Rules-as-Code promises more testable legal obligations, but it also changes what regulated firms can learn. Existing work mostly emphasizes implementation gains; the strategic gap is whether machine-readable rules make boundary search cheaper. I study that gap with a synthetic agent-based reinforcement-learning simulation that separates actual conduct near a legal threshold from proximity in the computable enforcement signal. Across 150 seed-level scenario runs, 378 common-random-number computability-sweep runs, 288 Latin-hypercube global-design runs, and a 2,880,000-row firm-period panel, computable static rules raise conduct boundary mass relative to ambiguous static rules (0.411 versus 0.367) and raise signal boundary mass more sharply (0.403 versus 0.281). Ordinary adaptive updates lower consumer harm (0.202 to 0.194) but do not reliably reduce boundary search. A budget-neutral anti-gaming design reduces conduct boundary mass by 0.032 and consumer harm by 0.025 relative to computable static rules. These are mechanism-oriented synthetic results, not estimates of real firm behavior in a jurisdiction or industry. The contribution is an estimand distinction, an inspectable ABM/RL mechanism, and a reproducible artifact showing that transparent behavioral assumptions are sufficient to generate gaming-like boundary dynamics without implying that computable regulation is inherently undesirable.

翻译：规则即代码有望使法律义务更具可测试性，但同时也改变了受监管企业的学习方式。现有研究主要关注实施层面的收益，而战略层面的空白在于：机器可读规则是否降低了边界搜索的成本。本文通过一个基于智能体的合成强化学习仿真来研究这一空白，该仿真将法律阈值附近的实际行为与可计算执法信号中的邻近性区分开来。在150次种子级场景运行、378次公共随机数可计算性扫描运行、288次拉丁超立方体全局设计运行以及一个包含2,880,000行企业-时期的面板数据中，可计算的静态规则相对于模糊静态规则提高了行为边界质量（0.411对比0.367），并更显著地提高了信号边界质量（0.403对比0.281）。常规的适应性更新降低了消费者损害（从0.202降至0.194），但未能可靠地减少边界搜索。与可计算的静态规则相比，预算中性的反钻漏洞设计将行为边界质量降低了0.032，消费者损害降低了0.025。这些是基于机制的合成结果，而非对某个司法管辖区或行业内真实企业行为的估计。其贡献在于提出了一种可估量的区分标准、一个可检验的基于智能体的强化学习机制，以及一个可复现的产物，展示了透明的行为假设足以产生类似钻漏洞的边界动态，但这并不意味着可计算监管本质上不可取。