{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:CUPCHA3FHFKIZQXAIJPEYKKS3D","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"960d73c4346b79c09e2e2832db337089a66e26718090f3b9281f0b65e5b66dad","cross_cats_sorted":["cs.AI","econ.GN","q-fin.EC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-11-02T14:42:20Z","title_canon_sha256":"21050c58558ae0841c9b1e47c67395de1505a7a77d4e105beb6bc0cff54e4007"},"schema_version":"1.0","source":{"id":"2011.01010","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.01010","created_at":"2026-07-05T02:16:15Z"},{"alias_kind":"arxiv_version","alias_value":"2011.01010v2","created_at":"2026-07-05T02:16:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.01010","created_at":"2026-07-05T02:16:15Z"},{"alias_kind":"pith_short_12","alias_value":"CUPCHA3FHFKI","created_at":"2026-07-05T02:16:15Z"},{"alias_kind":"pith_short_16","alias_value":"CUPCHA3FHFKIZQXA","created_at":"2026-07-05T02:16:15Z"},{"alias_kind":"pith_short_8","alias_value":"CUPCHA3F","created_at":"2026-07-05T02:16:15Z"}],"graph_snapshots":[{"event_id":"sha256:5a260c4c3a7ddd45d5d075d0e9295d9a406fcfcdf2c30e979398dbe2f4fd41b4","target":"graph","created_at":"2026-07-05T02:16:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2011.01010/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Campbell-Goodhart's law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it. This is a well known error in Economics and Political Science but not widely labelled in Artificial Intelligence research. Through a simple example, we show how off-the-shelf deep Reinforcement Learning (RL) algorithms are not necessarily immune to this cognitive error. The off-policy learning method is tricked, whilst the on-policy method is not. The practical implication is that naive application of ","authors_text":"Hal Ashton","cross_cats":["cs.AI","econ.GN","q-fin.EC"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-11-02T14:42:20Z","title":"Causal Campbell-Goodhart's law and Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.01010","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6ccc81b29070f3b2eaa157097d790bef74a2bb1b421d039eb29af58b958a5d94","target":"record","created_at":"2026-07-05T02:16:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"960d73c4346b79c09e2e2832db337089a66e26718090f3b9281f0b65e5b66dad","cross_cats_sorted":["cs.AI","econ.GN","q-fin.EC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-11-02T14:42:20Z","title_canon_sha256":"21050c58558ae0841c9b1e47c67395de1505a7a77d4e105beb6bc0cff54e4007"},"schema_version":"1.0","source":{"id":"2011.01010","kind":"arxiv","version":2}},"canonical_sha256":"151e23836539548cc2e0425e4c2952d8e348966457534591294c7ef9c2adb261","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"151e23836539548cc2e0425e4c2952d8e348966457534591294c7ef9c2adb261","first_computed_at":"2026-07-05T02:16:15.891081Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:16:15.891081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"or/FSdn44yC+dtJKSK6ZXcILwlmt3cgReb8tt6klOsR+yqG4UL3o4ZGnqygfKRwsy2a8VCIqvZyjU8f0D/syBw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:16:15.891590Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.01010","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ccc81b29070f3b2eaa157097d790bef74a2bb1b421d039eb29af58b958a5d94","sha256:5a260c4c3a7ddd45d5d075d0e9295d9a406fcfcdf2c30e979398dbe2f4fd41b4"],"state_sha256":"c95617209302be7c9c14ff4e0d985277ea2080f6a11d6c2d70d8daeb295f0b7e"}