Computational Market Economics: A Theory of Allocation Under Inferential Scarcity

Computational Market Economics introduces a theoretical framework for analyzing allocation processes in computationally-mediated markets where inferential capacity, rather than price alone, becomes the primary allocative constraint. The paper formalizes the transition from ranking-based allocation under separable valuations to network-dependent allocation under inferential scarcity, where optimal selection depends on combinatorial complementarities and representation structure. A canonical allocation problem is introduced to model conditions under which computational systems cannot recover globally optimal allocations through independent ranking mechanisms. Five core primitives are developed: inferential scarcity, representational capital, inferential accessibility, computational trust, and network-dependent allocation. Three foundational theorems establish irreducibility conditions, computational hardness properties, and structural exclusion dynamics emerging from protocol-mediated participation systems. The framework extends institutional economics, mechanism design, combinatorial optimization, and computational governance theory to analyze representation protocols as allocative infrastructure. Under this perspective, protocol design acquires distributive consequences by determining participation eligibility, inferential visibility, and computational accessibility within machine-mediated markets. The paper further proposes operational metrics and empirical hypotheses suitable for future validation across AI-mediated search, recommendation systems, computational marketplaces, and digitally-mediated allocation environments. This work is released as a working paper intended for scholarly discussion, theoretical refinement, and future empirical research.