The Cat and Mouse of Getting Around the Law

Actors in certain fields of regulation—tax shelters, payday lending, cybersecurity—play a costly game of cat-and-mouse. When a new regulation is enacted, those who are regulated quickly innovate to avoid complying, and in turn, regulators must enact a new regulation. Payday lenders, for example, have responded to regulation by affiliating with Indian tribes to claim immunity from regulation. Drawing from work on drug resistance, we develop a non-equilibrium population dynamics model of regulatory resistance, with an eye towards advising regulators about how to optimize the speed and strength of regulation responding to innovators. Counterintuitively, we find that in certain conditions, a regulator’s best option is to weaken or slow the regulatory response to an emerging workaround. If coupled with a fast response, weakening regulation can reduce the incentive for actors to innovate. Likewise, if coupled with a strong response, slowing the regulatory response can optimally extend the duration of each regulation and still limit the evolution of resistance. These findings demonstrate that math modeling can help legal policymakers and regulators to better understand and take into account the downstream dynamic consequences of their choices and to optimize accordingly.