Design of Hybrid Human~AI Agent Organizations: A Mathematical Framework for Organizational Dynamics

This work develops a mathematically closed, fully identifiable, and empirically falsifiable theory of hybrid Human–AI organizational design. The research introduces a unified probabilistic representation in which classical and AI-native organizational dimensions are embedded into a 17-dimensional latent space and mapped to a Fisher-geometric probability simplex through a softmax transformation. Measurement is formalized through RKHS-bounded feature maps, dynamics evolve on latent space via controlled stochastic differential equations, and governance is encoded through a Kullback–Leibler potential with strict dissipation. The resulting system forms a controlled stochastic manifold with explicit information geometry and guarantees convergence to a governance-optimal equilibrium. This is A blend of Highest Level of Management with Great level of Maths