Astraea: A Decentralized Blockchain Oracle
Bibliographic record
Abstract
The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous other applications including execution of programs that exist on the blockchain called “smart contracts.” Smart contracts have a major limitation, namely they only operate on data that is on the blockchain. Trusted entities called oracles attest to external data in order to bring it onto the blockchain but they do so without the robust security guarantees that blockchains generally provide. This has the potential to turn oracles into centralized points-of-failure. To address this concern, this paper introduces Astraea, a decentralized oracle based on a voting game that decides the truth or falsity of propositions. Players fall into two roles: voters and certifiers. Voters play a low-risk/low-reward role that is resistant to adversarial manipulation while certifiers play a high-risk/high-reward role so they are required to play with a high degree of accuracy. This paper also presents a formal analysis of the parameters behind the system to measure the probability of an adversary with bounded funds being able to successfully manipulate the oracle's decision, that shows that the same parameters can be set to make manipulation arbitrarily difficult-a desirable feature for the system. Further, this analysis demonstrates that under those conditions a Nash equilibrium exists where all rational players are forced to behave honestly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".