The Normative Calculation Problem Under Heterogeneous Intentionality: Why Effective Enforcement Requires Price Mechanisms

Legal systems invest massive resources in enforcement, yet corporate compliance remains low and recidivism high. I argue this puzzle is not solvable by increasing sanctions or enforcement resources. The problem is architectural: enforcement mechanisms designed for human moral agents (Level 3 intentionality) fail when applied to optimization algorithms embodied in corporate form (Level 1 intentionality). This paper connects two previously separate literatures: Asymmetric Intentionality Theory, which diagnosed the structural mismatch between legal design and corporate cognition, and the Normative Calculation Problem, which explains why legal systems fail to generate the information necessary for rational compliance calculation. I argue that enforcement mechanisms requiring common knowledge, shame, or reciprocity systematically fail against Level 1 entities. The solution is not more enforcement of the same type, but enforcement mechanisms that operate on verifiable outputs rather than inferred intentions. I examine algorithmic enforcement as a directional solution, document its limitations (Goodhart's Law, capture risk, rigidity), and propose hybrid mechanisms combining algorithmic verification with human oversight for ambiguous cases. The analysis has implications for mechanism design, AI governance, and legal reform in contexts where heterogeneous intentionality is unavoidable.