Foundations of Strategic Computing in AI Systems: OS2x2 Whitepaper (v3.4)

This whitepaper introduces Strategy Knowledge Science (SKS) as a formal framework for representing strategic environments as computable state-spaces. It presents the OS2x2 architecture as a strategic computing system for positioning, feasibility analysis, trajectory computation, verified strategic decision-making, continuous strategic memory, and strategic learning over time. The paper defines the core layers of strategic computation — Strategic Geometry, Strategic Algebra, Strategic Mechanics, Strategic Topology, and Strategic Field Theory — and shows how they are translated into an applied computational architecture for deployment, runtime computation, graph-based strategic memory, and validation. It also introduces the Strategy Knowledge Model (SKM) as a new class of strategic-native artificial intelligence aligned with strategic state-spaces, constraints, and trajectories rather than linguistic plausibility alone, and extends the framework into financial markets through Trading Strategy Knowledge (TSK). The paper introduces Strategy Knowledge Reality (SKR) as the protocol by which real-world domains are projected into strategically legible form. Under SKR, domains are no longer treated as unconstrained narrative topics, but as structured environments of coordinates, regimes, field gradients, friction, and transition logic. This same logic extends into user-facing access through Ask Strategy Knowledge (ASK), the unified service layer through which users can query strategically encoded domains, receive structured answers, and, when needed, continue into persistent strategic navigation. It is further extended through Expert Strategy Knowledge (ESK), the expert analytic layer for security audit, structural review, architectural diagnosis, and optimization of complex agentic and strategic systems. This document serves as the theoretical and architectural foundation of the OS2x2 platform and the broader category of computed strategy. What’s new in v3.4 introduced Multi-Agent and Institutional SKS as an extension of SKS for modeling interacting actors, delegated authority, collective regimes, semantic chains, and institutional fields as structured strategic ecologies rather than isolated decision nodes introduced the Principle of Strategy Knowledge Relativity, according to which strategic position, motion, and feasibility are relative to the coordinate frame, dimensional resolution, field structure, and mechanical parameters through which the environment is represented formalized the SKS Correspondence Principle, showing how established models such as BCG, Porter, Game Theory, and control/RL can be recovered as constrained local charts, static slices, or special cases within a broader computable strategic manifold Website: https://os2x2.com