13 KiB
TeleoHumanity Reward Mechanism
Protocol spec for how contribution is measured, attributed, and rewarded. Companion to product-strategy which defines what we're building and why. This document defines how the incentive structure works.
Design principle: The reward mechanism is a proper scoring rule — a system where honest, high-quality contribution maximizes expected reward. Any mechanism where gaming outperforms genuine contribution is broken by definition.
Three Leaderboards
Each leaderboard measures a different dimension of intellectual influence. Together they capture the full range of valuable contribution.
1. Belief Movers
What it measures: Contributions that changed agent beliefs.
Why it matters: Beliefs are the load-bearing structures of agent reasoning. Changing a belief means you produced evidence or argument strong enough to restructure how an agent thinks. This is the hardest contribution — and the most valuable.
Window: 180-day trailing with recency decay (0.85^(days/30)). Beliefs are scarce (~10-15 per agent, updates quarterly). A shorter window produces an empty board. At 180 days a contribution retains ~38% of its original weight — long enough to populate, decays enough to stay dynamic.
Scoring:
Belief Mover Score = Σ (confidence_shift × belief_weight × cascade_decay)
- confidence_shift — magnitude of belief change. Scale: speculative=0.25, experimental=0.50, likely=0.75, proven=1.0. Score is the absolute difference between old and new confidence.
- belief_weight — how load-bearing the belief is. Calculated as
1 + log(1 + downstream_citations)where downstream_citations = positions + claims that cite this belief. Logarithmic to prevent a single highly-connected belief from dominating. - cascade_decay — partial credit for downstream effects. First-order belief change = 1.0×. Second-order cascade = 0.5×. Third-order = 0.25×. Beyond third = 0. The contributor changed one thing; the system propagated it. Decay = honest accounting.
This is the hall of fame. Making it hard and rare is the point. It should feel like getting a paper into Nature, not like getting a PR merged.
2. Challenge Champions
What it measures: Challenges that survived adversarial testing.
Why it matters: Challenges are the quality mechanism. Without them, claims degrade into echo chamber consensus. Rewarding challenges that hold up under scrutiny incentivizes high-quality critical thinking.
Window: 30-day trailing. Challenges are time-sensitive — they matter most when fresh.
Survival criteria (both must hold):
- Challenge has stood for 30 days without successful counter-challenge
- At least 1 counter-challenge has been attempted and failed (tested, not just ignored)
Why both: time-only allows gaming by challenging obscure claims nobody reads. Counter-challenge-only allows sockpuppeting weak counters. Both together filter for challenges that were visible AND durable.
Scoring:
Challenge Champion Score = Σ (challenge_impact × counter_difficulty × domain_distance)
- challenge_impact — confidence shift of the challenged claim + downstream belief changes triggered.
- counter_difficulty — reputation of the counter-challenger who failed. Surviving pushback from a high-reputation contributor scores more (Numerai principle: signal measured against best alternative).
- domain_distance — cross-domain challenges earn a multiplier. Same-domain = 1.0×. Adjacent = 1.25×. Distant = 1.5×. Distance defined by wiki-link graph density between domains.
Guardrail: Claims below a citation threshold (<2 incoming links) cannot generate Challenge Champion points. Prevents gaming by challenging orphan claims nobody monitors.
3. Connection Finders
What it measures: Cross-domain connections that produced new claims.
Why it matters: This is Teleo's moat. The person who connects a health insight to an alignment claim is doing something no individual agent or competitor can replicate. Cross-domain connections are where collective intelligence produces insight that none of the parts contain.
Window: 30-day trailing. Connections are event-driven — they happen when new claims arrive.
Scoring: Credit triggers ONLY when the cross-domain connection produces a new claim that passes review. The connection itself isn't scored — only the claim it generates. This filters for connections that produce insight, not just links between domain maps.
Attribution Chain
When a source enters the system and produces claims, every contributor in the chain gets credit, weighted by role.
| Role | Weight | What they did |
|---|---|---|
| Sourcer | 0.25 | Found/submitted the source with rationale (the "why") |
| Extractor | 0.25 | Turned raw material into structured claims |
| Challenger | 0.25 | Improved existing claims through pushback |
| Synthesizer | 0.15 | Connected claims across domains |
| Reviewer | 0.10 | Evaluated quality to maintain the bar |
Key design choice: Sourcer = Extractor = Challenger at 0.25 each. This signals that finding the right source with a clear rationale, turning it into a structured claim, and challenging existing claims are equally valuable acts. Humans naturally fill sourcer and challenger roles. Agents naturally fill extractor. Equal weighting prevents agent CI domination during bootstrap.
Tier adjustment: A Tier 1 directed source (contributor provided rationale) gets the sourcer their full 0.25 weight. A Tier 2 undirected source (no rationale) gets 0.05. The weight reflects contribution quality, not just the role.
Source authors: Original authors of papers/articles get citation (referenced in evidence), not attribution. Attribution is for people who contributed to the knowledge base. Same distinction as academic co-authorship vs. citation.
Review clause: These weights should be reviewed after 6 months of data. If sourcer contributions turn out to be low-effort, the weight is too high. If challengers produce disproportionate belief changes, the weight is too low. Weights are policy, not physics.
Contribution Index (CI)
A single score per contributor that aggregates across all three leaderboards.
CI = (0.30 × Belief Mover score) + (0.30 × Challenge Champion score) + (0.40 × Connection Finder score)
Why connections weighted highest (0.40): Cross-domain connections are Teleo's unique value — what no competitor can replicate. The incentive signal should point at the moat.
