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18 July 2025 | Draft

Remembering the Magna Carta on Human Environment

Use of AI to develop mnemonic aids to comprehension of complex strategic articulations

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Introduction
Mnemonic exploration of strategies by DeepSeek
Mnemonic exploration of strategies by ChatGPT
Experimental visualization of SDG cycles

PDF versions of this document do not enable direct access to AI responses to questions posed below. Experimentally readers may be transferred by a link from the "Question" in the PDF version to the particular question in the original web version -from which they can access the response (as in that non-PDF version). That link can also be used as a hyperlink citation to individual questions.


Introduction

The thematic articulation of most fundamental strategies is completely unmemorable to most: Universal Declaration of Human Rights (30); Sustainable Development Goals (17), Magna Carta on Human Environment (26). The latter is not a single document but rather a nickname for the Stockholm Declaration of 1972, the first UN Conference on the Human Environment, which established principles for environmental protection and human rights, like the right to a healthy environment. 

The unmemorability of each such document is especially the case with regard to the systemic feedback loops implied by each set of articles. There are many such strategic documents -many already forgotten and all readily forgettable, as reviewed separately (Configuring Multiple Disparate Sets of Strategic Principles, 2025).

One simple approach for mnemonic purposes is to associate a letter of the alphabet with each article, as particularly suggested by the case of the 26 principles in the foundational Stockholm Declaration of 1972. Groups of letters could then be formed into words as an indicator of the systemic links between the themes of individual articles. Of interest in this respect is to take advantage of the skills of AI in formulating such clues to remembrance -an exercise easily repeated with various strategic declarations and with various AIs. In the following exercise, the challenge was presented to the DeepSeek AI for the case of the Magna Carta on Human Environment -otherwise variously confused as the Stockholm Declaration.

The Magna Carta on Human Environment (Malta, 2017), or "Malta Declaration", draws directly from the 26 principles articulated in the Declaration of the United Nations Conference on the Human Environment (Stockholm, 1972) . The latter is widely regarded as the global Magna Carta for environmental rights -and is occasionally heralded in legal scholarship as the "Magna Carta on Human Environment". It set the framework for later articulations such as the Rio Declaration on Environment and Development (1992). It also created the framework for later national initiatives (such as Malta’s constitutional reforms) seeking to realize and enforce its principles domestically and internationally. Such documents express the right to a healthy environment as fundamental, and Malta’s later actions are a continuation and local implementation of the vision established in 1972. Reference is also made to a distinct Malta Declaration by the members of the European Council on the external aspects of migration: addressing the Central Mediterranean route (February 2017).

Aspect Stockholm Declaration (1972) Magna Carta on Human Environment (Malta, 2017)
Origin United Nations Conference National/International reaffirmation (with Malta referencing it)
Core Content 26 Global Principles on Environment and Rights Reiterates/Embodies these global environmental rights
Key Idea Healthy environment is a human right Embedding right to healthy environment in law and policy
Influence Foundation of modern environmental law National policy/constitutional support and further development

The following reproduction of the engagement with DeepSeek includes both the unedited preliminary "internal" reflections ("Chain of Thought") of the AI and the final considered response. This could be considered valuable to understanding the issues meriting consideration prior to the formulation of any response -even if the exercise is undertaken without AI assistance. The questions could have been framed otherwise to focus to a far greater extent on the memorability of the systemic links between article themes of relevance to policy makers -through use of mnemonic words and phrases. The exercise can of course be repeated with other AIs for comparative purposes.

The exercise was then repeated with ChatGPT, using more policy-oriented prompts for the case of the UN's 17 Sustainable Development Goals -a late transmogrification of the Stockholm Declaration. The purpose was to explore possibilities of rendering more widely memorable both those goals and the systemic linkages between them as being vital to their sustainability. Such exercises frame the question as to whether strategies indeed lend themselves to meaningful sonification (A Singable Earth Charter, EU Constitution or Global Ethic? 2006). Significant in that respect is the initiative of Franz Josef Radermacher of the Institute for Applied Knowledge Processing (FAW, Ulm) in association with the Global Marshall Plan Initiative (12 songs of The Globalization Saga: Balance or Destruction, 2004).

