Relevant Expertise Aggregation for Policy Selection in Collective Adaptive Systems

Abstract

Many applications of collective adaptive systems for the digital transformation or digital society will necessarily be multi-functional; that is, the collective, as it adapts over time, will be required to resolve many and different types of problem. However, a long-standing issue for political theorists has been whether a decentralised problem-solving regime can be both 'democratic' and 'epistemic', i.e.\ is it possible to devise decision-making and action-determination processes that take into account both majority preference and expert judgement. In this paper, we address this issue in the context of engineering long-lived and sustainable collective adaptive systems, in which autonomous agents adapt conventional rules in order to be congruent with changes in their operating environment. Based on a preliminary proof of concept and inspiration from political science, we propose a reference architecture for relevant expertise aggregation. We conclude that this is one possible design solution to the problem of enabling an collective to assume direct responsibility for adaptation or adoption of problem-solving policies at a large scale, over long periods of time, and addressing diverse problem types.