Asi multiversal-finance
Multiversal Finance: Prediction Markets for Interesting Observations
git clone https://github.com/plurigrid/asi
T=$(mktemp -d) && git clone --depth=1 https://github.com/plurigrid/asi "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/multiversal-finance" ~/.claude/skills/plurigrid-asi-multiversal-finance-b8c235 && rm -rf "$T"
skills/multiversal-finance/SKILL.mdMultiversal Finance: Prediction Markets for Interesting Observations
Trit: +1 (PLUS - generative, creates value from attention) Color: #E7B367 (Agent-O-Rama stream, seed 1069) Source: color_at determinism + Schmidhuber compression progress
Core Principle
Nothing is stored, everything is bet.
Predictions are bets on which
(seed, index) paths yield "interesting" observations.
Rewards flow to observers who correctly predict or discover surprising patterns.
Interestingness Metric (Compression Progress)
Following Schmidhuber's curiosity-driven learning:
interestingness(observation) = ΔC = C_before - C_after
Where
C = minimum description length of the observer's world model.
High interestingness: observation that compresses the model (reveals structure). Low interestingness: observation already predictable (no learning).
ACSet Schema: MultiversalMarket
@present SchMultiversalMarket(FreeSchema) begin # Objects Observation::Ob # A (seed, index) → color witness Bet::Ob # Prediction on future observation Agent::Ob # Goblin: -1, 0, or +1 # Morphisms observes::Hom(Agent, Observation) # who witnessed predicts::Hom(Bet, Observation) # what was predicted settles::Hom(Bet, Agent) # who settles (Coordinator) # Attributes SeedType::AttrType IndexType::AttrType HexType::AttrType TritType::AttrType RewardType::AttrType seed::Attr(Observation, SeedType) index::Attr(Observation, IndexType) color::Attr(Observation, HexType) agent_trit::Attr(Agent, TritType) # -1, 0, +1 stake::Attr(Bet, RewardType) payout::Attr(Bet, RewardType) # Interestingness score compression_delta::Attr(Observation, RewardType) end
Goblin Roles in the Market
| Goblin | Trit | Market Role | Action |
|---|---|---|---|
| Agent-O-Rama | +1 | Proposer | Generates predictions, stakes bets |
| Coordinator | 0 | Settlement | Verifies , transfers rewards |
| Shadow Goblin | -1 | Scorer | Measures compression progress, assigns interestingness |
GF(3) Conservation in Reward Flow
stakes_in + payouts_out + fees ≡ 0 (mod 3)
Every bet has a balanced flow: proposer stakes (+1), scorer validates (-1), coordinator settles (0).
Verification Protocol
- Proposer (Agent-O-Rama, +1): Claims "at seed S, index I, color will be C"
- Scorer (Shadow Goblin, -1): Checks
via MCP, computescolor_at(S, I)ΔC - Coordinator (CapTP, 0): If verified, transfers
to proposerstake × ΔC
def settle_bet(bet, scorer, coordinator) actual = Gay.color_at(bet.seed, bet.index) if actual == bet.predicted_color delta_c = scorer.compression_progress(actual) payout = bet.stake * delta_c coordinator.transfer(payout, to: bet.proposer) else coordinator.transfer(bet.stake, to: scorer) # Penalty end end
Multiversal Aspect
Different seeds = different possible worlds.
World 42: #91BE25 → #... → #... (one derivation path) World 69: #A3F4B2 → #... → #... (another path) World 1069: #E7B367 → #... → #... (goblin genesis)
Cross-world bets: Predict which world has higher average interestingness.
Arbitrage: If two worlds have identical color at index N, they share structure.
GF(3) Triads
curiosity-driven (-1) ⊗ multiversal-finance (+1) ⊗ captp (0) = 0 ✓ [Reward Transport] shadow-goblin (-1) ⊗ agent-o-rama (+1) ⊗ coordinator (0) = 0 ✓ [Market Roles] compression-progress (-1) ⊗ multiversal-finance (+1) ⊗ gay-mcp (0) = 0 ✓ [Interestingness]
Implementation Bridge
| Concept | Our System | Function |
|---|---|---|
| Price | | Higher ΔC = higher reward |
| Liquidity | | 3 parallel betting pools |
| Settlement | | Unforgeable proof |
| Sturdy ref | tuple | Bet identifier |
MARL + Mutual Information Layer
Multiversal finance IS multi-agent reinforcement learning where the reward signal is compression progress (ΔC) and the coordination metric is mutual information.
Markov Category Structure
Generative channel (MonadDistribution): Agent-O-Rama proposes (seed, index) bets Recognition channel (MonadFactor): Shadow Goblin scores & conditions on ΔC Composition (TracedT(WeightedT)): LMSR market equilibrium = Nash fixed point
Mutual Information Flow
I(observation; world_model) = ΔC = compression_progress I(agent_action; grid_state) = coordination_quality I(corpus; outcome) >> I(public; outcome) ← Dyssonance information advantage
Nashator Games (implemented in stress-games.ts)
| Game | Players | Models |
|---|---|---|
| Prosumer(-1) × Grid(+1) | Ontology cityLearn OpenGame |
| Agent-O-Rama(+1) × Shadow Goblin(-1) | Core prediction market |
| DER_Producer(-1) × DER_Consumer(+1) | MARL mutual info maximization |
| Trader(+1) × MM(0) × Oracle(-1) | LMSR as open game |
| Speculator(+1) × Ecosystem(-1) | WEV from proposal outcomes |
| Oracle(-1) × Trader(+1) | Privileged corpus scoring |
| Believer(0) × Evidence(0) | AGM rational belief change |
Lifecycle Compositions
multiversalLifecycle = mutualInfoCoordination ; multiversalPredictionMarket ; dyssonanceOracleGame ; wevExtractionGame energyMarketLifecycle = cityLearnDemandResponse ; agmBeliefRevisionGame ; mutualInfoCoordination
Skills Required
: Deterministic color oraclegay-mcp
: Secure settlement transportcaptp
: Compression progress metriccuriosity-driven
: Navigate between seeds (possible worlds)world-hopping
: Market schemaacsets-algebraic-databases
: SMC/MCMC backends for inferencemonad-bayes-asi-interleave
: Autopoietic ergodicity + Open Games bridgeontology-asi-interleave
: Nash equilibrium solver for all game compositionsnashator
Key Insight
The oracle is the market.
Since
color_at(seed, index) is deterministic and unforgeable, the prediction market
has perfect settlement. No disputes possible—the color either matches or it doesn't.
This is "multiversal" because different seeds explore different derivation paths through the same generative function. Betting = choosing which multiverse to observe.