Human Adversaries in Security Games: Integrating Models of Bounded Rationality and Fast Algorithms

Security is a world-wide concern in a diverse set of settings, such as protecting ports, airport and other critical infrastructures, interdicting the illegal flow of drugs, weapons and money, preventing illegal poaching/hunting of endangered species and fish, suppressing crime in urban areas and securing cyberspace. Unfortunately, with limited security resources, not all the potential targets can be protected at all times. Game-theoretic approaches — in the form of ”security games” — have recently gained significant interest from researchers as a tool for analyzing real-world security resource allocation problems leading to multiple deployed systems in day-to-day use to enhance security of US ports, airports and transportation infrastructure. One of the key challenges that remains open in enhancing current security game applications and enabling new ones originates from the perfect rationality assumption of the adversaries — an assumption may not hold in the real world due to the bounded rationality of human adversaries and hence could potentially reduce the effectiveness of solutions offered. My thesis focuses on addressing the human decision-making in security games. It seeks to bridge the gap between two important subfields in game theory: algorithmic game theory and behavioral game theory. The former focuses on efficient computation of equilibrium solution concepts, and the latter develops models to predict the behaviors of human players in various