II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Twintelligent Knowledge

2. Collective Local/ Global Brain Intelligences

Riedl, Christoph and David De Cremer, eds. The potential and challenges of AI for collective intelligence. Collective Intelligence. February, 2025. Twenty-two practitioners in the UK and USA such as Gina Lucarelli, John Cartlidge and Joan Condell describe how their projects such as PSi: A scalable approach to community-led public decision-making; The AI4CI loop and Perspectives and on the UNDP Accelerator Network are integrating and taking advantage of these novel occasions.

Tackling large scale problems like climate change and the Sustainable Development Goals, requires taking a collective approach. Artificial Intelligence (AI) offers tremendous potential to enhance collective intelligence, both as an actor that contributes to the solution directly, and as a tool and mentor that helps coordinate human collective intelligence. Collective Intelligence invited experts and practitioners to highlight key challenges and explain how they employ AI to advance novel solutions.

See, Judi, et al. People are Like Plutonium. Collective Intelligence. 2.2, 2023. In this new journal, Sandia National Labs and Idea Connection Systems, Rochester scholars cast an innovative vista by which to suggest a deep affinity between a widest span of persons and physics. Going forward so it seems, a grand scientific affirmation and re-marriage of macro and micro ecosms can at last be affirmed and verified. Thus this once and future invariant feature at the heart of wisdom can become a luminous, truth we so need.

An analogy is drawn between the study of human behavior and of the element plutonium to demonstrate that soft and hard sciences are more similar than different. The studies of human behavior and plutonium follow a common research cycle akin to Thomas Kuhn’s paradigm changes which evinces that the thought processes and methodologies for success are congruent in these far removed realms. The primary implication from this analogy is that scientists in all disciplines could well buffer the distinction between soft (human) and hard (universe) phases. Focusing on similarities rather than differences among researchers from disparate disciplines would serve as a vital way to enhance collective intelligence.

Stepney, Susan. Computing with Open Dynamical Systems. ieeexplore.ieee.org/document/9475943.. A presentation by the University of York computation theorist (search) at a 2021 IEEE Conference in the IEEEXplore journal is available at this address. Its intent is to show how natural complexities can also be appreciated to perform as information processors

Computation is often thought of as a branch of discrete mathematics, using the Turing versions. That model works well for conventional applications such as word processing and database transactions. But much of the world's computer power resides in embedded devices, sensing and controlling complex physical processes. Other computational approaches might be better suited to such as a form of complex dynamical systems. One particular view is reservoir computing which can apply to different material substrates and integrate sensing and computing in a single physical package. (Excerpt)

Reservoir computing is a framework derived from recurrent neural network theory that maps input signals into high dimension computational spaces through the dynamics of a fixed, non-linear system called a reservoir. (Wikipedia and Recent Advances in Physical Reservoir Computing by Gouhei Tanake, et al in Neural Networks (115/7, 2019).

Thieu, Thoa and Roderick Melnik. Social Human Collective Decision-making and Applications with Brain Network Models. arXiv:2307.05731. As an awareness of the actual process and value of cooperative cognition grows, Wilfrid Laurier University, Canada system theorists (search RM) describe their study of way to enhance diverse, public engagements.

In this chapter, we consider probabilistic drift-diffusion models and Bayesian inferences to better assist this title issue. We explain the models and representative numerical examples. We also give a review of recent developments in human collective decision-making and its applications with brain network research such as the role of neuromodulation, reinforcement learning in decision-making processes. Finally, we call attention to open problems, and promising approaches iincluding those arising from nonequilibrium considerations. (Excerpt)

Wheatley, Thalia. The Emerging Science of Interacting Minds. Perspectives on Psychological Science. November, 2023. In a special issue on The Psychology of Collectives, the Dartmouth College psychologist and director of its Consortium for Interacting Minds joins a welling chorus which quantifies and advocates this spiral moment which can, at last, appreciate and facilitate the value of common wisdom.

For over a century, psychology has focused on the mental processes of a single individuals, but humans rarely navigate the world in isolation. A person’s successful development, traits, dispositions, are due to family and many friends. Social interaction makes us who we are, how we think, and our behavior. Here we discuss issues that have limited a robust science of how minds engage, commune, and new approaches beginning to address these aspects. A deep understanding of the human mind requires studying the public contextual milieu within which it originates and proceeds. (Abstract)

In this article, we discuss prior research that notes the vital consequences of social interaction for individuals and collectives. We then address why the study of interaction itself—the meeting of minds that co-constitutes thought and behavior—has been empirically neglected despite its lifelong importance. Finally, we explain why it seems we are on the expansive cusp of a conceptual and methodological advance that will refocus the field on the importance of social interactions for the purpose of understanding the human mind. (1)

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