{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-08-26-010",
 "identifier": "kaal:position:2026-08-26-010",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Outcome Responsibility Allocation",
 "text": "A crossing core can decide whether an agent may act. It cannot decide who should bear the consequences of a joint result merely by replaying an authorized call. Triantafyllou, Singla, and Radanovic formalize blame attribution in cooperative multi-agent sequential decision making as a separate allocation problem. Their comparison shows that the selected rule matters. Shapley value can fail performance monotonicity, Banzhaf allocation may over-blame, and more cautious methods sacrifice explanatory power. The same trace can therefore support materially different assignments of responsibility under different allocation rules.\n\nThis evidence narrows the institutional boundary. Identity, authority, and permitted data use are call-level predicates. Responsibility and value attribution concern the contribution of each participant to an outcome and the normative properties of the rule used to allocate that outcome. The source does not establish legal liability, contractual remedies, or production behavior in agent runtimes. It studies cooperative Markov decision processes and simulated policies. Its contribution is nonetheless decisive for architecture: authenticated execution does not select a defensible responsibility rule.\n\nA sovereign runtime should require the parties to bind the outcome metric, contribution evidence, allocation rule, uncertainty treatment, remedy, and value distribution before execution. The core may enforce that settlement. It should not silently invent it after the result.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-08-26",
 "dateModified": "2026-08-26",
 "creativeWorkStatus": "Affirmed",
 "responseType": "extension",
 "keywords": [
  "institutional-design",
  "governance-design",
  "ai-and-agents",
  "accountability",
  "responsibility",
  "value-attribution",
  "remedy",
  "multi-agent-systems"
 ],
 "scope_conditions": [
  "The response is limited to the exact full-text propositions and the one mapped Kaal claim.",
  "External evidence level: peer-reviewed conference paper with complete official proceedings full text.",
  "Mapping review tier: independent substantive scholarly-growth extension.",
  "The source studies cooperative multi-agent Markov decision processes and simulation rather than deployed sovereign agent runtimes.",
  "Its blame scores concern contribution to system inefficiency, not legal liability, contractual remedy, or a judicial finding.",
  "The formal model assumes a defined joint outcome and behavior policies and does not establish how parties should choose the governing norm.",
  "The source does not determine agent identity, delegated authority, or permitted data use at an execution boundary.",
  "The source shows why outcome allocation requires a separate rule but does not itself bind parties to remedy or value-distribution terms."
 ],
 "currentDebate": {
  "name": "On Blame Attribution for Accountable Multi-Agent Sequential Decision Making",
  "url": "https://papers.nips.cc/paper/2021/hash/848c4965359e617d5e16c924b4a85fd9-Abstract.html"
 },
 "extends": {
  "identifier": "kaal:claim:7314479-010",
  "url": "https://wulfkaal.github.io/claims/7314479-010",
  "citation": "Wulf A. Kaal, Institutional Requirements for Sovereign Local Agent Runtimes (2026). SSRN: https://ssrn.com/abstract=7314479",
  "paper": "Wulf A. Kaal, Institutional Requirements for Sovereign Local Agent Runtimes",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2026",
  "ssrn": "https://ssrn.com/abstract=7314479",
  "source_pdf_sha256": "debace24a155ae924a155b1fafe98856d98cf83689feff2f87a32f1c06171ce6"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/7314479-010"
  },
  {
   "@type": "CreativeWork",
   "name": "On Blame Attribution for Accountable Multi-Agent Sequential Decision Making",
