About a month ago, we finished our 2-year long EC-Horizon2020 project on Human-Machine Networks (HUMANE). The first task of this project was to perform a systematic literature review to see what the state of the art in understanding such systems is.
The short answer is that we do not know much! And what we know is not very cohesive. In other words, design, development, and exploration of human-machine systems have been done mostly through trial and error and there has not been much theory or systematic thinking involved.
We wrote a review paper to report on our systematic exploration of the literature. It took us nearly 18 months to finally get the paper published, but it was worth every second waiting as we managed to get it out at the ACM Computing Survey, which has the highest impact factor among all the journals in Computer Science.
And the abstract says:
In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.
