Michael Hogan

Research on meditation and mindfulness practice has flourished in recent years. While much of this research has focused on well-being outcomes associated with mindfulness practice, less research has focused on how perception of self may change as a result of mindfulness practice, or whether these changes in self-perception may be mechanisms of mindfulness in action. This is somewhat surprising given that mindfulness derives from traditions often described as guiding people to realise and experience the non-separation of self from the world or its ‘oneness’ with the whole of reality. The current study used a collective intelligence methodology, Interactive Management (IM), to explore the nature of oneness experiences. Five IM sessions were conducted with five separate groups of experienced meditators. Participants generated, clarified, and selected oneness self-perceptions they believed most characterised their experience both during meditation and in their everyday experience in the world. Each group also developed structural models describing how highly ranked aspects of oneness self-perceptions are interrelated in a system. Consistent themes and categories of oneness experience appeared across the five IM sessions, with changes in the sense of space (unboundedness), time, identity, wholeness and flow highlighted as most influential. Results are discussed in light of emerging theory and research on oneness self-perception and non-dual awareness.

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider barriers to legitimate and effective algorithmic governance and the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.