This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.
Abstract: Recently, there has been an increase of interest in domains involving repeated interactions between defenders and adversaries. This has been modeled as a repeated Stackelberg Security Game (repeated SSG). Although different behavioral models have been proposed for the attackers in these games, human subjects experiments for testing these behavioral models in repeated SSGs have not been conducted previously. This paper presents the first “longitudinal study” – at least in the context of SSGs – of testing human behavior models in repeated SSG settings. We provide the following contributions in this paper. First, in order to test the behavioral models, we design a game that simulates the repeated interactions between the defender and the adversary and deploy it on Amazon Mechanical Turk (AMT). Human subjects are asked to participate in this repeated task in rounds of the game, with a break between consecutive rounds. Second, we develop several approaches to keep the human subjects motivated throughout the course of this longitudinal study so that they participate in all measurement occasions, thereby minimizing attrition. We provide results showing improvements of retention rate due to implementation of these approaches. Third, we propose a way of choosing representative payoffs that fit the real-world scenarios as conducting these experiments are extremely time-consuming and we can only conduct a limited number of such experiments.
Keywords: Game Theory, Human Behavior Models, Repeated Stackelberg Games, Longitudinal Experiments, Amazon Mechanical Turk.
Citation: Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov and Milind Tambe. Conducting Longitudinal Experiments with Behavioral Models in Repeated Stackelberg Security Games on Amazon Mechanical Turk.