Argumentation Logic as a Formal Foundation for Collective Intelligence Systems: a Roadmap

Collective intelligence is shared (or group) intelligence that emerges when aggregating the collective efforts of many individuals (Woolley et al., 2010). The success of collective intelligence (CI) systems rely heavily on the effectiveness of the aggregation mechanism (Surowiecki, 2005). Individuals may contribute a simple data element (for instance, a number representing an estimated or predicted value), whereas the aggregation mechanisms is quite simple (group’s statistical average, voting, or a prediction market); alternatively, some CI systems require individuals to contribute much more complex knowledge (for example when posting content to Wikipedia or when reporting a sightseeing to a citizen science environmental monitoring system (Bonney et al., 2009)), requiring more sophisticated aggregation mechanisms (Lévy and Bonomo, 1999). The focus of the current investigation are those CI systems that require the contribution of rich (often multi-dimensional) knowledge elements and therefore must employ complex aggregation mechanisms, often acting as a sociotechnical system that involves both human actors and automated tools.