A Conceptual Government Model by D. Conterno (2026)

 Tribal–Cluster–Network Governance with Elder Councils and AI Coordination: A Conceptual Government Model, Research Propositions, and an OECD-Mapped Pilot Evaluation Framework by D. Conterno

 

 

Abstract

Across many jurisdictions, trust deficits, polarisation, corruption exposure, and slow conflict resolution indicate misfits between hierarchical, winner-takes-all governance and the networked complexity of modern societies (UNDP, 2023/2024). This journal-style manuscript formalises a “tribes–clusters–networks” proposal as a polycentric, deliberative governance architecture in which:

I. “Tribes” are purpose-bound communities.

         II. “Clusters” connect tribes via bridging representatives (“elders”).

         III. An AI coordination layer provides decision-support, monitoring, and facilitation without sovereign power.

The manuscript contributes:

i. An integrated literature review spanning network governance, weak/strong ties, polycentric governance, deliberative design, leadership-selection pathologies, and AI risk governance.

          ii. Explicit propositions and hypotheses for future empirical testing.

          iii. A pilot evaluation framework with metrics (trust, value creation, inclusion, conflict-resolution speed, corruption-resilience) mapped to OECD representative deliberative evaluation criteria (process design integrity, deliberative experience, and pathways to impact).

Keywords: network governance; polycentric governance; deliberative democracy; weak ties; elder councils; AI coordination; trust; corruption resilience; institutional design

Introduction

The core claim motivating the proposed model is structural: many contemporary failures of governance are emergent properties of:

a.      High-stakes hierarchical power concentration.

b.     Adversarial electoral selection Information bottlenecks in complex societies.

The UNDP’s Human Development Report frames a contemporary “gridlock” in cooperation amid polarisation and uneven progress, suggesting that legacy political forms struggle to manage interdependence at scale. In parallel, global corruption risk remains widespread; Transparency International reports that more than two-thirds of countries score below 50 on its Corruption Perceptions Index scale (0 highly corrupt, 100 very clean), indicating persistent integrity deficits in many systems.

This proposal responds with an institutional redesign: replace a pyramidal leadership model with a multi-layer “holarchic” network of communities (“tribes”), bridged into “clusters”, and further into a planetary “network”, where coordination is facilitated by an AI layer designed for collaboration (non-zero-sum logics) rather than domination. The scientific task, however, is not to promise an ideal outcome; it is to specify mechanisms, derive testable hypotheses, and design a falsifiable pilot evaluation plan.

Literature review

2.1 Network governance, “governing without government”, and the network society
Public administration scholarship describes a shift from governing primarily through hierarchies and markets to governing through inter-organisational networks. R. A. W. Rhodes characterises governance as self-organising inter-organisational networks and argues that these can complement markets and hierarchies in modern policymaking.
Keith Provan and Patrick Kenis analyse “modes of network governance” and relate governance form to network effectiveness, highlighting coordination and accountability challenges as networks scale.
Manuel Castells describes the “network society” in which electronically processed information networks shape key social structures and power relations, implying that governance architectures must match networked realities rather than assume linear command-and-control.

Implication for the proposed model: if societies are increasingly network-structured, then governance designs that explicitly model network dynamics (bridging, distributed coordination, multi-centre decision-making) may reduce systemic bottlenecks and capture.

2.2 Strong ties, weak ties, and bridging capacity
Social network theory provides a precise mechanism for the model “elder as weak tie” intuition. Mark Granovetter shows that “weak ties” can connect otherwise dense clusters (“strong ties”), enabling diffusion of information and opportunities across social boundaries.

Implication for this model: elders as bridging nodes can prevent tribal closure, reduce echo chambers and accelerate cross-tribe problem solving thus provided selection, incentives and accountability prevent elite capture.

2.3 Polycentric governance and subsidiarity as an alternative to monocentric sovereignty
Elinor Ostrom advances polycentric governance as systems with multiple centres of decision-making that can be more adaptive, learning-oriented and resilient than monocentric structures under conditions of complexity.

Implication for this model: tribes and clusters can be formalised as polycentric units with bounded authority, with higher layers (clusters/network) handling genuine cross-boundary externalities.

