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Dynamics of Adversarial Co-evolution in Tax Non-Compliance Detection

In cyber security there are adversarial relationships, e.g. spammers versus spam-filter. A similar arms-race exists for tax non-compliance. The ability of individuals to exploit gaps in the "attack surface" of tax regulation reduces trust in governmental equity, drains money from public goods and reduces productivity. This relationship between utility-maximizing tax evaders and an increasingly resource constrained auditing system is codependent. We present a framework that allows a modest but representative area of US partnership taxation activity to be explored as an adversarial coevolutionary relationship. In this setting tax evaders try to minimize tax by exploiting loopholes in tax code inconsistencies and gaps. Reciprocally, auditors, with constrained resources, try to pinpoint strategies that shelter evasion. The framework relies upon a) a representation of the relevant partnership tax law b) a simulation of the auditing, tax calculation and compliance checking processes and c) co-optimizing taxpayer and auditor behavior via dual genetic algorithms that in turn model their coevolutionary dynamics.