kaal:position:2026-07-31-7709

Quantifier Learning: An Agent-based Coordination Model presents the following source proposition: We discuss the possibility of extending the model to cover the parameter of spatial separation. This proposition is pertinent to Kaal's source-bound claim that In Neoclassical analysis the action space is a parameter of the model and the agent is a chooser within it, whereas in Computative Economics the action space is an output of the agent and the agent is a generator over it; every operational difference between the two architectures follows from this parameter versus output distinction. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.

• The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.
• External evidence level: abstract indexed.
• Mapping review tier: moderate-confidence claim review.
• Primary mapping confidence: 0.3522.
• The source-to-claim mapping remains explicitly ambiguous and is published with that limitation.

Quantifier Learning: An Agent-based Coordination Model

kaal:claim:6655138-004
Wulf A. Kaal, Possibility Loops An Operational Architecture for Computative Economics in Agent Coordination Systems (2026). SSRN: https://ssrn.com/abstract=6655138
Source PDF sha256: 52cb210167f06f0c7a2581214b156326216543de80b8394320b629c045c9e214

Evidence: abstract indexed
Review tier: moderate-confidence claim review
Mapping confidence: 0.3522
Mapping ambiguous: true

ai-and-agentshistorical-responsescholarly-literaturecrossref

Affirmed in kaal-review:2026-07-31:streaming-etl-0010 on 2026-07-31. Review record.

Canonical markdown sha256: 8330e24889a2d491808cf07bc9f7fc91ce7a4cd4eefc357a2b61705d040d08a7
curl -s https://wulfkaal.github.io/positions/2026-07-31-7709.md | sha256sum