{
  "abstract": "Predictive-score-based valuation often allocates value from residual or accuracy improvements while leaving the map to downstream operational value implicit. We study federated\n\ndemand forecasting among firms as a transferable-utility cooperative game whose characteristic function is an inventory-motivated weighted reduction in predictive residual scale.\n\nWeights combine the normal-loss expected-cost coefficient in holding and shortage costs\n\nwith a √\n\nLi + 1 protection-period scaling factor under an i.i.d. one-step error benchmark;\n\nthey supply a relative inventory-motivated weighting rule rather than a calibrated estimator of realized inventory cost, and are not claimed to coincide with realized path inventory\n\ncost under a finite-horizon single-echelon diagnostic. Coalition models use exact federated\n\nridge on additive training statistics Ti = (Ai\n\n, bi\n\n, ni) with size-scaled regularization (including the intercept). The design caches residual maps and changes only operational weights,\n\nso that, conditional on each seed’s fixed residual map, ranking and normalized-allocation\n\nshifts are induced by weighting rather than refitting. Along a mean-normalized path from\n\nequal weights to the largest tested effective weight imbalance (κeff ≈ 2.94), Spearman agreement with the unweighted residual ranking falls from 1 to 0.89–0.77 for n ∈ {4, 6, 8} (40\n\nseeds), with full-rank-change rates up to 1.00 and exhaustive assignment dispersion (4! = 24,\n\n6! = 720, 8! = 40320) showing that, within the tested DGP and weight multiset, fixed κeff\n\ndoes not uniquely determine rank agreement. In an n=4 finite-horizon path-cost diagnostic\n\nusing average cost per hold-out period, residual-weighted and path-cost Shapley rankings are\n\nnegatively correlated (bootstrap CIs exclude zero). We report analytically computed first\n\nrank-change thresholds along the mean-normalized path, Wilson intervals, and robustness\n\nunder fixed versus size-scaled ridge and moderate sample-size heterogeneity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Sajjad Khoshakhlagh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7vkw6frh9az80.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23457715",
  "fulltext_url": null,
  "id": "7vkw6frh9az80",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23457715",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23457715/files/zk_shapley_paper.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7vkw6frh9az80.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:20:57Z",
      "native_id": "23457715",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23457715"
    }
  ],
  "subjects": [],
  "title": "Operational Weighting Changes Cooperative Forecast Valuation: A Residual-Fixed Sensitivity Analysis for Federated Demand Forecasting",
  "title_text": "Operational Weighting Changes Cooperative Forecast Valuation: A Residual-Fixed Sensitivity Analysis for Federated Demand Forecasting",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:57Z",
  "url": "https://research.draconitic.com/paper/7vkw6frh9az80",
  "venue": "Zenodo",
  "versions": []
}