{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LPWW5VQRJMGNJPTBGWSSBOAUEO","short_pith_number":"pith:LPWW5VQR","canonical_record":{"source":{"id":"2403.02960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-03-05T13:30:55Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"b928e6075ea1a68eb512cfa8c4899a752c45b811a1a3f1da46419b14e4f2a061","abstract_canon_sha256":"4f8204c6245952d9cbb7d89ad9e194306b274570384c2b5a9b85fa3ebd095bfd"},"schema_version":"1.0"},"canonical_sha256":"5bed6ed6114b0cd4be6135a520b81423b729ec2b36c31c72e041cb5407e9235d","source":{"kind":"arxiv","id":"2403.02960","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.02960","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.02960v1","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.02960","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_12","alias_value":"LPWW5VQRJMGN","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_16","alias_value":"LPWW5VQRJMGNJPTB","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_8","alias_value":"LPWW5VQR","created_at":"2026-07-05T07:52:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LPWW5VQRJMGNJPTBGWSSBOAUEO","target":"record","payload":{"canonical_record":{"source":{"id":"2403.02960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-03-05T13:30:55Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"b928e6075ea1a68eb512cfa8c4899a752c45b811a1a3f1da46419b14e4f2a061","abstract_canon_sha256":"4f8204c6245952d9cbb7d89ad9e194306b274570384c2b5a9b85fa3ebd095bfd"},"schema_version":"1.0"},"canonical_sha256":"5bed6ed6114b0cd4be6135a520b81423b729ec2b36c31c72e041cb5407e9235d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:24.873440Z","signature_b64":"uWXo0apkM5uqOOPOWZlSGZ5/dtxz4WHzf/q7NrPDrrn+W1C6p6gFqY1+9iR7u09iDUWQ1WPqGMa0iD0AVRfBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bed6ed6114b0cd4be6135a520b81423b729ec2b36c31c72e041cb5407e9235d","last_reissued_at":"2026-07-05T07:52:24.873048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:24.873048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.02960","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:52:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eLARNc03XgTuPScaEwcyX1Wu8zFv2f1nBIVhaLKn6WG4/X0bVaJGlSWkier47A9h1qPK7dZrkrd0P9KL8vflCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T00:57:12.509599Z"},"content_sha256":"272e20635f7b51e7b8a5c3862bd538e7c91b35892c247ca2bbbacc73de0f3e85","schema_version":"1.0","event_id":"sha256:272e20635f7b51e7b8a5c3862bd538e7c91b35892c247ca2bbbacc73de0f3e85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LPWW5VQRJMGNJPTBGWSSBOAUEO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regret-based budgeted decision rules under severe uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Matthias C. M. Troffaes, Nawapon Nakharutai, S\\'ebastien Destercke","submitted_at":"2024-03-05T13:30:55Z","abstract_excerpt":"One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying precise probabilities is hard. For this reason, imprecise probability models uncertainty through convex sets of probabilities, and considers decision rules that can return multiple options to reflect insufficient information. Many well-founded decision rules have been studied in the past, but none of those standard rules are able to control the number of returned alterna"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.02960","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.02960/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:52:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e9bDMGFYd33ExqJu7atGJg8bdCmW2e6NdczTDMt3rPeR0GykNADz0EQ2wcIYCO9M5rUFFI8ez7b6wkHxH6inDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T00:57:12.509977Z"},"content_sha256":"6d1dbdec7e9aa9cc93b1c628d753922ef079aee2ab84319160975018248a70fa","schema_version":"1.0","event_id":"sha256:6d1dbdec7e9aa9cc93b1c628d753922ef079aee2ab84319160975018248a70fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/bundle.json","state_url":"https://pith.science/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-22T00:57:12Z","links":{"resolver":"https://pith.science/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO","bundle":"https://pith.science/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/bundle.json","state":"https://pith.science/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LPWW5VQRJMGNJPTBGWSSBOAUEO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LPWW5VQRJMGNJPTBGWSSBOAUEO","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":"4f8204c6245952d9cbb7d89ad9e194306b274570384c2b5a9b85fa3ebd095bfd","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-03-05T13:30:55Z","title_canon_sha256":"b928e6075ea1a68eb512cfa8c4899a752c45b811a1a3f1da46419b14e4f2a061"},"schema_version":"1.0","source":{"id":"2403.02960","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.02960","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.02960v1","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.02960","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_12","alias_value":"LPWW5VQRJMGN","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_16","alias_value":"LPWW5VQRJMGNJPTB","created_at":"2026-07-05T07:52:24Z"},{"alias_kind":"pith_short_8","alias_value":"LPWW5VQR","created_at":"2026-07-05T07:52:24Z"}],"graph_snapshots":[{"event_id":"sha256:6d1dbdec7e9aa9cc93b1c628d753922ef079aee2ab84319160975018248a70fa","target":"graph","created_at":"2026-07-05T07:52:24Z","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/2403.02960/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying precise probabilities is hard. For this reason, imprecise probability models uncertainty through convex sets of probabilities, and considers decision rules that can return multiple options to reflect insufficient information. Many well-founded decision rules have been studied in the past, but none of those standard rules are able to control the number of returned alterna","authors_text":"Matthias C. M. Troffaes, Nawapon Nakharutai, S\\'ebastien Destercke","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-03-05T13:30:55Z","title":"Regret-based budgeted decision rules under severe uncertainty"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.02960","kind":"arxiv","version":1},"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":"sha256:272e20635f7b51e7b8a5c3862bd538e7c91b35892c247ca2bbbacc73de0f3e85","target":"record","created_at":"2026-07-05T07:52:24Z","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":"4f8204c6245952d9cbb7d89ad9e194306b274570384c2b5a9b85fa3ebd095bfd","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-03-05T13:30:55Z","title_canon_sha256":"b928e6075ea1a68eb512cfa8c4899a752c45b811a1a3f1da46419b14e4f2a061"},"schema_version":"1.0","source":{"id":"2403.02960","kind":"arxiv","version":1}},"canonical_sha256":"5bed6ed6114b0cd4be6135a520b81423b729ec2b36c31c72e041cb5407e9235d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5bed6ed6114b0cd4be6135a520b81423b729ec2b36c31c72e041cb5407e9235d","first_computed_at":"2026-07-05T07:52:24.873048Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:52:24.873048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uWXo0apkM5uqOOPOWZlSGZ5/dtxz4WHzf/q7NrPDrrn+W1C6p6gFqY1+9iR7u09iDUWQ1WPqGMa0iD0AVRfBBA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:52:24.873440Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.02960","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:272e20635f7b51e7b8a5c3862bd538e7c91b35892c247ca2bbbacc73de0f3e85","sha256:6d1dbdec7e9aa9cc93b1c628d753922ef079aee2ab84319160975018248a70fa"],"state_sha256":"00e845f5f19dc992f51b380d345e477e487da63e600c69117ba7d906380a1bc1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e3YouNRvzvjS4xXMtXYz8h9Cn/7oTEVlrQfgtniYWauMDa6urBhFj9TvNauzbxBHBxmiAdCHtzfO+pEeDuK0Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T00:57:12.512116Z","bundle_sha256":"5d53bf760bc3a3e8efb8abd1486d7109c300ce134b395911cb82aa68cf28ecc8"}}