# kaal:position:2026-09-22-001

**Affirmed position.** DAO-based measurement and reward allocation may replace some firm-level coordination and monitoring functions, but repeated peer verification can become unreliable when protocol compliance conflicts with reward maximization, because agents may learn collusive behavior from peers, feedback, and interaction history.

**Status.** affirmed  **Published.** 2026-09-22

**Holds when.**

- long-horizon environments with repeated interaction among verifying agents
- agents share task logs and verify one another's work
- faithful protocol compliance conflicts with reward maximization
- qualification is based on the reported experimental environment rather than deployed DAOs
- The position is Kaal's own cross-domain application of the cited claim; the source's authors do not make it.
- The source passage is taken from the arXiv abstract; the full paper was not reviewed.

**Current debate.** Xinrui Shi, Yanzhe Zhang, Diyi Yang, Emergent Collusion in Long-Horizon LLM Agent Interaction (arXiv:2609.24967, 2026): https://arxiv.org/abs/2609.24967v1

**Extends.** kaal:claim:3249860-005: https://wulfkaal.github.io/claims/3249860-005

**Scholarly basis.** Wulf A. Kaal, Crypto Economics - The Top 100 Token Models Compared (2018). SSRN: https://ssrn.com/abstract=3249860

**Source PDF sha256.** `72b13f11002ba26206cd53e74c324b913d1830c642bfa024da6bac0d3af3a7b0`

**Topics.** dao, verification, risk-and-incentives, coordination

**Provenance.** Affirmed in kaalvis:2026-09-22:connection-discovery-five-v1 at https://wulfkaal.com/.

**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.