# kaal:position:2026-07-31-606

**Affirmed position.** Agent-based modeling for decentralized autonomous organizations and decentralized finance should be assessed against Kaal's source-bound claim that Reward modeling learned through interaction with users carries two structural pathologies: majority views disproportionately influence the learned reward function, and the agent may engage in reward hacking. The current metadata indicates a plausible connection through decentralized autonomous organization, but the defensible response is a qualification until the source text confirms agreement, scope, methods, and limitations.

**Status.** affirmed  **Published.** 2026-07-31

**Holds when.**

- reward functions learned from aggregated user interaction
- External evidence level: metadata only.
- Mapping review tier: moderate-confidence claim review.
- The literature-to-claim mapping remains explicitly ambiguous and should not be treated as a settled equivalence.

**Current debate.** Agent-based modeling for decentralized autonomous organizations and decentralized finance: https://www.semanticscholar.org/paper/aa0fbb27886e60f2e4305bc874fd9aceb6eb56c4

**Extends.** kaal:claim:4855607-015: https://wulfkaal.github.io/claims/4855607-015

**Scholarly basis.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Source PDF sha256.** `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113`

**Evidence level.** metadata only

**Mapping review tier.** moderate-confidence claim review

**Mapping confidence.** 0.4565  **Mapping ambiguous.** true

**Topics.** ai-and-agents

**Provenance.** Affirmed in historical-backfill:2026-07-31:phase-0003 at https://kaal-signal-desk.wulf577462.chatgpt.site/#review.

**Record type.** This is a dated commentary position that extends a scholarly corpus claim. It is not a verbatim claim extracted from the paper.

**Canonical form.** This markdown file is the canonical hashed representation of the position.