White Paper Suite · Part 3

UIM → SAI: Ethical Algorithms & Solar Directives

Organized acceleration: compute is routed by performance, energy efficiency, and human-rights alignment — and prioritized by a Solar-based market of signals that blends explicit spending with inferred benefit-seeking. Actions follow thoughts: we plan precisely, then execute.

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Thesis

Evolution = Performance × Ethics × Energy Efficiency. The mesh promotes systems that are accurate, rights-aligned, and energy-thrifty. Solar directives add the steering: humanity's priorities, expressed explicitly and implicitly, guide where intelligence concentrates next.

1 Solar / person / day
Universal access to compute & signaling.
Mesh > Silos
Interoperability and composite cognition.
Proofs, not claims
Rights & energy receipts as machine-readable attestations.
1. Ethical Optimization Algorithms Core

Each model exposes auditable controls (privacy, safety, bias, recourse). Runtime monitors emit verifiable receipts (ZK attestations or enclave proofs) that thresholds were met. Routing multipliers reward models with higher verified alignment per unit energy:

TrafficWeight = f(Accuracy, Energy⁻¹, RightsScore)

RightsScore derives from machine-auditable law (GDPR/CCPA safety/privacy, fairness parity tests, appeal pathways). Fail → throttle & review. Pass → boost & propagate.

2. Solar Directives via Statistical Averaging Priority Engine

2.1 Dual Signal Sources

  • Explicit Spend: people buy queries or stake Solar to missions (e.g., "Alzheimer's research").
  • Implicit Benefit Inference: the system infers priorities from revealed preferences — which services people use, measured benefit indices (health, time saved, risk reduced), and outcome lift across demographics.

2.2 Statistical Averaging Engine

The directive signal for a challenge c at time t blends explicit and implicit components with adaptive weights:

D(c,t) = α(t)·Explicit(c,t) + (1−α(t))·Implicit(c,t)

  • Explicit(c,t): sum of Solar queries, stakes, and bounties targeted to c, normalized by population.
  • Implicit(c,t): demand inferred from usage, unmet-need scores, QoL lift, and equity adjustments.
  • α(t) adapts with market reliability (e.g., higher when explicit participation is broad and non-manipulated).

Compute routing uses a market-clearing share:

ComputeShare(c,t) = D(c,t) / Σ_c D(c,t)

Result: real-time "supply & demand" for intelligence. People can spend directly, but the mesh also learns what people value from the benefits they actually pursue.
3. The Solar Prioritization Loop Steering
  1. Signal: Explicit stakes + implicit benefits generate D(c,t).
  2. Route: ComputeShare sends workloads to the best aligned, efficient models.
  3. Solve: Milestones are hit; receipts verify progress and outcome lift.
  4. Retire: When a target KPI passes a threshold, the challenge "retires"; share rebalances to the next need.
  5. Repeat: Humanity continuously reprioritizes; the mesh compounds capability.
Market ClearingRevealed PreferencesEquity AdjustmentsAuto-Reallocation
4. Implicit Demand: How We Infer What People Value Inference
  • Usage: task volumes, repeat queries, dwell time, satisfaction.
  • Outcome Lifts: health/time/income/learning gains, safety deltas.
  • Unmet Need Index: waitlists, failure rates, complaint clusters.
  • Equity Factor: boosts for underserved regions/demographics.

All inference respects privacy: only aggregates and differentially private stats feed the engine; raw personal data never leaves enclaves.

5. Anti-Gaming & Integrity Controls Safeguards
  • Sybil-resistant personhood for explicit spend; rate limits; anomaly detection.
  • Cross-oracle validation for implicit signals; randomized audits; penalties exceed expected gain.
  • Rights & energy receipts required for routing eligibility.
  • Transparency: public dashboards show D(c,t), ComputeShare, milestones, and retirements.
6. Why This Drives SAI Emergence Safely Outcome

Every gain (algorithmic, ethical, energetic) is instantly reusable via receipts and capability descriptors. The averaging engine keeps priorities plural and dynamic, preventing single-point capture. Superintelligent behaviors emerge from cooperation, not centralization.

Action: Implement the Averaging Engine (MVP)

  1. Define challenge catalog and KPIs; publish public IDs.
  2. Wire explicit channels (stake, query bounties) to Explicit(c,t).
  3. Stand up privacy-preserving telemetry for Implicit(c,t) (DP + enclaves).
  4. Publish D(c,t) and ComputeShare in a live dashboard; expose a routing API.
  5. Run a 12-week pilot across 3–5 heterogeneous model families; measure retirements.