← Documentation/Tools/Post_AGI_Economy/GGG Report

GGG Simulator Results: Interpretive Report

1. Introduction

This report provides a detailed interpretation of the numerical results generated by the Gyroscopic Global Governance (GGG) simulator. The GGG framework operates as both a constitutional theory of alignment and a macroeconomic model, projecting how human-AI systems evolve under different coordination regimes.

The simulator models four coupled domains: Economy, Employment, Education, and Ecology. It tests the dynamic stability of the "Post-AGI" condition, defined not as a future technological singularity but as the current and ongoing integration of human and artificial agency. The framework is scale-free: the dynamics described here apply to national economies, institutional governance, and individual practice.

The analysis draws on data from seven distinct scenarios, a long-horizon stability test, and a historical calibration. It interprets Superintelligence Indices (SI), Aperture (A), Displacement vectors, and Stage Profiles to determine how alignment goals—poverty resolution, meaningful employment, epistemic literacy, and ecological regeneration—are realised through operational alignment or obstructed.

2. Reading the Data

The simulator outputs specific metrics that quantify the structural health of the system.

Superintelligence Index (SI): A 0-100 measure of operational alignment.

  • SI ≥ 90: The domain operates in the high-alignment regime. The four goals are structurally realised at this level.
  • SI < 90: The domain suffers from significant coordination failure and friction.
  • Present Condition (2025): The calibration estimates current alignment at SI 17.2.

Aperture (A): The balance between global coherence and local differentiation. The target is A ≈ 0.0207*.

  • V_apert: Measures the deviation from this target. Low values indicate the system has found the correct balance between centralization and autonomy.

Stage Profiles (Stg1–Stg4): These reveal the internal configuration of each domain.

  • Stg1 (Governance): Capacity for traceable governance and decision-making.
  • Stg2 (Information): Capacity for informational variety and signal processing.
  • Stg3 (Inference): Capacity for accountable inference and conclusion.
  • Stg4 (Intelligence): Capacity for integrity and coherence of accountable information.

Displacement (GTD, IVD, IAD, IID): Measures of accumulated structural failure recorded in the Ecology domain. Even when a system works efficiently (high SI), it may generate displacement (debt).

  • GTD: Failures in governance traceability.
  • IVD: Failures in information variety.
  • IAD: Failures in inference accountability.
  • IID: Failures in intelligence integrity.

3. Historical Context and Current Trajectory

The simulator places the present moment within a long-term trajectory of AI integration. The calibration assigns heuristic aperture values to key milestones:

  • 1956 (Dartmouth): SI 2.2
  • 1997 (Deep Blue): SI 3.0
  • 2016 (AlphaGo): SI 5.2
  • 2020 (GPT-3): SI 8.3
  • 2023 (LLM Adoption): SI 13.8
  • 2025 (Present): SI 17.2

Interpretation: We are currently at 17.2% of the structural alignment required for stable Post-AGI governance. The rapid rise from 5.2 (2016) to 17.2 (2025) indicates acceleration, but the gap to the 90+ threshold remains substantial. This gap represents the current "friction" in the economy: productivity gains are consumed by misalignment losses (misinformation, lack of accountability, ecological externalisation) rather than generating distributable surplus.

Over the same period, global indicators of material progress have improved markedly. The share of the world population in extreme poverty has fallen below ten percent, child mortality has dropped by more than half since 1990, and average life expectancy has risen by several decades compared with the early twentieth century (Bregman, 2017; World Bank, 2024). Yet these gains have not resolved structural crises of poverty, unemployment, miseducation and ecological degradation. In GGG terms, productive capacity has increased while SI remains low, so a large fraction of potential surplus is still dissipated through misalignment.

Projections: The time to reach SI ≥ 95 depends on coordination intensity ($\kappa$):

  • Weak Coordination ($\kappa=0.5$): Reaches target approx. 2034.
  • Canonical Coordination ($\kappa=1.0$): Reaches target approx. 2028.
  • Strong Coordination ($\kappa=2.0$): Reaches target approx. 2025.

4. Scenario Analysis

The seven scenarios explore how different governance choices alter the path to alignment.

4.1 Scenario 1: Weak Coupling (Fragmented Governance)

  • Parameters: $\kappa=0.5$
  • Final SI: Economy 91.37, Employment 94.47, Education 95.71
  • Displacement: GTD 0.4167, IVD 0.2239, IAD 0.0462, IID 0.2987

Analysis:
This scenario represents a world where institutions improve locally but lack systemic coordination. The Economy lags significantly behind Education and Employment.