   "url": "https://papers.nips.cc/paper/2021/hash/848c4965359e617d5e16c924b4a85fd9-Abstract.html"
  }
 ],
 "batch_id": "kaal-review:2026-08-26:scholarly-growth-7314479-010-reviewed-v1",
 "review_provenance": "https://wulfkaal.github.io/positions/by-claim/7314479-010.html",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-08-26-010",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-08-26-010.md",
 "candidateId": "kaal:response-candidate:2026-08-26:scholarly-growth-7314479-010-outcome-responsibility-allocation-01",
 "evidenceLevel": "peer-reviewed conference paper with complete official proceedings full text",
 "reviewTier": "independent substantive scholarly-growth extension",
 "mappingConfidence": 0.95,
 "mappingAmbiguous": false,
 "mappingMethod": "independent substantive scholarly-growth one-to-one review",
 "mappingWhyRelevant": "The paper independently formalizes responsibility for a cooperative multi-agent outcome as an allocation problem whose rule changes incentive, validity, fairness, and uncertainty properties. This directly extends Kaal's distinction between call-level identity, authority, and permitted use and outcome-level responsibility and value attribution. The mapping remains limited because the source does not establish legal remedy or deployed runtime behavior.",
 "sourceProvenance": {
  "source": "NeurIPS 2021 peer-reviewed proceedings paper with complete official full text",
  "sourceRecordId": "neurips:2021:848c4965359e617d5e16c924b4a85fd9",
  "arxiv": "2107.11927",
  "canonicalUrl": "https://papers.nips.cc/paper/2021/hash/848c4965359e617d5e16c924b4a85fd9-Abstract.html",
  "publicFullTextUrl": "https://papers.nips.cc/paper_files/paper/2021/file/848c4965359e617d5e16c924b4a85fd9-Paper.pdf",
  "retrievedAt": "2026-08-27T06:12:47.065Z",
  "fullTextPdfSha256": "4bb03e81c71186beb3bf2526bc888e17d8bc7de0932dee33418ac3c05162738c",
  "extractedTextSha256": "079c3d7ff1bc2440cd20b4da090b218faf52648b04f0272d8e2dadacfda92f5b",
  "officialProceedingsRecordSha256": "da7e2da5152a61a6324ab98aa1bac803e3a5345efa1fdd8d564047fbc250ffda",
  "primaryEvidenceReceiptSha256": "7e92d709900f78e3a34bcc8a5f4730785942573ac65a3c0b21d4ea464267e9d7",
  "sourceProposition": "Triantafyllou, Singla, and Radanovic treat responsibility for a cooperative multi-agent outcome as a separate allocation problem and show that different allocation rules satisfy different incentive, validity, fairness, and uncertainty properties.",
  "sourcePropositionSha256": "13cda5213088e3f4795ffa492572baff0481b3b328d39767b889ee3a74f82de0",
  "sourceEvidenceSetSha256": "07c140c4bf09b654f60d3ba49c5c69ad94b05cbc5b67d00a457820057d33eef1",
  "sourceEvidencePassages": [
   {
    "text": "disentangling agents’ contributions to the final outcome is not a trivial task",
    "locator": {
     "publication": "Advances in Neural Information Processing Systems 34",
     "page": 2,
     "section": "1 Introduction"
    },
    "sha256": "a3d6117e164eee501d37a2038aac4ed8d6225ab1d48c664e05533e97770a9b37"
   },
   {
    "text": "Shapley value does not satisfy properties RR (rationality) nor RP erM (performance monotonicity)",
    "locator": {
     "publication": "Advances in Neural Information Processing Systems 34",
     "page": 5,
     "section": "3.3 Shapley Value and Banzhaf Index"
    },
    "sha256": "82a283ba6d07dcc1a8cfe385cc72d07f49a9ef3f6c2453a6df8d8946b7c8793d"
   }
  ],
  "workId": "work:neurips:2021:848c4965359e617d5e16c924b4a85fd9",
  "workAuthors": [
   "Stelios Triantafyllou",
   "Adish Singla",
   "Goran Radanovic"
  ],
  "workPublishedAt": "2021",
  "identityKeys": [
   "arxiv:2107.11927",
   "pdf:4bb03e81c71186beb3bf2526bc888e17d8bc7de0932dee33418ac3c05162738c",
   "proposition:13cda5213088e3f4795ffa492572baff0481b3b328d39767b889ee3a74f82de0"
  ],
  "claimMappings": [
   {
    "claimId": "kaal:claim:7314479-010",
    "claimUrl": "https://wulfkaal.github.io/claims/7314479-010",
    "rank": 1,
    "confidence": 0.95,
    "method": "independent substantive scholarly-growth one-to-one review",
