Modeling Human Bounded Rationality in Opportunistic Security Games


Yasaman Dehghani Abbasi. 2016. “Modeling Human Bounded Rationality in Opportunistic Security Games ”. Thesis Type: PhD thesis.


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 decision-making process. 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.
See also: 2016