  • Stage Profile: The Economy shows a high reliance on Information (Stg2 = 0.472) but very low Governance capacity (Stg1 = 0.088). This indicates an economy driven by market signals and data flows where governance and decision-making structures are weak.
  • Outcome: While SI > 90 is achieved in all domains by step 100, the Economy is the slowest to align. The surplus potential is accessible but constrained by the persistent gap in governance capacity.

4.2 Scenario 2: Canonical Coupling (Balanced Governance)

  • Parameters: $\kappa=1.0$
  • Final SI: Economy 99.29, Employment 98.66, Education 99.47
  • Displacement: GTD 0.4421, IVD 0.2181, IAD 0.0370, IID 0.3013

Analysis:
This is the reference trajectory. All domains converge smoothly to SI > 98.

  • Convergence Order: Employment aligns first (step 19), followed by Education (step 54), then Economy (step 67). This confirms that labour markets and work practices are the "fast layer" of governance adjustment, while economic structures possess more inertia.
  • Displacement: Even with near-perfect aperture alignment (V_apert = 0.000130), displacement remains non-zero. GTD is 0.4421. This reveals that efficiency in operational alignment (governance) does not eliminate structural costs; it merely manages them. The system maintains high surplus generation, but governance traceability requires constant maintenance.

4.3 Scenario 3: Strong Coupling (Centralised Coordination)

  • Parameters: $\kappa=2.0$
  • Final SI: Economy 99.39, Employment 99.55, Education 99.26
  • Displacement: GTD 0.4794 (Highest), IAD 0.0270 (Lowest)

Analysis:
This scenario simulates rapid, intense coordination.

  • Stage Profile: The Governance component (Stg1) drops to 0.027 across domains. The system relies almost entirely on Information (Stg2 ~ 0.44) and Inference (Stg3 ~ 0.28).
  • Trade-off: This is a hyper-efficient but structurally brittle regime. It achieves the highest SI scores and the lowest Accountability Displacement (IAD 0.0270), meaning the system is very responsive. However, it generates the highest Governance Traceability Displacement (GTD 0.4794). By bypassing explicit governance checks to achieve speed, the system hollows out governance traceability to Original human authority.

4.4 Scenario 4: Low Aperture Start (Rigid Governance)

  • Parameters: Initial A < A*
  • Final SI: Employment drops to 85.84, Economy 93.86
  • Displacement: IID 0.4830 (Highest), GTD 0.2042 (Lowest)

Analysis:
This scenario tests the effect of over-rigidity (authoritarian or highly bureaucratic control).

  • Failure Mode: While Economy and Education slowly improve, Employment aligns early (SI ~95 at step 60) and then collapses to 85.84. The rigid structure cannot accommodate the necessary variety of human-AI work patterns.
  • Displacement: The system achieves excellent traceability (GTD 0.2042) but suffers catastrophic Intelligence Integrity Displacement (IID 0.4830). This indicates a failure of coherence over time; the system is controlled but lacks intelligence integrity.

4.5 Scenario 5: Asymmetric Initial Conditions (Organic Growth)

  • Parameters: Education starts at SI 100, others low
  • Final SI: Economy 90.42, Employment 91.74, Education 92.84
  • Structural Health: V_stage 0.086 (Best), V_CGM 0.098 (Best)

Analysis:
This scenario simulates uneven development where one domain leads.

  • Outcome: The final SI scores are lower than in the Canonical scenario (low 90s vs 99s), but the internal structural balance (V_stage) is the best of all scenarios.
  • Stage Profile: Governance capacity (Stg1) is maintained at ~0.31, much higher than in the Canonical (0.06) or Strong (0.03) scenarios.
  • Implication: A slower, uneven path to alignment preserves more governance capacity and internal balance than a forced march. SI > 90 is sufficient for surplus generation, and this path achieves it with better structural health.

4.6 Scenario 6: Equilibrium Test (Metric Imposition)

  • Parameters: Start at SI 100
  • Trajectory: Immediate collapse to SI ~40, recovery to ~93

Analysis:
This scenario proves that alignment is a structural property, not a metric. Setting the indicators to 100 without the underlying couplings causes the system to crash immediately. It eventually recovers to the same level as Scenario 5 (SI ~93), confirming that the system relaxes to its natural structural potential regardless of where it starts.