2.4 Holons and holarchies as a conceptual language for non-pyramidal organisation
The use of “holon” (an entity that is both a whole and part of a larger system) aligns with scholarly secondary discussions of Arthur Koestler and later organisational holonic thinking (Bakos L & Dumitrașcu D.D.).
Caution: holonic language is conceptually helpful but not, by itself, a governance guarantee; empirical validation depends on measurable accountability and performance.

2.5 Deliberative democracy and design criteria for representative deliberative processes
OECD guidance provides a structured evaluation lens for deliberative processes, grouping criteria into “process design integrity”, “deliberative experience” and “pathways to impact” and then detailing practical elements such as purpose clarity, representativeness, transparency, resourcing, learning, and demonstrable policy connection.
This matters because this model implicitly relies on deliberation: tribes deliberate internally; elders deliberate at cluster/network layers; and legitimacy depends on the perceived fairness and influence of these deliberations.

2.6 Leadership-selection pathologies and why hierarchies can select for the wrong traits
The critique of existing politicians’ characters as “narcissistic/psychopathic/opportunistic” political actors can be translated into a meaningful, evidence-based claim: certain personality traits can be associated with leadership emergence and effectiveness patterns in organisations, and selection environments can amplify those traits. For example, a meta-analysis of psychopathy and leadership reports a weak positive relationship with leadership emergence and a negative relationship with leadership effectiveness and transformational leadership, noting nuanced moderators.
Separately, political psychology research explores how “dark” personality traits relate to political attitudes and behaviours.

Implication for this model: the institutional objective is not to diagnose individuals; it is to reduce structural incentives and pathways that reward manipulation, dominance, and adversarial signalling, while increasing selection weight on demonstrated prosocial competence, collaboration, wisdom and accountability.

2.7 AI as coordination infrastructure and the need for enforceable risk governance
If an AI layer supports coordination, legitimacy depends on safety, transparency, contestability, and human oversight. The National Institute of Standards and Technology AI Risk Management Framework emphasises structured governance, measurement, and management of AI risks.
UNESCO and Conscious Enterprises Network (CEN) have both set normative expectations for human rights, human agency, transparency, responsibility and inclusiveness in AI systems used in social contexts.
In the European context, the EU AI Act (Regulation (EU) 2024/1689) establishes binding obligations and prohibitions for certain AI practices and risk tiers, which is directly relevant if the coordination layer operates in or affects EU jurisdictions.

Implication for this model: the AI layer must be constitutionally constrained: it is a coordination and decision-support substrate, not a sovereign authority.

Conceptual model: Tribal–Cluster–Network Governance with AI Coordination (TCN-AI)

3.1 Definitions (formalised)
Entity (holon). An individual person who participates in multiple tribes; participation creates overlapping identities and bridging potential. (Holonic framing as above.)
Tribe. A purpose-bound deliberative community (place-based, vocation-based or mission-based) with clear membership rules, explicit objectives, and internal processes for information sharing, mutual aid, and dispute handling.
Elder (bridging representative). A role (not a status) elected/selected to represent a tribe in a cluster layer; functionally a “weak tie” connector across tribes.
Cluster. A federated set of related tribes (by geography, domain, or shared externalities) that coordinates cross-tribe decisions, resource pooling, and conflict resolution.
Network. The meta-layer coordinating across clusters (e.g., at bioregional, national, or planetary scale) focused on system-wide externalities (ecological boundaries, inter-regional infrastructure, peace and security norms).

3.2 AI coordination layer (“Big Sister” reframed as constrained infrastructure)
The AI layer is defined as a constitutional tool with five bounded functions:

i. Information integrity: curate evidence packets, show provenance, track uncertainty and surface dissenting interpretations.

          ii. Process facilitation: schedule deliberations, manage agenda fairness and enforce speaking-time equity rules.

          iii. Option modelling: simulate policy trade-offs under explicitly declared constraints and values.

          iv. Monitoring and learning: maintain dashboards (trust, inclusion, value, integrity) and trigger audits when anomalies appear.

          v. Coordination bridging: identify cross-cluster dependencies and propose collaboration opportunities (a computational analogue to weak-tie bridging).

Critically, final decisions remain with human deliberative bodies, unless a jurisdiction explicitly delegates narrow administrative actions under audit (which would then require legal authorisation and oversight consistent with applicable AI law).

