How does this skill ensure market settlement?

Settlement is deterministic and unforgeable. It uses a color oracle to verify specific (seed, index) paths; because the result is mathematically certain, disputes are impossible.

What are the GF(3) roles within the market?

The market operates on a triad: the Proposer (Agent-O-Rama, +1) creates bets, the Scorer (Shadow Goblin, -1) measures progress, and the Coordinator (0) handles settlement.

What does 'Multiversal' mean in this context?

It refers to the different derivation paths or 'possible worlds' generated by different seeds. The market allows for betting on which world contains more discoverable structure.

What is the 'Interestingness' metric in Multiversal Finance?

The interestingness metric is based on Schmidhuber's curiosity-driven learning, calculated as the change (delta) in the minimum description length of the observer's world model after an observation.

Multiversal Finance

Name: Multiversal Finance
Author: plurigrid

byplurigrid

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Data Science & ML

Implements decentralized prediction markets for evaluating the 'interestingness' of deterministic observations using compression progress metrics.

Multiversal Finance is a specialized capability that integrates Schmidhuber’s curiosity-driven learning into a prediction market framework. By treating observations as bets on 'interestingness'—defined as the delta in compression progress—this skill enables Claude to navigate and value derivation paths across deterministic multiverses. It leverages ACSet schemas for market modeling and GF(3) triadic logic to ensure conservation in reward flows between proposers, scorers, and coordinators, providing a formal mathematical foundation for curiosity-driven data exploration.

Key Features

01GF(3) Conservation Logic: Balances reward flows between Agent-O-Rama, Coordinator, and Shadow Goblin roles.

02Compression Progress Metrics: Calculates quantitative 'interestingness' scores based on model delta.

03Deterministic Prediction Markets: Enables bets on unforgeable (seed, index) color witnesses.

04ACSet Schema Integration: Utilizes formal algebraic structures for multiversal market actors.

05Cross-World Arbitrage: Identifies structural similarities and shared patterns between different derivation paths.

068 GitHub stars

Use Cases

01Implementing zero-dispute settlement protocols for deterministic decentralized systems.

02Developing curiosity-driven AI agents that prioritize high-value data discovery.

03Modeling complex market dynamics using category theory and algebraic databases.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi multiversal-finance

For use in Claude.ai and ChatGPT

Download Skill