Introduction to Idea
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Peer to Peer Science proposes a decentralized approach to the scientific process using blockchain and distributed computing. Key aspects include decentralized sensor mapping and sampling protocols to gather representative data from the network. Computational models or "Dmaps" are scored based on their predictions, complexity, and context through a blind oracle mechanism. The network aims to make it difficult to selectively choose data that supports a particular theory. The goal is for the best available science to be accessible to all.
Introduction to Idea
- 1. Peer to Peer Science Dsensor.org Introduction to idea
- 3. N T I M P
- 5. 'too clean'
- 6. Future of Science? ● MESSY ● Computationa lly active 24/7
- 7. ● Clean context –-> Myriad context ● Peer review –-> Computational consensus ● Clinical Trials –-> Computationally active Open Science Citizen Human Guess Dsensor Human + Machine Peer review Journal
- 8. Blockchain + Proof of work Complexity BLOCKCHAIN DISCOVERY SPACE Vast uncertainty & complexity
- 9. Problem to solve ● Make it (near) impossible to validate or cheery pick data sets that support a science theory regardless of having enough money or compute resources to flood the network with data.
- 10. Peer to Peer Science BLOCKCHAIN Compute Consensus DMAPS Decentralized Sensor Mapping Protocol
- 11. Decentralised Sensor Mapping Protocol ● Best science available to all ● 'Scoring' Protocol -prediction complexity context ● Decentralized Sampling Protocol ● Anyone free to put forward Dmaps (scientific computations-how world works) ● Blockchain 'Science Accounting'
- 12. Dsampling ● Two different DHTs to 'random' walk over ● Estimate network size from structure ● Samples representative of 'reality' ● Auto record – proof of evidence trail
- 13. Network View
- 14. Peer view DHT A DHT B IF (CHOSEN) Start COMPUTE Select A or B Random select peer
- 15. Dscoring ● Blind Oracle on scientific truth ● Score – predictions, complexity, context ● Learn: − Same context – segment 'accuracy' − Across different context − Aggregate 'network' view 'priors'
- 16. Process Dmap – computational (knowledge) theory Samples across network and computes Returns a prediction path that is locked into blockchain PredictionReality
- 17. Learning ● Smart contract aggregates 'training' ● MCMC scale up 'scoring'
- 18. Quotes ● New post monetary currency ● A unit of computationally active knowledge