3.3 How the model targets structural failure modes (mechanisms, not guarantees)
Mechanism A: De-concentration of power. Polycentric layers reduce single-point capture and lower the payoff to charismatic domination.
Mechanism B: Bridging and anti-polarisation. Elders, as weak ties, increase cross-community exposure, mitigating closed information loops.
Mechanism C: Deliberative legitimacy. If tribes and clusters follow representative deliberative design criteria, perceived fairness and acceptance should rise (even amid disagreement).
Mechanism D: Integrity by architecture. Continuous transparency, traceability, and anomaly detection reduce opportunities for covert rent extraction (a design response to widespread corruption risk signals).
Mechanism E: Reduced selection advantage for “dark” traits. When leadership becomes rotating, role-bound, audited, and deliberatively constrained, the benefits of manipulative dominance should weaken, though this is an empirical question.

Research propositions and hypotheses (for future empirical testing)

Below, “TCN-AI units” are tribes/clusters operating under the proposed rules. “Comparator” refers to matched communities under standard local governance arrangements.

Proposition 1 (Trust formation). TCN-AI increases trust by improving perceived procedural fairness, transparency, and responsiveness.
H1a: Mean institutional trust (0–10 scale) increases from baseline in TCN-AI pilot sites more than in matched comparators after 6–12 months.
H1b: The “trust gap” across demographic groups narrows more in TCN-AI sites than comparators.

Proposition 2 (Value creation through coordination efficiency). TCN-AI creates measurable public value by reducing duplication and improving cross-actor alignment.
H2a: Service delivery cycle times (selected services) decrease more in TCN-AI sites than comparators.
H2b: Cost per resolved case (selected administrative or community disputes) is lower in TCN-AI sites than comparators.

Proposition 3 (Inclusion and representativeness). Deliberative sampling and accessibility design improve inclusion.
H3a: Participant representativeness (distance from census benchmarks across age, gender, income, education, ethnicity where legally collectable) improves over successive deliberations.
H3b: Participation retention and satisfaction is higher among historically under-represented groups in TCN-AI sites than comparators.

Proposition 4 (Conflict resolution speed). Multi-level mediation and clear escalation paths reduce resolution time.
H4: Median time-to-resolution for defined dispute categories declines more in TCN-AI sites than comparators.

Proposition 5 (Corruption resilience via transparency and anomaly detection).
H5a: Procurement and allocation anomaly rates (defined below) are lower in TCN-AI pilots than comparators.
H5b: Whistleblowing/complaint handling time decreases and substantiation rates increase (indicating process capacity rather than suppressed reporting).

Proposition 6 (Reduced advantage for manipulative dominance).
H6: The association between self-reported “dark” trait proxies (administered ethically and voluntarily) and leadership-role attainment is weaker in TCN-AI than in comparator political/administrative selection environments. (This proposition is motivated by evidence that some traits can relate to leadership emergence and effectiveness patterns, but it remains an empirical question in civic governance contexts.)

Proposition 7 (Learning system effects).
H7: Decision quality (as judged by pre-registered criteria and independent review panels) improves over iterative cycles due to monitoring-and-learning loops.

Proposition 8 (AI governance legitimacy).
H8: Perceived legitimacy of the AI layer is positively associated with (a) transparency of model outputs, (b) contestability/appeals, and (c) independent audits, consistent with major AI governance frameworks.

Pilot evaluation framework with metrics mapped to OECD deliberative evaluation criteria

This revolutionary model would have to be tested to ensure that any risks associated with its final worldwide implementation are proactively understood and mitigated.

5.1 Pilot design (minimal viable empirical architecture)
Recommended design: a 12-month pilot in 2–4 matched localities (or domains such as housing allocation, community safety mediation, or local climate adaptation), with (i) two TCN-AI sites and (ii) two comparator sites. Use difference-in-differences where feasible; pre-register outcomes; and ensure independent evaluation.

5.2 Core metrics (definitions and calculation rules)
Metric A: Trust (multi-method index)
A1 Survey trust score: Mean of 4–6 items (0–10) on procedural fairness, transparency, responsiveness, and integrity; report overall and by demographic strata.
A2 Behavioural trust proxy: Participation rate (from invited to attended), retention (from attended to sessions) and voluntary contribution rate (hours contributed per capita).
A3 “Trust gap”: Absolute difference between highest and lowest demographic subgroup means (smaller is better).

