# Information loss

`kaal:entity:information-loss`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `information-loss`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 3 works, 2020 to 2024.

**2020**

- [3606663-032](https://wulfkaal.github.io/claims/3606663-032) [mechanism/argued] *(failure mode)* -- Because human interactions in business are often too complex to be fully codified objectively, DeFi systems that exclude all non objective information from their analysis do not fully utilize available information, which limits their efficiency and usefulness.
  > However, human interaction in business are often too complex to be fully codified objectively. By excluding all non- objective information from the product analysis, all available information may not be fully utilized which limits efficiency and potential usefulness of DeFi
  Kaal, Digital Asset Market Evolution (2020). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3606663

**2021**

- [3782192-007](https://wulfkaal.github.io/claims/3782192-007) [mechanism/argued] *(failure mode)* -- Because information at the edge does not travel up a centralized hierarchy, the central authority makes decisions without full information, and a centralized organization is therefore not well designed to respond to any crisis it has not anticipated.
  > The central authority makes decisions without full information, so a central- ized organization is not well designed to respond to any crisis it hasn't anticipated.
  Craig Calcaterra, Wulf A. Kaal, Introduction to Decentralization (2021). SSRN: https://ssrn.com/abstract=3782192

**2024**

- [4855607-010](https://wulfkaal.github.io/claims/4855607-010) [failure/evidenced] *(failure mode)* -- GNN scalability on large real world graphs is a genuine trade off rather than an engineering gap: sampling methods lose influential neighbors while clustering methods lose structural patterns, so each remedy sacrifices part of the signal the model needs.
  > Scalability is a major concern for GNNs on large real-world graphs, as sampling methods may lose influential neighbors while clustering methods may lose structural patterns.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/information-loss.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.