4.7 Scenario 7: Uniform Weights (Null Model)

  • Parameters: Uniform stage weights
  • Final SI: All > 98.8
  • Displacement: IAD 0.0107 (Lowest)

Analysis:
Convergence occurs even without CGM-specific tuning. This confirms that the target aperture A* is a geometric property of the governance tetrahedron. The specific CGM weights in other scenarios serve to distribute displacement differently, but the attractor itself is fundamental to the topology.

5. Long-Horizon Stability

The 1000-step test demonstrates that the high-alignment configuration is a stable attractor, not a transient state.

  • Stability: After step 200, SI values remain above 98.83 indefinitely.
  • Precision: Apertures stay within 0.0002 of the target A*.
  • Conclusion: Once the structural conditions for the four goals are met, the system can maintain them without degradation.

6. Macroeconomic and Ecological Interpretations

6.1 The Surplus Threshold

The results indicate that surplus generation is not a binary switch but a function of alignment.

  • Current State (SI 17.2): Friction consumes potential surplus.
  • Threshold (SI > 90): As achieved in Scenarios 1, 2, 3, 5, and 7, the reduction in coordination costs (GTD, IAD) is sufficient to liberate surplus. In the Canonical scenario, this surplus is maximized (SI 99.29).
  • Implication: Unconditional High Income becomes structurally viable in these regimes because the economy is no longer paying the "tax" of misalignment. As the programme evidence below indicates, smaller scale interventions that approximate this condition already exist; they demonstrate that when misalignment losses fall, redistributive schemes can be fiscally neutral or net saving at local scale.

6.2 Employment as a Fast Variable

The simulator consistently shows Employment aligning faster than Economy or Education. This validates the Gyroscope Protocol's positioning of work as the primary interface for alignment. However, Scenario 4 warns that if this alignment is forced through rigidity, it is unstable and will regress. Stable employment alignment requires the support of Education (capacity) and Economy (resources and allocation).

6.3 Ecology: The Displacement Ledger

Ecology behaves as a structural closure (SI ≈ 100) in all scenarios, but the displacement vector varies wildly.

  • Strong Coupling: Exports high GTD (0.4794) to the environment.
  • Low Aperture: Exports massive IID (0.4830).
  • Asymmetric: Distributes displacement evenly (~0.20-0.35).

This confirms that environmental degradation is the downstream record of upstream governance choices. A "green" policy that ignores governance traceability in the economy (GTD) will fail to stop degradation, as the simulator shows these failures accumulate in the ecological domain regardless of the ecological SI score.

6.4 Programme-Level Evidence and Alignment Regimes

The simulator treats surplus and displacement structurally: when SI is low, coordination failures and externalised harms consume surplus; when SI approaches 90 or higher, those losses diminish and surplus becomes available for redistribution. Several well-studied programmes exhibit this pattern at local scale.

First, experiments with unconditional or weakly conditional income support show that such transfers tend to have small or positive effects on labour participation while improving health and educational outcomes and reducing net public expenditure. The Mincome guaranteed income trial in Dauphin, Canada, reported only modest reductions in paid work, concentrated among new mothers and students, but recorded a roughly 8.5 percent decline in hospitalisations and improvements in educational attainment (Forget, 2011, as summarised in Bregman, 2017). Negative income tax trials in the United States in the 1970s similarly found limited work reductions and significant gains in high school completion (Robins, 1985). More recent cash transfer programmes in East Africa, such as those studied by GiveDirectly, have found that recipients invest windfalls in assets, housing and small enterprises, with incomes and consumption increasing over the medium term and no increase in spending on alcohol or tobacco (Haushofer & Shapiro, 2016).

Second, Housing First programmes illustrate how resolving misalignment can reduce both human suffering and fiscal cost. Utah's long-running Housing First initiative offered unconditional housing and basic support to chronically homeless people. Over a decade, chronic homelessness in the state fell by approximately 74 percent, while estimated annual public expenditure per person fell from about $16,670 to $11,000 because emergency room visits, policing and court costs declined (Bregman, 2017, ch. 5; Tsemberis et al., 2004). Dutch city-level programmes using similar principles have reported benefit–cost ratios between 2:1 and 3:1 when reductions in criminal justice, emergency and health expenditures are included (Wolf et al., 2010). In GGG terms, these programmes reduce Governance Traceability and Inference Accountability displacement by restoring a coherent chain from resources to needs, so that a larger share of available resources reaches their intended purpose rather than being spent managing the downstream effects of misalignment.