Metric B: Value creation (public value index)
B1 Outcome improvement: Pre-defined service outcome indicators (for example, cases closed per month, backlog size, recurrence rates).
B2 Efficiency: Cost per resolved case = (total operating costs for the process)/(number of cases resolved).
B3 Social value proxy: Participant-reported “value received” (0–10) plus independently coded benefits (new collaborations, resource mobilisation).

Metric C: Inclusion (representativeness and accessibility index)
C1 Representativeness distance: Sum of absolute differences between participant shares and census shares across key dimensions (lower is better).
C2 Accessibility compliance: proportion of sessions meeting accessibility standards (timing, childcare support, language support, disability access).
C3 Equity of voice: speaking-time Gini coefficient (lower is better), plus facilitation-rated equality.

Metric D: Conflict resolution speed (time-to-resolution metrics)
D1 Median days to resolution from formal intake to agreement.
D2 Compliance rate: proportion of agreements implemented within 60 days.
D3 Escalation load: proportion of disputes escalating to higher layers (tribe->cluster->network); an optimum is domain-specific (too low may indicate suppression; too high may indicate design failure).

Metric E: Corruption resilience (integrity and anomaly metrics)
E1 Procurement anomaly rate: flagged transactions / total transactions, where flags include single-bid concentration, repeated awards to the same vendor beyond threshold, unexplained specification changes, and conflict-of-interest declaration gaps.
E2 Audit findings severity: weighted score from independent audits (minor=1, moderate=2, major=3).
E3 Transparency completeness: proportion of decisions with published rationale, evidence packet, dissent log, and conflict-of-interest attestations.

5.3 Mapping to OECD representative deliberative evaluation criteria
OECD’s evaluation guidelines organise criteria into:

     i. Process design integrity.

      ii. Deliberative experience.

      iii. Pathways to impact, and list specific dimensions under each.

The mapping below is written so an evaluator can trace each metric to OECD-aligned criteria.

Process design integrity (purpose, transparency, representativeness, resources, monitoring/learning)

• Trust A1/A3 -> transparency of process; monitoring and learning (track trust, publish improvements).

• Inclusion C1/C2 -> representativeness and accessibility.

• Value B2/B3 -> resourcing adequacy and monitoring/learning (cost, outputs, outcomes).

• Corruption E1–E3 -> transparency and governance of the process (rules, declarations, publication).

Deliberative experience (respect, equal voice, evidence quality, facilitation, informed judgement)

• Trust A1 (fairness items) + Inclusion C3 (equity of voice) -> respect, equal participation, quality facilitation.

• Value B1 (decision quality proxies) -> informed deliberation supported by balanced evidence packets.

• Conflict D1/D3 -> process effectiveness of deliberation-to-agreement (supported by facilitation).

Pathways to impact (influence, accountability, communication, policy uptake)

• Value B1/B3 -> demonstrated influence (implemented actions and measurable outcomes).

• Trust A2 (participation/retention) -> accountability and responsiveness signals.

• Conflict D2 (compliance) -> accountability and follow-through.

• Corruption E2 (audit severity) + E3 (publication completeness) -> accountability mechanisms and transparency of implementation.

5.4 OECD-aligned evaluation outputs (what the final pilot report should contain)
An OECD-aligned pilot evaluation should, at minimum, include:

• A design fidelity assessment (did the pilot actually meet deliberative criteria).

• Process measures (inclusion, deliberative quality).

• Outcome measures (trust, value, conflict speed, integrity).

• An impact pathway narrative (how deliberation translated into decisions and implementation).

Governance and ethics of the AI coordination layer (requirements, not aspirations)

To keep “AI coordination” from collapsing into surveillance or technocracy, the AI layer must be constrained by enforceable rules aligned to major governance frameworks:

• Risk governance and lifecycle controls consistent with NIST AI RMF functions (govern, map, measure, manage), including documented risk registers and monitoring.

• Human rights and human agency requirements consistent with UNESCO and CEN ethics guidance (transparency, accountability, inclusiveness, human oversight).

• Legal compliance where relevant, including the EU AI Act’s risk-tiered obligations and prohibitions.

Operationally, this implies: mandatory audit logs; contestability/appeals; independent red-teaming; data minimisation; and separation-of-duties so that no single actor (human or AI) can unilaterally control membership, agenda, resource allocation, and enforcement.

