--- license: cc-by-4.0 language: - en task_categories: - tabular-classification pretty_name: OracleEconLab Oracle Incentives and Accountability v1 size_categories: - n<1K tags: - blockchain - oracle - economics - trustworthy-ai - tabular configs: - config_name: abc_dictionary data_files: data/abc_dictionary.parquet - config_name: uma_real_episode data_files: data/uma_real_episode.parquet - config_name: uma_real_episode_abc data_files: data/uma_real_episode_abc.parquet - config_name: uma_public_rpc_episode data_files: data/uma_public_rpc_episode.parquet - config_name: uma_public_rpc_abc data_files: data/uma_public_rpc_abc.parquet - config_name: uma_decisions data_files: data/uma_decisions.parquet - config_name: uma_decision_splits data_files: data/uma_decision_splits.parquet - config_name: decision_evidence data_files: data/decision_evidence.parquet - config_name: ai_feature_dictionary data_files: data/ai_feature_dictionary.parquet - config_name: public_ai_episodes data_files: data/public_ai_episodes.parquet - config_name: public_ai_decisions data_files: data/public_ai_decisions.parquet - config_name: public_ai_splits data_files: data/public_ai_splits.parquet - config_name: public_ai_evidence data_files: data/public_ai_evidence.parquet - config_name: public_ai_predictions data_files: data/public_ai_predictions.parquet - config_name: public_ai_metrics data_files: data/public_ai_metrics.parquet --- # OracleEconLab Oracle Incentives and Accountability v1 This review-sized draft turns fixed, public UMA protocol evidence into complete or right-censored economic lifecycles. It is a release candidate for the paper *Who Verifies Decentralized Information? Economic Incentives, Accountability, and Trustworthy AI Across Oracle Protocols.* ## Dataset configurations | Configuration | Rows | Observation unit | Purpose | |---|---:|---|---| | `abc_dictionary` | 28 | economic variable | Formulas, units, sources, missing rules and A/B/C family | | `uma_real_episode` | 1 | cross-chain episode | End-to-end Polygon OOV2 to Ethereum DVM validation | | `uma_real_episode_abc` | 28 | case-measure status | Honest mapping of every A/B/C measure to the real case | | `uma_public_rpc_episode` | 1 | cross-chain episode | Clean-room episode reconstructed without curated-ledger input | | `uma_public_rpc_abc` | 28 | case-measure status | Public-source-only A/B/C mapping and explicit unavailable states | | `uma_decisions` | 810 | challenged proposal-time decision | Minimal four-action AI evaluation | | `uma_decision_splits` | 810 | decision split assignment | Frozen chronological train/validation/test partition | | `decision_evidence` | 2430 | timestamped evidence reference | Prediction-level provenance and leakage audit | | `ai_feature_dictionary` | 16 | decision-time feature | Feature meaning, source and availability | | `public_ai_episodes` | 64 | public-source cross-chain episode | Economic lifecycle reconstructed from fixed public receipts | | `public_ai_decisions` | 64 | proposal-time decision | Credential-free public-source AI demonstration inputs | | `public_ai_splits` | 64 | split assignment | Label-free chronological 44/10/10 partition | | `public_ai_evidence` | 192 | timestamped evidence reference | Decision-time traceability for every demonstration prediction | | `public_ai_predictions` | 70 | model/action output | Four-action predictions, confidence, evidence and protocol endpoint | | `public_ai_metrics` | 7 | model evaluation | Calibration, coverage, abstention and stylized economic cost | ## A/B/C economic measure families - **A — Verification concentration (Who verifies?):** HHI, top-k share, effective number of monitors and threshold monitor count. - **B — Incentives and frictions (Why verify?):** reward/bond, capital-days, fees, Gas, verification cost and scenario-based expected private utility. - **C — Accountability outcomes (What happens?):** dispute, adjudication, transfer, realized payoff, delay and right censoring. A/B/C are measure families. They are not the project's Sample A/B/C data cohorts. Identity, decision-time, cross-chain-link and provenance fields remain supporting fields rather than being mislabeled as economic outcomes. ## Construction and provenance The fixed cutoff is `2026-06-30T23:59:59Z`. The real case begins with canonical Polygon request, proposal, dispute and settlement records, links them to the Ethereum DVM with a Grade-A deterministic match, reconciles token flows, and separates returned principal from realized reward. Every processed episode or decision contains transaction/evidence references and a deterministic provenance identifier. File integrity is recorded in `checksums.csv`. The main reconstruction commands live in the GitHub `real-release-v1.5` branch. `public_evidence/` includes the five fixed receipts, canonical headers, case specification, source checksums and verified contract-semantic rules used by the clean-room entry. This draft contains review-sized processed evidence; it does not claim that every protocol ledger can be regenerated without archive infrastructure. The executed `notebooks/uma_minimal_trustworthy_ai_demo.ipynb` loads the frozen tables, reruns the leakage audit and chronological evaluation, and writes the review-sized artifacts under `demo_outputs/`. Its reported performance is not edited or filtered after execution. The independent `public_ai_evidence/` path closes the public-source chain for a fixed 64-episode demonstration: a label-free transaction registry, 280 canonical Polygon/Ethereum receipt and header bundles, decoded economic episodes, decision-time features, model outputs and QC. Live mode uses credential-free public endpoints; offline mode deterministically replays the frozen snapshot. The model result is intentionally reported even when weak. It is a pipeline demonstration rather than evidence of production challenge quality. ## Trustworthy-AI task At the OOV2 proposal timestamp, the benchmark emits one of `Accept`, `Investigate`, `Challenge`, or `Abstain`. The endpoint is whether the later UMA protocol resolution rejects the proposal. Only decision-time fields may be used as model inputs. Columns whose names end in `_outcome_only`, the protocol label, dispute result and settlement evidence are evaluation-only and must not enter the model. The 810 observations condition on proposals that were actually challenged, Grade-A linked, price-consistent and flow-exact. The task is therefore a conditional protocol-outcome evaluation, not a causal policy for all proposals and not an independent truth classifier. ## Intended uses - audit economic variable construction and source traceability; - reproduce the real UMA episode and its payoff decomposition; - evaluate calibration, abstention, leakage and robustness on the conditional decision cohort; - study A/B/C constructs within their declared observation units. ## Uses that are not validated - production deployment of automated challenges; - causal claims that rewards or penalties improve truthfulness; - treating protocol acceptance or DVM resolution as independent factual truth; - identity, geography or intent inference from blockchain addresses; - aggregation of heterogeneous token amounts without asset-preserving conversion. ## Biases and limitations The decision cohort has selective labels because it includes only actually challenged UMA requests. It over-represents mechanisms and market adapters for which exact cross-chain and token-flow evidence is recoverable. Address-level concentration is not entity-level concentration. Off-chain investigation labor is unavailable and remains a declared scenario. Independent factual truth is available only for a small external-rule subset outside the core protocol label. ## Personal and sensitive information The data contain public pseudonymous contract/account addresses, transaction hashes and block metadata. They contain no asserted mapping from an address to a natural person and must not be used to infer human identity. RPC endpoint URLs, credentials and private keys are not included. ## Licensing Original curated tables and documentation are CC BY 4.0. Upstream contracts, APIs and third-party records remain governed by their own terms. See `DATA_LICENSE.md` and `CITATION.cff`.