Security is a critical concern around the world, whether it is the challenge of protecting ports, airports and other critical national infrastructure, or protecting wildlife/forests and fisheries, or suppressing crime in urban areas. In many of these cases, limited security resources prevent full security coverage at all times. Instead, these limited resources must be allocated and scheduled efficiently, avoiding predictability, while simultaneously taking into account an adversary's response to the security coverage, the adversary's preferences and potential uncertainty over such preferences and capabilities.
Computational game theory can help us build decision-aids for such efficient security resource allocation. Indeed, casting the security allocation problem as a Bayesian Stackelberg game, we have developed new algorithms that are deployed over multiple years in multiple applications including:
Fundamentally, we are focused on the research challenges in these efforts, marrying these applications with research on topics such as (i) fast algorithms for solving massive-scale games; (ii) behavioral game theory research for addressing human adversaries who may act with bounded rationality and imperfect observations; (iii) understanding the impact of players' limited observations on solution approaches adopted.