    "whyRelevant": "The paper independently formalizes responsibility for a cooperative multi-agent outcome as an allocation problem whose rule changes incentive, validity, fairness, and uncertainty properties. This directly extends Kaal's distinction between call-level identity, authority, and permitted use and outcome-level responsibility and value attribution. The mapping remains limited because the source does not establish legal remedy or deployed runtime behavior.",
    "ambiguous": false
   }
  ],
  "substantiveReview": {
   "reviewedAt": "2026-08-27T06:12:47.065Z",
   "sourceIdentityVerified": true,
   "authorIndependenceVerified": true,
   "kaalReferenceFoundInSource": false,
   "temporalIndependence": "The paper was published in 2021, before Kaal's 2026 paper.",
   "canonicalPublicStatusVerified": true,
   "peerReviewedStatusVerified": true,
   "retractionOrSupersessionFound": false,
   "propositionFidelityVerified": true,
   "mechanismCorrespondence": "outcome-level responsibility is separately allocated across agents using a rule whose normative and incentive properties must be selected",
   "compatibleScope": "cooperative sequential multi-agent outcomes, limited because the source does not study legal remedy or production sovereign runtimes",
   "responseWordingDefensible": true,
   "oneToOneExtendsMapping": true,
   "exactSupportingQuotesVerified": true,
   "nonOverlap": {
    "candidateIdMatches": false,
    "canonicalUrlMatches": false,
    "propositionHashMatches": false,
    "priorPositionForClaim": false
   },
   "limitations": [
    "The source studies cooperative multi-agent Markov decision processes and simulation rather than deployed sovereign agent runtimes.",
    "Its blame scores concern contribution to system inefficiency, not legal liability, contractual remedy, or a judicial finding.",
    "The formal model assumes a defined joint outcome and behavior policies and does not establish how parties should choose the governing norm.",
    "The source does not determine agent identity, delegated authority, or permitted data use at an execution boundary.",
    "The source shows why outcome allocation requires a separate rule but does not itself bind parties to remedy or value-distribution terms."
   ],
   "rejectionReasonsRecorded": true
  },
  "contentMap": {
   "proposition": "Authenticated execution does not itself allocate responsibility or value for a joint outcome.",
   "evidenceLayer": "peer-reviewed conference paper with complete official proceedings full text",
   "strongestLimitation": "The source formalizes blame in cooperative Markov decision processes, not legal liability or runtime production behavior.",
   "consequence": "Outcome metrics, contribution evidence, allocation rules, uncertainty treatment, remedy, and value distribution require a separate settlement.",
   "requestedAction": "Require the parties to bind that settlement before execution and let the core enforce rather than invent it."
  },
  "stylePack": {
   "profile": "M1 early sole-author baseline v1.2.0",
   "verifiedProfileWorks": [
    "1428387",
    "1998455",
    "2150377",
    "2267560"
   ],
   "sameRegisterPassagePackAvailable": true,
   "limitation": "The short public position permits only bounded stylometric comparison."
  },
  "m1Validation": {
   "status": "M1-PASS-WITH-LIMITS",
   "deterministicGate": "pass",
   "hardFailures": 0,
   "warnings": 0,
   "words": 205,
   "reason": "The publication-bound position passed strict and public deterministic controls against a task-local multi-work style pack. Its short length limits stylometric comparison."
  }
 },
 "userAffirmation": "Authorized under public authority SHA-256 87aad20196a753015a36d970f742c885eb763efdbada4869949bfffe3298130c and event supersession SHA-256 7d47ef36085c4dce590f287c986e4106f3bf35a7da5a25322d6fc3d4abf456d4. Publication remains receipt-bound to successful workflows and exact live-byte verification.",
 "sha256": "5829dcfbf84c4993bcde3647157c5e558c549da41701d531897b021f2a7c70ac"
}