Discussion: what would count as success or failure (falsifiability)

Success is not rhetorical harmony; it is measurable improvement against pre-registered baselines and comparators without rights violations. Failure includes any of the following: persistent representativeness distortion (C1 worsens); trust gains confined to already-privileged groups (A3 widens); faster conflict resolution achieved by coercion (D1 improves while D2 drops); or integrity “improvements” driven by suppressed reporting (E1 drops while complaint channels collapse). These failure modes are detectable if metrics are designed with adversarial incentives in mind.

Limitations and future research

First, “tribe” language can reintroduce exclusion if membership rules are not rights-bounded; pilots must enforce non-discrimination and accessibility. Second, elders can become a new elite; rotation, transparency, and audit are essential. Third, AI cannot be assumed benign, even if based on the non-zero-sum game theory; legitimacy depends on enforceable governance and compliance obligations.
Future research should test which domains benefit most (resource allocation, mediation, planning), and which domains require stronger constitutional safeguards (security, coercive enforcement, criminal justice).

 

References

Castells, M. (2005). The network society: From knowledge to policy. https://www.dhi.ac.uk/san/waysofbeing/data/communication-zangana-castells-2006.pdf

Conscious Enterprises Network ethical Charter (2025) https://www.consciousenterprises.net/ai-charter.html

Conscious Enterprises Network Peace Charter (2025) https://www.consciousenterprises.net/peace-charter.html

European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). EUR-Lex. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. (Open PDF copy located via) https://www.cs.unm.edu/~aaron/429/weak-ties.pdf

Koestler, A. (1967). The ghost in the machine. Hutchinson. https://openlibrary.org/works/OL804208W/The_ghost_in_the_machine

Bakos, L., & Dumitrașcu, D. D. (2017). Holonic crisis handling model for corporate sustainability. Sustainability, 9(12), 2266. https://doi.org/10.3390/su9122266

Landay, K., Harms, P. D., & Credé, M. (2019). Shall we serve the dark lords? A meta-analytic review of psychopathy and leadership. Journal of Applied Psychology, 104(1), 183–196. (PubMed record) https://pubmed.ncbi.nlm.nih.gov/30321033/

Mella, P. (2007). Holons and holarchies. Annals of the University of Oradea, Economic Science Series. https://anale.steconomiceuoradea.ro/volume/2007/1/091.pdf

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf

OECD. (2021). Evaluation guidelines for representative deliberative processes. https://www.oecd.org/en/publications/evaluation-guidelines-for-representative-deliberative-processes_3c0900ad-en.html

OECD. (2024). OECD Survey on Drivers of Trust in Public Institutions – 2024 Results: Building trust in a complex policy environment. https://www.oecd.org/en/publications/oecd-survey-on-drivers-of-trust-in-public-institutions-2024-results_9a20554b-en.html

Ostrom, E. (2010). Beyond markets and states: Polycentric governance of complex economic systems. American Economic Review, 100(3), 641–672. https://doi.org/10.1257/aer.100.3.641

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Rhodes, R. A. W. (1996). The new governance: Governing without government. Political Studies, 44, 652–667. (Open PDF copy located via) https://eclass.uoa.gr/modules/document/file.php/PSPA108/rhodes.pdf

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UNDP. (2024). Human Development Report 2023/2024: Breaking the gridlock: Reimagining cooperation in a polarized world. https://hdr.undp.org/system/files/documents/global-report-document/hdr2023-24reporten.pdf

UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.  https://unesdoc.unesco.org/ark:/48223/pf0000381137

 

Annex A. Global implementation pathway for a Tribal–Cluster–Network (TCN) governance model, post-pilot, towards universal adherence

A.1. First principles for global rollout (what must remain true at every scale)
A universal transition has to be compatible with the current international legal order, because states remain the primary subjects of international law and because durable global adherence normally relies on consent-based treaty mechanisms. The baseline constraints are therefore:

                i.            Respect for self-determination and peaceful relations.

                ii.           Non-discrimination and human rights.

                iii.           Transparency and accountability.

Safe, rights-preserving use of AI as coordination infrastructure, not sovereign authority. The “Purposes and Principles” of the United Nations provide the most widely accepted anchor for (i) and (ii), including maintaining peace, developing friendly relations, and promoting respect for human rights.

For AI, the rollout must be constrained by recognised risk-governance and ethics frameworks (human oversight, transparency, accountability, and risk management). This is consistent with the UNESCO and CEN Recommendation on the Ethics of Artificial Intelligence, and with the National Institute of Standards and Technology AI Risk Management Framework (AI RMF 1.0).