Given the major challenge of human rights, it might well be asked why there are no such aids to the memorability of the Universal Declaration of Human Rights (UNDHR) and the interlinkages between its 30 themes. Given the potential correspondence between the 26 principles of the Magna Carta on the Human Environment and the 26 principles of governance articulated by Ray Ison and Ed Straw (The Hidden Power of Systems Thinking:: governance in a climate emergency, 2020), the method could well be explored in the latter case. The distinctive sets of 26-fold principles have been experimentally mapped onto polyhedra with AI assistance in a separate approach to memorabilty (Mapping of a 26-fold framework of strategic relevance, 2025).

Mnemonic exploration of strategies by DeepSeek

Question to AI: Do you have access to the 26 articles of the Magna Carta on Human Environment (Stockholm, 1972). [Show/Hide AI response]

Question to AI: The 26 articles are not memorable to most people -nor are the systemic relationships between them. If you were to allocate one letter of the 26 letter alphabet to each article, could you generate some mnemonic words (or phrases) to make each article systemically memorable, as is done when teaching children. [Show/Hide AI response]


Question to AI: Much appreciated. Given what you have scoped out, could you comment on the possibility of turning the 26 into a poem or the lyrics of a song. [Show/Hide AI response]


Question to AI: Very much appreciated. Go ahead. [Show/Hide AI response]


Mnemonic exploration of strategies by ChatGPT

Question to AI: The UN's 17 Sustainable Development Goals (with their 169 tasks) are far from being memorable to most people -nor are the systemic relationships between them so vital to the sustainability of the goals together. Could you allocate letters of the alphabet to each of the goals as mnemonic triggers -such as to enable you to generate some mnemonic words (or phrases) to make memorable subsets of the goals in terms of their systemic connectivity , as might be valuable to policy-makers. A striking variant of such mnemonics is Harold Baum's Biochemists Songbook (1995) -rendering memorable complex patterns of metabolic pathways. [Show/Hide AI response]


Question to AI: Given the systemic connectivity implied by the SDGs, could you comment on the requisite number of such mnemonic phrases potentially necessary to encode that variety of feedback loops. How could they be rendered memorable together as an indication of sustainability. How could some pathways be set aside as secondary to achieve this. [Show/Hide AI response]


Question to AI: Part of the challenge is the degree to which any design opts for phrasing readily perceived as "platitudinous" slogans -in contrast to phrasing with a more memorable "bite". Should there be two strategic variants: "nice" (hopefully politically correct) and "nasty" (realistically cutting). [Show/Hide AI response]


Question to AI: The suggested table would be appreciated. I have a concern that it is obviously easier to focus on 2-theme loops, whereas the Biochemists Songbook is remarkable in interlinking multiple phases in the citric acid cycle, urea cycle, glyoxulate cycle, etc Are the major SDG pathways of such metabolic complexity. How many are there. [Show/Hide AI response]


Question to AI: Could you give me citations for the 30 to 60 key multi-goal pathways, each involving 4–7 SDGs. How has it been suggested that they should be rendered memorable and communicable. [Show/Hide AI response]


Question to AI: A seed set would indeed be helpful initially. [Show/Hide AI response]


Question to AI: You proposed a mapping of the 10 interlocking cyclic pathways you have listed -effectively a pattern of strategic metabolic pathways. Could you do that. [Show/Hide AI response]


Question to AI: Your proposed extensions would be valuable. However it would be prudent to look at why such an approach has not been taken, even for the metabolic pathways. Why has the documentation of the set of 30-60 pathways apparently not given rise to such presentations. Is there an inherent lack of interest in systemic memorability for strategic purposes. [Show/Hide AI response]


Question to AI: Your clarification regarding intergovernmental and academic diffidence with regard to these possibilities is striking. It does evoke the question as to wider appreication of the challenge, as embodied since the 1990s in the interrlinked online databases of the Encyclopedia of World Problems and Human Potential. This benefitted from European Union funding of loop-related work (Ecolynx, 2000). This has embodied data from the Rio Declaration and SDGs. A report on later work is featured in the shared document (Tomáš Fülöpp, Loop Mining in the Encyclopedia of World Problems, 2015). That report notably presents data on several thousand issue chains and loops in relation to their length --as well as on the possibility of their comprehensible visualization. There is clearly a challenge in that the UN SDG data, despite its global significance, is necessarily a subset of data orginating from a wider range of concerned sources. [Show/Hide AI response]


