Viability of Sustainable Development as Implied by Metabolic Cycles

TRANSCEND MEMBERS, 23 Sep 2024

Anthony Judge | Laetus in Praesens - TRANSCEND Media Service

AI-assisted Clarification of Cognitive Challenge of Turbocharging SDGs

Introduction

23 Sep 2024 – The UN’s ambition to “turbocharge” the Sustainable Development Goals on the occasion of the 2024 Summit of the Future was previously explored through interaction with AI (Turbocharging SDGs by Activating Global Cycles in a 64-fold 3D Array, 2024). The detection by inspection, and subsequent visualization of feedback loops in that experimental procedure with ChatGPT and Claude, proved to be encouraging to the point of envisaging an AI-enabled automated detection of indicative SDG cycles that could be essential to their viability.

As feedback loops in cybernetic terms, such cycles have been a notable feature of the extensive analysis of the networks of thousands of problems and strategies profiled in the online Encyclopedia of World Problems and Human Potential (Feedback Loop Analysis in the Encyclopedia Project, 2000; Tomas Fülöpp, Loop Mining in the Encyclopedia of World Problems, 2015). For example, a total of 473 5-loop cycles were detected in the first, and 502 in the second.

The focus in the development of those recent experiments with AI has been on how to configure cycles in a systemically meaningful manner to enhance their comprehensibility and memorability. It was argued that a fruitful approach to “turbocharging” could be usefully framed in terms of enhancing connectivity dynamically. The preoccupation with visualization was explored through mapping onto polyhedra of requisite complexity — for which the 64-vertexed truncated tesseract was highlighted.

This polyhedral bias followed from the insight of Buckminster Fuller: All systems are polyhedra (Synergetics [400.011-02]) — an insight echoed and elaborated by Mariah Guimarães Di Stasi1 and Anja Pratschke (Acting Cybernetically in Architecture, Cybernetics and Human Knowing. 27, 2020, 3). From that perspective it is appropriate to ask how the SDGs might be understood in polyhedral terms as an indication of the “organization architecture” and “knowledge architecture” fundamental to their viability in cybernetic terms (Ousanee Sawagvudcharee, et al, Understanding Organizational Studies Toward Knowledge Cybernetics, International Journal of Economics, Business and Management Research, 4, 2020, 6).

Characterized as they are by vertices, edges and faces, polyhedra as configurations of multi-edged faces then invite recognition in systemic terms — the corollary to Fuller’s insight, namely all polyhedra are systems. The 48 faces of the truncated tesseract would then suggest its recognition as a system of 48 cycles of various dimensions. To the extent that the SDGs can be meaningfully mapped onto such a polyhedron, this evokes the question as to whether the coherence of the SDGs as a global strategic system could be most fruitfully understood as a configuration of cycles — rather than in conventional linear terms. The question then is whether such cycles are appropriately interlocked by feedback groups to ensure the viability of that strategic enterprise.

The somewhat elusive perspective thereby evoked can be fruitfully compared with the set of metabolic pathways fundamental to metabolism and the viability of life in human and other species. Curiously there is a marked tendency to reduce appreciation of viable metabolism to a set of micronutrients which are however interlinked by those metabolic pathways and a number of key cycles. It might then be asked whether the “psychosocial life” of the collective — exemplified by the SDGs — is characterized by key cycles whose dynamics are fundamental to any effort to “turbocharge” the UN’s strategic initiative (Memorable Configuration of Psychosocial “Vitamins”, “Amino acids” and “Minerals”, 2024). Given widespread recognition of metabolic disorders, it might then be asked whether these offer insights into potential “sustainability disorders”.

Whilst the UN’s Summit of the Future is preoccupied with a 5-factor framing of “turbocharging” through 6 transitions, it is quite unclear whether there is any explicit recognition of what might be considered the “key cycles” essential to its viability — as could have been appropriately recognized in its inter-governmentally negotiated, action-oriented Pact for the Future. This failure would seem to date from the assumptions of the World3 model by which the original Limits to Growth model was framed in 1972 — and their subsequent embodiment in the Earth4All articulation of the Club of Rome in an unquestioned 5-fold/6-fold pattern. Any such pact could be considered “cyclically defective”, given the lack of interaction with the wider population (Derrick Broze, Summit of the Future: the public still has not seen the final draft of the Pact for the Future, Nexus, 20 September 2024).

Inspired by the understanding of key cycles essential to biological life, there is then a case for challenging AI to suggest correspondences in systemic terms to cycles potentially relevant to sustainable governance — in the spirit of general systems research. As the following exchange indicates, both AIs responded surprisingly proactively to this challenge. A notable correspondence was for example suggested between the “urea cycle” and that of “waste management”. Other potential correspondences have been articulated of relevance to sustainable development.

The issue in what follows is therefore how to develop an AI-enabled methodology with respect to the detection and visualization of cycles meaningful to sustainable development. The point of departure was the extraction of an adjacency list based on the traditional set of hexagram-encoded psychosocial conditions, given the manner in which transformations from one such condition to another are notably defined in terms of one (or more line changes). Such an adjacency list was extracted from the set of documents accessible from Transformation Metaphors derived experimentally from the Chinese Book of Changes (1997). As an experiment this approach was partially justified by past recognition of a degree of correspondence between disparate 64-fold patterns, including the genetic codons and the Mathematical Subject Classification. The 16-fold articulations of the SDGs and logical connectives was seen as suggestive of a significant relationship within a 64-fold pattern.

Conventional deprecation of that unusually detailed traditional articulation of patterns of change as “divination” was challenged earlier by comparison with the “modelling” on which governance is now so reliant (Misleading Modelling of Global Crises, 2021). There is the considerable irony that conventional interaction with AI, and any statistical modelling it may enable, could in turn be compared with “divination” — and equally suspect. Methodologically it is assumed here that there is value in according a degree of value to traditional knowledge systems, as argued by Susantha Goonatilake (Toward a Global Science: Mining Civilizational Knowledge, 1999).

A particular interest of the following exercise is the manner in which the iterative interaction with either ChatGPT or Claude clarified how that process might be used to elicit feedback loops of importance to the viability of sustainable development. The results arising from this process, as reported here, are therefore primarily of value as indicative of how that iterative process might be further developed and the nature of the AI engagement with that possibility. In this sense the exercise is understood as a demonstration of method — especially for institutions reluctant or resistant to its potential. The demonstrated ability of AIs to develop a narrative clarifying the coherence of particular cycles is a facility which merits critical development.

As in the previous experiments, the responses of ChatGPT 4o are distinctively presented below in grayed areas, in parallel with those of Claude 3.5. Given the length of the document to which the exchange gives rise, the form of presentation has itself been treated as an experiment — in anticipation of the future implication of AI into research documents. Web technology now enables the whole document to be held as a single “page” with only the “questions” to AI rendered immediately visible — a facility developed in this case with the assistance of both ChatGPT and Claude 3 (but not operational in PDF variants of the page, in contrast with the original). Reservations and commentary on the process of interaction with AI to that end have been discussed separately (Methodological comment on experimental use of AI, 2024). Whilst the presentation of responses of two AIs could be readily considered excessive, it offers a “stereoscopic” perspective highlighting the strengths and limitations of each.

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