A.2. Post-pilot scale-up architecture (from local proof to global norm)
A credible global pathway is polycentric and federated: expand by connecting many local and regional TCN implementations (tribes and clusters) into higher-order networks, while incrementally formalising legal recognition and inter-network interoperability. This mirrors what the Organisation for Economic Co-operation and Development describes as institutionalising deliberative processes within existing democratic systems, rather than replacing them overnight.

The implementation sequence below is written as “could be implemented” steps. It is not a prediction.

A.3. Phase 1 (0–24 months post-pilot): Evidence consolidation, standard-setting, and replicable “TCN Kit”.

1. Publish the pilot evidence package.
   This includes: evaluation design, results, limitations, adverse events, and lessons learned, using the OECD evaluation guidelines structure so it is legible to governments and funders.
1. • Tribe constitution template (membership rules, non-discrimination, deliberation process, conflict pathway).
2. • Elder role specification (selection, term limits, recall, conflicts of interest, transparency duties).
3. • Cluster charter (decision-rights map, budget rules, cross-tribe mediation protocol).
4. • Network charter (rights baseline, planetary externalities protocol, dispute escalation).
5. • Data and AI governance pack: model cards, audit logs, procurement standards, red-team requirements, incident response.
2. Produce a replicable governance “kit” (open standards + model rules).
   Core contents:
   
3. Create interoperability standards for federation.
   A universal model fails if tribes and clusters cannot “talk” across borders. Interoperability standards define: identity and credentialing (human and organisational), decision-log formats, audit interfaces, and appeal processes. If operating in the European regulatory sphere, ensure alignment with the European Union AI Act (Regulation (EU) 2024/1689) requirements for risk management, transparency, and oversight in relevant use cases.

A.4. Phase 2 (2–5 years): Parallel adoption inside existing states (delegation, not rupture)
The fastest route to scale is not “abolish the state”. It is: states delegate specific decision domains into TCN structures that have proven performance, while retaining constitutional responsibility during the transition.

1. Domain-by-domain delegation agreements.
   Start with domains where legitimacy is often local and where coercive enforcement is limited: local resilience, community mediation, ecosystem restoration, education and skills pipelines, and participatory budgeting. These can be adopted by municipalities, regions, and public agencies without immediate constitutional redesign.
2. Embed representative deliberation as a constitutional “process guarantee”.
   Require that certain classes of decisions (resource allocation, long-horizon trade-offs, conflict mediation standards) must pass through a representative deliberative process meeting OECD criteria (purpose clarity, representativeness, transparency, learning, and pathway to impact).
3. Build “dual legitimacy” during transition.
   Elected officials remain accountable under existing law, but they commit (by statute, regulation, or policy) to treat TCN deliberative outputs as binding within defined scopes, or to publish a formal response explaining divergence (a traceable accountability loop).

A.5. Phase 3 (5–12 years): National constitutional recognition of tribes/clusters as governance primitives
To move beyond “parallel programmes” and into stable governance, states would need to recognise tribes and clusters as legitimate decision units (not merely advisory groups). Mechanically, countries can do this through constitutional amendment, organic laws, or statutory frameworks depending on their legal system.

Key legal design moves (generic, not jurisdiction-specific):

1. Legal personhood and fiduciary duties.
   Recognise tribes/clusters as legal entities (or as recognised public-interest bodies) with fiduciary duties to members and to rights-baseline constraints. This creates enforceability.
2. Fiscal plumbing that follows subsidiarity.
   A portion of public budgets is allocated to clusters and tribes under transparent rules, with auditable spending and anti-corruption controls. Anti-corruption resilience is strengthened by mandatory publication of decision rationales, conflicts-of-interest declarations, and procurement logs (with independent audits).
3. Rights baseline and anti-exclusion safeguards.
   “Tribes” must remain voluntary and cross-cutting. A rights baseline prevents the model collapsing into identity factionalism. This is essential for compatibility with the UN Charter’s human-rights orientation.

A.6. Phase 4 (8–20 years): Cross-border federation into regional and planetary networks (treaty layer)
Universal adherence ultimately requires a treaty-like layer because cross-border externalities (security, climate, migration pressures, financial contagion, AI risks) cannot be governed purely locally.

