Security has been an important, world-wild concern over the past decades. Security agencies
have been established to prevent different types of crimes in various domains, such as illegal
poaching, human trafficking, terrorist attacks to ports and airports, and urban crimes.
Unfortunately, in all these domains, security agencies have limited resources and cannot protect
all potential targets at all time. Therefore, it is critical for the security agencies to allocate their
limited resources optimally to protect potential targets from the adversary.
Recently, game-theoretic decision support systems have been applied to assist defenders (e.g.
security agencies) in allocating and scheduling their limited resources. Stackelberg Security Game
(denoted as SSG), is an example of a game-theoretic model that has been deployed to assign the
security resources to the potential targets. Indeed, decision-support systems based on SSG models
have been successfully implemented to assist real-world security agencies in protecting critical
infrastructure such as airports, ports, or suppressing crime in urban areas. SSG provides an
approach for generating randomized protection strategies for the defender using a mathematical
representation of the interaction between the defender and the attacker. Therefore, one of the key
steps in applying the SSG algorithm to real-world security problems is to model adversary
Building upon the success of SSGs applications, game theory is now being applied to adjacent
domains such as Opportunistic Security. In this domain, the defender is faced with adversaries
with special characteristics. Opportunistic criminals carry out repeated, and frequent illegal
activities (attacks), and they generally do not conduct extensive surveillance before performing an
attack and spend less time and effort in planning each attack. To that end, in my thesis, I focus on modeling the opportunistic criminals’ behavior in which
modeling adversary decision-making process is particularly crucial to develop efficient patrolling
strategies for the defenders. I provide an empirical investigation of adversary behavior in
opportunistic crime settings by conducting extensive human subject experiments and analyzing
how participants are making their decisions to create adversary behavior prediction models to be
deployed in many opportunistic crime domains. More specifically, this thesis provides (i) a
comprehensive answer to the question that “which of the proposed human bounded rationality
models best predicts adversaries’ behavior in the Opportunistic Crime domain?”, (ii) enhanced
human behavior models which outperform existing state-of-the-art models (iii) a detailed
comparison between human behavior models and well-known Cognitive Science model: InstanceBased Learning model (iv) an extensive study on the heterogeneity of adversarial behavior, and
(v) a thorough study of human behavior changing over time, (vi) as well as how to improve human
behavior models to account for the adversaries’ behavior evolve over time.