The growing complexity of our regulatory environment presents us with a hard problem: how can we determine if we are compliant with an ever-growing body of regulations? Computational legal auditing may help, as computational tools are exceptionally good at making sense of large amounts of data. In this research, we explore the possibility of creating a computational auditor that checks if mobile health (mHealth) apps satisfy federal security and privacy regulations. In doing so, we find that while it is challenging to convert open-ended, generally applicable, complicated laws into computational principles, the use of non-legal, authoritative, explanatory documents allows for computational operationalization while preserving the open-ended nature of the law. We test our auditor on 182 FDA/CE-approved mHealth apps. Our research suggests that the use of non-legal, authoritative, guidance documents may help with the creation of computational auditors, a promising tool to help us manage our ever-growing regulatory responsibilities.

A simple game provides a framework within which agents can spontaneously self-organize. In this paper, we present this game, and develop basic theory underlying a robust method for distributed coordination based on this game. This method makes use of finite state automata-one associated with each agent-which guide the agents. We give a new, general method of analysis of these systems, which previously had been studied only in limited cases. We also provide a physical example, which should hint at the type of problems resolvable using this method.

The distributed system is becoming increasingly popular, and this produces the need for more sophisticated distributed control techniques. The authors present a method for distributed control using simple finite state automata. Each of the distributed entities is 'controlled' by its associated automaton, in the sense that the entity examines the state of the automaton to determine its behavior. The result of the collective behavior of all of the entities is fed back to the automata, which change their state as a result of this feedback. They give a new method of analysis which derives the steady state behavior of this system as a whole, by decomposing it into two parts: describing and solving an imbedding auxiliary Markov chain, and analyzing the behavior of the system within each of the states of this auxiliary chain.>