The Superorganism of Massive Collective Wearables

This workshop asks questions on the potential and opportunities of turning massively deployed wearable systems to a globe-spanning superorganism of socially interactive personal digital assistants. While individual wearables are of heterogeneous provenance and typically act autonomously, it stands to reason that they can (and will) self-organize into large scale cooperative collectives, with humans being mostly out-of-the-loop. A common objective or central controller may thereby not be assumed, but rather volatile network topologies, codependence and internal competition, non-linear and non-continuous dynamics, and sub-ideal, failure-prone operation. We refer to these emerging massive collectives of wearables as a “superorganism”, since they exhibit properties of a living organism (like e.g. ‘collective intelligence’) on their own.

One essential aspect of such globe-spanning collective ensembles is 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 properly exploit such superorganisms, this workshop concerns itself with the development of a deeper scientific understanding of the foundational principles by which they operate. To this end, the workshop attempts to address the following foundational research concerns:

• Understanding the trade-offs between the power of top-down (by design) adaptation means and bottom-up (by emergence) ones, also by studying how the two approaches co-exist in modern wearable ICT systems, and possibly contributing to smoothing the tension between the two approaches.
• Understanding the “power of the masses” principle as far as participatory wearable ICT processes are involved. In particular, this implies understanding how and to what extent even very simple collective phenomena and algorithms – when involving billions of wearables – can express forms of intelligence much superior than that of more traditional AI techniques.
• Understanding the issue of diversity and of diversity increase in complex systems and in service/data systems and how diversity of structure and behavior is currently accommodated in wearable ICT systems. As of now, most studies focus on a limited number of different classes, which is far from approximating the diversity of existing systems.
• Laying down new foundations for the modelling of large-scale Human-ICT organisms and their adaptive behaviors, also including lessons from applied psychology, sociology, and social anthropology, other than from systemic biology, ecology and complexity science.
• Identifying models and tools by which individual organs of the systems can influence and direct “by design” the emergent adaptive behavior of the whole system, or at least of substantial parts of it.

 

Further, the workshop attempts to address the following systems research concerns:

• 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 selforganized ensembles.
• 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.
• Social 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.