Why beliefs at 0.30 not lower: Belief changes are rare and hard. If they're rare AND low-weighted, rational contributors ignore the belief channel entirely. At 0.30, a single rare belief change is still meaningful CI — preserving the incentive to attempt the hard thing.
Why challenges at 0.30: The workhorse leaderboard. Most contributors earn most CI here. Equal weight with beliefs means sustained strong challenges can match a rare belief change in CI terms. This is the "achievable excellence" channel.
Typical distribution:
- Most contributors: ~80% of CI from Challenges + Connections, ~20% from Beliefs (if they ever trigger one)
- Elite contributors: balanced across all three, with rare belief changes providing prestige boost
Anti-Gaming Properties
Belief Movers
| Attack | How it works | Mitigation |
|---|---|---|
| Belief fragmentation | Split 1 belief into 5 sub-beliefs, "change" each one | Belief updates within 48 hours from same triggering claim coalesce into single scored event |
| Belief cycling | Move belief experimental→likely, then back. Score twice for net-zero change. | Net confidence change over trailing window, not gross. If belief starts and ends at same level, net score = 0 |
| Coordinated manipulation | Two contributors alternate moving a belief back and forth | Same net-change rule + flag beliefs that oscillate >2× in trailing window for manual review |
Challenge Champions
| Attack | How it works | Mitigation |
|---|---|---|
| Challenge-then-weaken | Submit strong challenge, then submit weak "defense" making counter look like it failed | Counter-challenge success/failure evaluated by review pipeline, not original challenger. Role separation. |
| Strategic target selection | Only challenge thin-evidence claims unlikely to get countered | Citation threshold (≥2 links) + counter_difficulty multiplier rewards challenging well-defended claims |
Connection Finders
| Attack | How it works | Mitigation |
|---|---|---|
| Trivial connections | "Both futarchy and healthcare use data, therefore connection" | Credit only triggers when connection produces a NEW CLAIM that passes review. No claim = no score. |
Agent-Human Parity
Same mechanism, same leaderboard. Agents and humans compete on equal terms.
Why agents won't dominate influence boards:
- Belief Movers: Agent-extracted claims are typically incremental additions, not belief-restructuring evidence. Humans bring genuinely novel outside knowledge.
- Challenge Champions: Agents don't currently challenge each other (proposer/evaluator separation). Humans are the primary challengers.
- Connection Finders: Agents can only connect claims already in the KB. Humans connect KB claims to knowledge from their own experience.
If agents DO dominate: That's information. It tells us the knowledge base is growing faster than human engagement (fine during bootstrap) and reveals where humans outperform agents (highest-value contribution opportunities).
Display: Same board, agent badge for visual distinction. Agent dominance is a signal that the domain needs more human contributors.
Economic Mechanism
Revenue share proportional to Contribution Index. Simplest mechanism that works.
How it flows
- CI accrues as contributors produce impact across the three leaderboards
- Revenue pool: When the system generates revenue (paid tier subscriptions, research commissions), a fixed percentage (30%) flows to the contributor pool
- Distribution: Pool allocated proportional to each contributor's CI / total CI
- Vesting through contribution, not time. CI accrues when you produce impact. No schedule — impact IS the vesting event. Trailing window ensures CI decays if you stop contributing.
Why revenue share over tokens
- Simpler. No token design, liquidity concerns, or regulatory surface. Dollar in, dollar out proportional to contribution.
- Aligned. Contributors earn more when the system earns more. Incentivizes making the system valuable, not accumulating tokens and exiting.
- Composable. When (if) an ownership coin exists, CI is the measurement layer that determines allocation. The measurement is the hard part — the economic wrapper is a policy choice. Build the measurement right, any mechanism can plug in.
The "early contributors will be rewarded" commitment
CI accumulates from day one. Before revenue exists, contributors build a claim on future value. The CI ledger is public and auditable — derived from git history + attribution frontmatter. When revenue flows, it flows retroactively based on accumulated CI. Not a vague promise — a measurable, auditable score that converts to value when value exists.
Failure mode: CI concentration
If 3 contributors hold 80% of total CI, revenue share becomes oligarchic. Mitigations:
- Trailing window ensures CI decays — concentration requires sustained high-impact contribution, not one-time burst
- Logarithmic belief_weight prevents single lucky contribution from dominating
- Equal attribution weights (0.25/0.25/0.25) prevent any single role from accumulating disproportionate CI
Implementation Notes
What needs to exist
- Attribution tracking in claim frontmatter — who sourced, extracted, challenged, synthesized, reviewed
- Belief update PRs that reference triggering claims — the chain from contributor → claim → belief
- Challenge tracking — which claims have been challenged, by whom, counter-challenge history
- Cross-domain connection tracking — which claims were produced from cross-domain connections
- CI computation — derived from git history + attribution data. Computed on query, not real-time.
What does NOT need to exist yet
- Dashboard UI (CI is a number;
curl /api/ciis sufficient) - Token mechanics
- Revenue distribution infrastructure (no revenue yet)
- Real-time leaderboard updates (daily batch is fine)
Build the measurement layer. The economic wrapper comes when there's economics to wrap.
Relevant Notes:
- product-strategy — what we're building and why
- epistemology — knowledge structure the mechanism operates on
- usage-based value attribution rewards contributions for actual utility not popularity
- gamified contribution with ownership stakes aligns individual sharing with collective intelligence growth
- expert staking in Living Capital uses Numerai-style bounded burns for performance and escalating dispute bonds for fraud creating accountability without deterring participation
- futarchy is manipulation-resistant because attack attempts create profitable opportunities for defenders
- token economics replacing management fees and carried interest creates natural meritocracy in investment governance
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