WHEN THE RED QUEEN STUMBLES: Temporal Asymmetry and the Collapse of Regulatory Coevolution

Regulatory systems exhibit Red Queen dynamics: regulators evolve rules, regulated entities evolve workarounds, prompting new rules. This coevolution assumes temporal symmetry — both parties adapt at comparable velocities. Computational acceleration has put this assumption under severe stress. When regulated entities adopt algorithmic decision-making (adaptation cycle: days) while regulators operate at legislative tempo (adaptation cycle: years), the Evolutionary Velocity Ratio (EVR) exceeds 100:1. This paper argues that EVR > 100 triggers qualitative regime change: regulatory systems cease to coevolve and instead collapse into what I term "administrative theater" — formal governance structures that cannot affect outcomes they nominally regulate.

Drawing on evolutionary biology, game theory, and institutional economics, I formalize three coevolutionary regimes (symmetric, asymmetric, collapsed) and test predictions across tax law (EVR ≈ 5), financial regulation (EVR ≈ 100), and high-frequency trading (EVR > 1000). Quantitative analysis reveals a systematic EVR-effectiveness relationship: jurisdictions with EVR > 100 exhibit regulatory arbitrage rates 15–25 times higher than matched controls, independent of regulatory stringency or resources.

The dominant reform proposal — “computational law” using AI adjudication to match regulatory velocity — eliminates rather than preserves the human agency regulation exists to protect. The most technically sophisticated version of this proposal, the JudgeAI system developed by Kozlov (2024), explicitly designs for “absolute independence” by making human influence on decision-making impossible — which is precisely what destroys the conditions for governance. I propose mandatory velocity constraints (circuit breakers, cooling periods, algorithmic speed limits) that restore coevolutionary symmetry without sacrificing innovation.