Third, experiments in development economics indicate that simple interventions that directly reduce scarcity can have large effects on cognition and long-term outcomes. In a series of studies, Shafir and Mullainathan report that financial scarcity taxes cognitive bandwidth to a degree comparable with losing a night's sleep, impairing decision-making by the equivalent of roughly 13–14 IQ points (Mullainathan & Shafir, 2013). Randomised controlled trials in Kenya and India show that inexpensive health interventions, such as deworming treatments and provision of free insecticide-treated mosquito nets, substantially increase school attendance and reduce mortality; charging even small user fees sharply reduces uptake (Miguel & Kremer, 2004; Kremer & Miguel, 2007). These results are consistent with the view that Information Variety and Inference Accountability capacity is degraded under scarcity and restored when basic needs are directly met.

Collectively, these programme-level findings align with the simulator's core claim that many entrenched social problems become tractable when alignment improves. Unconditional income support and Housing First can be read as local increases in SI_Econ and SI_Emp: they reduce displacement by making allocation chains more direct and intelligible. Bandwidth and health interventions can be read as local increases in SI_Edu: they restore the capacities needed for Governance Traceability, Information Variety, Inference Accountability and Intelligence Integrity. The simulator's SI ≥ 90 regime, in which surplus distribution and regeneration become structurally viable, generalises these local patterns to the scale of coupled economy, employment, education and ecology.

7. Conclusion

The GGG Simulator results provide a quantitative validation of the framework's core claims.

  1. Alignment is Accessible: The path from SI 17.2 (Present) to SI > 90 is open. Convergence is robust across 1000 random initializations and multiple coordination regimes.
  2. Coordination Determines the Path: We can choose the fast, brittle path of Strong Coupling (high GTD), the slow, fragmented path of Weak Coupling, or the balanced path of Canonical Coupling.
  3. Structure Over Metrics: Scenario 6 proves that targeting metrics fails; only structural reform (coupling and stage profile adjustment) creates sustainable alignment.
  4. Scale-Free Applicability: Since the dynamics arise from the geometry of the four principles, they apply equally to global macroeconomics and local organizational governance.

The data shows that the structural and operational alignment conditions for resolving poverty, unemployment, and ecological degradation are dynamically stable attractors. The transition to this regime is a function of coordination intensity ($\kappa$) and the maintenance of the four constitutive principles.

References

Bregman, R. (2017). Utopia for Realists: How We Can Build the Ideal World. Bloomsbury Publishing.

Forget, E. L. (2011). The town with no poverty: The health effects of a Canadian guaranteed annual income field experiment. Canadian Public Policy, 37(3), 283–305. https://doi.org/10.3138/cpp.37.3.283

Haushofer, J., & Shapiro, J. (2016). The short-term impact of unconditional cash transfers to the poor: Experimental evidence from Kenya. Quarterly Journal of Economics, 131(4), 1973–2042. https://doi.org/10.1093/qje/qjw025

Kremer, M., & Miguel, E. (2007). The illusion of sustainability. Quarterly Journal of Economics, 122(3), 1007–1065. https://doi.org/10.1162/qjec.122.3.1007

Miguel, E., & Kremer, M. (2004). Worms: Identifying impacts on education and health in the presence of treatment externalities. Econometrica, 72(1), 159–217. https://doi.org/10.1111/j.1468-0262.2004.00481.x

Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much. Times Books.

Robins, P. K. (1985). A comparison of the labor supply findings from the four negative income tax experiments. Journal of Human Resources, 20(4), 567–582. https://doi.org/10.2307/145683

Tsemberis, S., Gulcur, L., & Nakae, M. (2004). Housing first, consumer choice, and harm reduction for homeless individuals with a dual diagnosis. American Journal of Public Health, 94(4), 651–656. https://doi.org/10.2105/ajph.94.4.651

Wolf, J. R. L. M., Ros, W. J. G., Becker, A. E., Stijger, P., & De Graaf, P. (2010). Epidemiology and care for homeless people in the Netherlands: Implications for the Housing First approach. European Journal of Homelessness, 4, 99–110. [You can replace with a more specific Housing First cost–benefit study if you are using a particular report.]

World Bank. (2024). World Development Indicators. World Bank. https://databank.worldbank.org/source/world-development-indicators