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 game settings. More specifically, I provide: (i) the answer to the question of which of the existing models best represents the salient features of the security problems, by empirically exploring
different human behavioral models from the literature; (ii) algorithms to efficiently compute the
resource allocation strategies for the security agencies considering these new models of the adversaries; (iii) real-world deployed systems that range from security of ports to wildlife security.