Legal theory assumes all actors operate as moral agents capable of shame, reciprocity, and good-faith compliance. This assumption fails for corporations, which function as optimization algorithms: they minimize Cost(compliance) versus Cost(violation × P(detection)), immune to social enforcement mechanisms that constrain humans. I formalize this intentionality mismatch and derive the Generalized Intentionality Mismatch Theorem: any legal regime attributing moral agency (Level 3 intentionality) to optimizers (Level 1) produces five predictable failures: (1) compliance theater, where visible programs exist without behavioral change; (2) letter-vs-spirit exploitation, where technical compliance masks substantive violation; (3) social enforcement failure, where reputation shocks prove ineffective; (4) regulatory arms race, where rules proliferate without reducing violations; and (5) recidivism, where entities violate repeatedly despite escalating penalties. I illustrate these predictions using published empirical research across 12 legal domains. Key findings: ISO 14001 certification shows zero effect on toxic releases (Potoski corporations with prior violations are 2.7x more likely to reoffend (Gray environmental regulations grew 2,000% while violations declined only 20%. The pattern is systematic: 54 of 60 domain-specific predictions illustrate. The diagnosis implies different solutions than conventional approaches. For Level 1 entities: strict liability, algorithmic enforcement with P(detection) approaching unity, bright-line rules, and structural prohibitions. The contribution identifies intentionality homogeneity as a hidden assumption in legal theory, providing a framework for level-appropriate regulatory design.
Ignacio Adrian LERER (2025) studied this question.