This chapter develops a mathematical interpretation of governance using the framework of control systems theory. Drawing on the foundational concepts of state-space representation, we model governance as a dynamic system, assuming rules, regulations, and policies as control inputs. We examine the relevance of core control-theoretic ideas, such as stability, controllability, observability, and optimality, in capturing the mechanisms of modern statecraft. By treating governments as regulators of socioeconomic systems, we highlight how mathematical modeling can inform the design of resilient and adaptive institutions. This perspective not only unifies insights from mathematics, engineering, and social science but also lays the groundwork for data-driven and computational policy analysis. To ground the theory, we present simplified models derived from real-world scenarios that illustrate the potential of this approach. Finally, we identify the absence of well-defined metrics as a fundamental obstacle to advancing quantitative research in this domain.