{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-07-31-7717",
 "identifier": "kaal:position:2026-07-31-7717",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Private Agent Based Modeling 64E1E0533D",
 "text": "Private Agent-Based Modeling presents the following source proposition: Yet, the incorporation of such data poses significant challenges due to privacy concerns. This proposition is pertinent to Kaal's source-bound claim that Federated learning does not eliminate privacy risk: because gradients and partial parameters are transmitted, the system remains vulnerable to attacks that leak data, and this vulnerability together with communication overhead is a significant hurdle to deployment. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
 "responseType": "extension",
 "keywords": [
  "consensus-and-security",
  "historical-response",
  "scholarly-literature",
  "crossref"
 ],
 "scope_conditions": [
  "The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4248.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
 "currentDebate": {
  "name": "Private Agent-Based Modeling",
  "url": "https://doi.org/10.65109/zalh8439"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-006",
  "url": "https://wulfkaal.github.io/claims/4855607-006",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/4855607-006"
  },
  {
   "@type": "CreativeWork",
   "name": "Private Agent-Based Modeling",
   "url": "https://doi.org/10.65109/zalh8439"
  }
 ],
 "batch_id": "kaal-review:2026-07-31:streaming-etl-0010",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#review",
 "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/2026-07-31-7717",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-07-31-7717.md",
 "candidateId": "kaal:response-draft:2026-07-31:7ff43c990edbef592b20",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "moderate-confidence claim review",
 "mappingConfidence": 0.4248,
 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: data, significant, due, privacy, concerns. Scope: federated learning schemes that share gradients or partial parameters with a server.",
 "sourceProvenance": {
  "source": "crossref",
  "sourceRecordId": "10.65109/zalh8439",
  "queryId": "concept:a2c487c0792c",
  "queryText": "autonomous agent accountability",
  "canonicalUrl": "https://doi.org/10.65109/zalh8439",
  "doi": "10.65109/zalh8439",
  "externalIds": {
   "DOI": "10.65109/zalh8439",
   "Crossref": "10.65109/zalh8439"
  },
  "retrievedAt": "2026-07-31T21:31:31.002Z",
  "providerPage": 10,
  "rawObservationSha256": "8b7af722e9ad4be141bd8322b978b34a2cbe1a4b18f885b07389887081c97e06",
  "inputSnapshotSha256": "38854b992555b92444c77ee205e21525efdcb5784dcf9267d112df4b84928413",
  "inputLine": 7384,
  "chunkId": "crossref-00011",
  "workId": "work:doi:10.65109/zalh8439",
  "workAuthors": [
   "Ayush Chopra",
   "Arnau Quera-Bofarull",
   "Nurullah Giray-Kuru",
   "Michael Wooldridge",
   "Ramesh Raskar"
  ],
  "workPublishedAt": "2024-05-06",
  "identityKeys": [
   "doi:10.65109/zalh8439",
   "crossref:10.65109/zalh8439",
   "url:https://doi.org/10.65109/zalh8439",
   "title:c529adc3a76a793c3173ae72",
   "proposition:1fbf4d3bb2ad6f37ece878ed97561e02e31961e34391a4cfaf8dc7f2d8078cd8"
  ],
  "sourceProposition": "Yet, the incorporation of such data poses significant challenges due to privacy concerns.",
  "sourcePropositionSha256": "c40148d1bff9fea663027bb6dae1bfccdab20f3ee2fe0403f7a81796c1578732",
  "sourcePropositionIndex": 1,
  "claimMappings": [
   {
    "claimId": "kaal:claim:4855607-006",
    "claimUrl": "https://wulfkaal.github.io/claims/4855607-006",
    "rank": 1,
    "confidence": 0.4248,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: data, significant, due, privacy, concerns. Scope: federated learning schemes that share gradients or partial parameters with a server.",
    "ambiguous": true
   },
   {
    "claimId": "kaal:claim:4941807-014",
    "claimUrl": "https://wulfkaal.github.io/claims/4941807-014",
    "rank": 2,
    "confidence": 0.3803,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: data, poses, significant, privacy. Scope: in centralized AI architectures that pool data into one repository.",
    "ambiguous": true
   },
   {
    "claimId": "kaal:claim:2348463-031",
    "claimUrl": "https://wulfkaal.github.io/claims/2348463-031",
    "rank": 3,
    "confidence": 0.326,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: private, data, privacy, concerns. Scope: judge-only access, with no disclosure to debtors or other creditors.",
    "ambiguous": true
   },
   {
    "claimId": "kaal:claim:4796714-032",
    "claimUrl": "https://wulfkaal.github.io/claims/4796714-032",
    "rank": 4,
    "confidence": 0.3168,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: such, data, privacy, concerns. Scope: applies to decentralized ML governance spanning multiple stakeholders and locations; concerns stringent privacy regimes such as GDPR and CCPA.",
    "ambiguous": true
   },
   {
    "claimId": "kaal:claim:4755632-005",
    "claimUrl": "https://wulfkaal.github.io/claims/4755632-005",
    "rank": 5,
    "confidence": 0.3168,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: such, data, privacy, concerns. Scope: applies to industries seeking to realize AI driven market potential.",
    "ambiguous": true
   }
  ]
 },
 "userAffirmation": "affirm batch kaal-review:2026-07-31:streaming-etl-0010, SHA-256 702170a5716aed9e79302e88407930485844aeec8c140fe64a7bf7d5dd9603af, as written and authorize publication of all 250 response claims on my canonical property, preserving their evidence levels, ambiguity labels, provenance, and the unchanged 5,033 scholarly claims.",
 "sha256": "9b2ceb2d4cebda3e0d872a09de0eb9a7cdebcdac34e56da3988fe02181cc7385"
}
