teleo-codex/domains/internet-finance/optimal governance requires mixing mechanisms because different decisions have different manipulation risk profiles.md
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leo: remove 21 duplicates + fix domain:livingip in 204 files
- What: Delete 21 byte-identical cultural theory claims from domains/entertainment/
  that duplicate foundations/cultural-dynamics/. Fix domain: livingip → correct value
  in 204 files across all core/, foundations/, and domains/ directories. Update domain
  enum in schemas/claim.md and CLAUDE.md.
- Why: Duplicates inflated entertainment domain (41→20 actual claims), created
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- Impact: Entertainment _map.md still references cultural-dynamics claims via wiki
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Pentagon-Agent: Leo <76FB9BCA-CC16-4479-B3E5-25A3769B3D7E>

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-06 09:11:51 -07:00

3.6 KiB

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No single governance mechanism is optimal for all decisions -- meritocratic voting for daily ops, prediction markets for medium stakes, futarchy for critical decisions creates layered manipulation resistance claim internet-finance 2026-02-16 likely Governance - Meritocratic Voting + Futarchy

optimal governance requires mixing mechanisms because different decisions have different manipulation risk profiles

The instinct when designing governance is to find the best mechanism and apply it everywhere. This is a mistake. Different decisions carry different stakes, different manipulation risks, and different participation requirements. A single mechanism optimized for one dimension necessarily underperforms on others.

The mixed-mechanism approach deploys three complementary tools. Meritocratic voting handles daily operational decisions where speed and broad participation matter and manipulation risk is low. Prediction markets aggregate distributed knowledge for medium-stakes decisions where probabilistic estimates are valuable. Futarchy provides maximum manipulation resistance for critical decisions where the consequences of corruption are severe. Since futarchy is manipulation-resistant because attack attempts create profitable opportunities for defenders, reserving it for high-stakes decisions concentrates its protective power where it matters most.

The interaction between mechanisms creates its own value. Each mechanism generates different data: voting reveals community preferences, prediction markets surface distributed knowledge, futarchy stress-tests decisions through market forces. Organizations can compare outcomes across mechanisms and continuously refine which tool to deploy when. This creates a positive feedback loop of governance learning. Since recursive improvement is the engine of human progress because we get better at getting better, mixed-mechanism governance enables recursive improvement of decision-making itself.


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