  It has become commonplace to assert that autonomous agents will have to be
built to follow human rules of behavior--social norms and laws. But human laws
and norms are complex and culturally varied systems, in many cases agents will
have to learn the rules. This requires autonomous agents to have models of how
human rule systems work so that they can make reliable predictions about rules.
In this paper we contribute to the building of such models by analyzing an
overlooked distinction between important rules and what we call silly
rules--rules with no discernible direct impact on welfare. We show that silly
rules render a normative system both more robust and more adaptable in response
to shocks to perceived stability. They make normativity more legible for
humans, and can increase legibility for AI systems as well. For AI systems to
integrate into human normative systems, we suggest, it may be important for
them to have models that include representations of silly rules.
