{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:ZEFEBX4BORDVRSJDSTTOY26E5B","short_pith_number":"pith:ZEFEBX4B","schema_version":"1.0","canonical_sha256":"c90a40df81744758c92394e6ec6bc4e87eca0091d0caa09ec3806efe9f1c9c33","source":{"kind":"arxiv","id":"1505.00566","version":1},"attestation_state":"computed","paper":{"title":"Estimating the Margin of Victory of an Election using Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Palash Dey, Y. Narahari","submitted_at":"2015-05-04T09:25:42Z","abstract_excerpt":"The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections.\n  More formally, we introduce the \\textsc{$(c, \\epsilon, \\delta)$--Margin of Victory} problem, where given an election $\\mathcal{E}$ on $n$ voters, the goal is to estimate the margin of victory $M(\\mathcal{E})$ of $\\mathcal{E}$ within an additive factor of $c Mo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1505.00566","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-05-04T09:25:42Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"930c15d9749505eecaf49d2bfe9892fc733349643bb7ba1a67de292db42e274b","abstract_canon_sha256":"70ba2433928cafa13e9bf47aac5064ad2bafa28b4df7d0dc4717e097165ecffd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:02.593049Z","signature_b64":"/RlYkja8s+O8Zrkc/CHRCDh+BvCKJ5DzhwJqE68avCkkOcFwLNqTyChTCGKtCLPJJPKkjf+Aq3VSLdqaZ4WxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c90a40df81744758c92394e6ec6bc4e87eca0091d0caa09ec3806efe9f1c9c33","last_reissued_at":"2026-05-18T02:17:02.592399Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:02.592399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating the Margin of Victory of an Election using Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Palash Dey, Y. Narahari","submitted_at":"2015-05-04T09:25:42Z","abstract_excerpt":"The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections.\n  More formally, we introduce the \\textsc{$(c, \\epsilon, \\delta)$--Margin of Victory} problem, where given an election $\\mathcal{E}$ on $n$ voters, the goal is to estimate the margin of victory $M(\\mathcal{E})$ of $\\mathcal{E}$ within an additive factor of $c Mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00566","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1505.00566","created_at":"2026-05-18T02:17:02.592479+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.00566v1","created_at":"2026-05-18T02:17:02.592479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00566","created_at":"2026-05-18T02:17:02.592479+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEFEBX4BORDV","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEFEBX4BORDVRSJD","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEFEBX4B","created_at":"2026-05-18T12:29:52.810259+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B","json":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B.json","graph_json":"https://pith.science/api/pith-number/ZEFEBX4BORDVRSJDSTTOY26E5B/graph.json","events_json":"https://pith.science/api/pith-number/ZEFEBX4BORDVRSJDSTTOY26E5B/events.json","paper":"https://pith.science/paper/ZEFEBX4B"},"agent_actions":{"view_html":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B","download_json":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B.json","view_paper":"https://pith.science/paper/ZEFEBX4B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.00566&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEFEBX4BORDVRSJDSTTOY26E5B/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEFEBX4BORDVRSJDSTTOY26E5B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B/action/storage_attestation","attest_author":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B/action/author_attestation","sign_citation":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B/action/citation_signature","submit_replication":"https://pith.science/pith/ZEFEBX4BORDVRSJDSTTOY26E5B/action/replication_record"}},"created_at":"2026-05-18T02:17:02.592479+00:00","updated_at":"2026-05-18T02:17:02.592479+00:00"}