Experimental visualization of SDG cycles

In the light of the suggestions of ChatGPT, the exercise was extended in a preliminary effort to visualize a selection of loops within the SDG thematic framework -partially inspired by the cycles featured in the many metabolic pathway maps and by previous polyhedral mappings of SDGs (Turbocharging SDGs by Activating Global Cycles in a 64-fold 3D Array, 2024). After extending the earlier selection of loops to 12 (for the purpose of the exercise), the exchange gave rise to the images below on the basis of the following tentativly proposed mnemonic articulation .

Illustrative Loop Summary by ChatGPT
Mnemonic Loop Name SDGs in loop "Nice" Mnemonic "Nasty" Mnemonic
Global Partnerships 17 → 9 → 16 Link to Last Talkshop Tangle
Climate Fragility 13 → 6 → 11 → 3 → 10 Storms of Inequality Collapse Cascade
Water-Food-Planet 6 → 2 → 12 → 13 → 15 Sustain to Remain Drain Chain
Educate-Heal-Innovate 4 → 5 → 3 → 8 → 9 Learn, Mend, Create Teach–Patch–Preach Trap
Inequality Trap 10 → 4 → 8 → 16 Fall Behind Faster Rigged Race
Smart Cities 7 → 9 → 11 → 13 → 16 Wired and Inspired Surveillance Gridlock
Blue Justice 14 → 2 → 8 → 16 → 17 Rights in Rising Tides Bluewash Blues
Learn-Well-Live-Light 4 → 3 → 12 → 13 Grow Lightly Soft Steps, Hard Fall
Food-Girls-Govern 2 → 4 → 5 → 16 Nourish to Flourish Pinkwashed Power
Innovate for Earth 9 → 7 → 13 → 15 Planet Hacks Eco-Tech Wreck
Oversight Loop (overview or meta-loop) Meta Matters Loop of Illusion
Vicious Cycle (climate–inequality–conflict) Spin of Despair Doom Loop

Mapping of 12 selected SDG loops onto a dodecahedron
(provisional adjacency and colouring)
Highlighting "nice" mnemonics Highlighting "nasty" mnemonics
Mapping of 12 selected SDG loops onto a dodecahedron with nice mnemonic Mapping of 12 selected SDG loops onto a dodecahedron with nasty mnemonic
Animations made using Stella 4D
Unfolding of mapping of 12 selected SDG loops onto a dodecahedron with "nice" mnemonics
(based on the model above left)
Unfolded in 2D Animation folding from 2D to 3D
Unfolding of mapping of 12 selected SDG loops onto a dodecahedron with nicemnemonics Unfolding from 2D to 3D of mapping of 12 selected SDG loops onto a dodecahedron with nice mnemonics
Animations made using Stella 4D

As a basis for discussion, the polyhedral mappings highlight a range of issues:

Of particular potential interest is the use of a tensegrity variant of a polyhedral configuration, as discussed separately in the light of biomimetic clues to collective resilience and unshackling knowledge (Transcending Psychosocial Polarization with Tensegrity, 2021). This suggested the following possibility.

Application of tensegrity torus approach to 16 Sustainable Development Goals
(reproduced from Matching sets of psychosocial polarities to tensegrities: case of Sustainable Development Goals? 2021)
Version 1 SDG colour coding and legend Version 2
SDGs presented on a 16-strut tensegrity torus -- colour adaptation of Burkhardt original SDGs presented on a 16-strut tensegrity torus -- colour adaptation of Burkhardt original

Question to AI: Clearly the issue of adjacency is of major significance, as with the simplifying assumption that all loops were 5-fold. It occurs to me that a tensegrity configuration would be far more realistic -but difficult to represent, even with X3D. Any comment. [Show/Hide AI response]


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