The Intelligence Form Space: Mathematical Core of a Theory of Ontological Form Spaces
The paper develops a mathematical core for Intelligence Form Space Theory, positing that intelligent organization can be described as a modal order of stably realizable forms independent of any particular physical, biological, or artificial substrate. It maps concrete realizations to relational morphological signatures represented as directed measure networks and uses Network Gromov–Wasserstein geometry to compare these signatures across independently arising systems. The resulting order is empirically testable through morphological convergence, performance gains, and predictive surplus, prov…
The paper develops a mathematical core for Intelligence Form Space Theory, positing that intelligent organization can be described as a modal order of stably realizable forms independent of any particular physical, biological, or artificial substrate. It maps concrete realizations to relational morphological signatures represented as directed measure networks and uses Network Gromov–Wasserstein geometry to compare these signatures across independently arising systems. The resulting order is empirically testable through morphological convergence, performance gains, and predictive surplus, prov…
The framework offers Swiss AI researchers a quantitative method to compare AI systems’ organizational forms, assess morphological convergence, and test for predictive surplus, thereby informing the design and evaluation of AI architectures.
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