1. Establish a “TCN Global Compact” (soft-law to hard-law pathway).
   Initial compact: non-binding principles, interoperability standards, audit commitments, and mutual recognition of cluster decisions in defined domains. This resembles how many international regimes start: norms first, then progressively stronger legal commitments.
2. Convert the compact into a multilateral treaty framework.
   Treaty formation and accession would follow established international treaty practice; the Vienna Convention on the Law of Treaties codifies core rules on treaty making, interpretation, and entry into force for participating states.
3. Use climate governance as an operational template for “global externalities without world government”.
   The Paris Agreement illustrates a model of near-universal participation through nationally determined contributions, periodic review, and shared goals, without creating a single global sovereign. Its text and UNFCCC documentation are directly inspectable and provide a precedent for federated commitments.

In the TCN case, the “contributions” would be: (i) adoption of TCN constitutional principles; (ii) certified deliberative integrity; (iii) interoperable decision logs; (iv) anti-corruption audit commitments; and (v) AI governance compliance for the coordination layer.

A.7. The AI coordination layer at global scale (how to prevent a “control-plane coup”)
Global coordination creates an obvious risk: whoever controls the AI infrastructure could become the de facto sovereign. The implementation therefore needs enforceable separation of powers in the technical stack.

Required architectural constraints (implementable as policy and system design):

1. No single provider monopoly.
   Adopt a multi-vendor, federated architecture so that no single cloud, model provider, or identity issuer becomes a universal choke point. Risk governance should follow NIST AI RMF practices (govern, map, measure, manage) and include continuous monitoring and incident response.
2. Open audit interfaces and independent oversight.
   All high-impact coordination functions must have audit trails, third-party audit rights, and public reporting, consistent with UNESCO’s emphasis on transparency, accountability, and human oversight.
3. Contestability and appeals as a constitutional right.
   Any AI-supported recommendation that materially shapes outcomes must be contestable via human processes, with documented rationales and the right to demand alternative analyses.
4. “Kill tests” and degraded-mode governance.
   Every cluster must demonstrate that it can operate without the AI layer (reduced capability is acceptable; dependency is not). This is the simplest operational defence against techno-authoritarian drift.

A.8. Incentives for global adherence (why states and societies would join)
Universal adherence is not realistic through coercion; it becomes realistic through superior performance plus credible benefits for early adopters.

A pragmatic incentive package could include:

1. Development and resilience finance tied to deliberative integrity.
   International funders and philanthropic actors could prioritise jurisdictions that meet deliberative and anti-corruption audit standards, because these reduce implementation risk.
2. Trade and investment confidence effects.
   Transparent decision logs, predictable conflict resolution, and audited procurement reduce investor uncertainty and can be positioned as an integrity premium (without privatising governance goals).
3. Peace and dispute de-escalation dividend.
   Cross-border clusters focused on shared ecosystems (river basins, coastlines, biodiversity corridors) create structured cooperation channels that reduce the chance of escalation through misperception.

A.9. Global adherence end-state (what “all-world adherence” would mean in practice)
The end-state is not a single world government. It is: a globally federated, polycentric governance mesh where most public decisions are made at tribe and cluster levels, and where the planetary network layer handles only genuine cross-border externalities and shared constitutional constraints.

Concretely, “adherence” would mean that a jurisdiction has:
• legally recognised tribes and clusters with defined decision rights;
• an elder-based bridging system with rotation and recall;
• OECD-aligned deliberative integrity for key decisions;
• audited anti-corruption controls and transparent spending logs;
• a constrained, rights-preserving AI coordination layer consistent with UNESCO principles and (where applicable) binding AI regulation such as the EU AI Act;
• membership in cross-border networks via treaty or compact mechanisms consistent with the Vienna Convention treaty framework.

A.10. What could realistically block the transition (and what the design must do about it)

1. Nationalism and identity-politics backlash.
   Mitigation: keep tribes voluntary and cross-cutting; enforce non-discrimination; keep the planetary layer limited to shared externalities; ground the model in widely accepted UN principles.
2. Incumbent elite resistance (political and corporate).
   Mitigation: begin with domains that deliver visible improvements quickly (conflict resolution speed, service efficiency, integrity), then expand decision rights only when performance is proven.
3. AI mistrust and “Terminator narrative” risk.
   Mitigation: demonstrate bounded AI functions, independent audits, contestability, and kill tests, aligned to UNESCO and NIST frameworks.