Improving resource allocation strategies against human adversaries in security games: An extended study

Abstract

Artículo de publicación ISIStackelberg games have garnered significant attention in recent years given their deployment for real world security. Most of these systems, such as ARMOR, IRIS and GUARDS have adopted the standard game-theoretical assumption that adversaries are perfectly rational, which is standard in the game theory literature. This assumption may not hold in real-world security problems due to the bounded rationality of human adversaries, which could potentially reduce the effectiveness of these systems. In this paper, we focus on relaxing the unrealistic assumption of perfectly rational adversary in Stackelberg security games. In particular, we present new mathematical models of human adversaries’ behavior, based on using two fundamental theory/method in human decision making: Prospect Theory (PT) and stochastic discrete choice model. We also provide methods for tuning the parameters of these new models. Additionally, we propose a modification of the standard quantal response based model inspired by rank-dependent expected utility theory. We then develop efficient algorithms to compute the best response of the security forces when playing against the different models of adversaries. In order to evaluate the effectiveness of the new models, we conduct comprehensive experiments with human subjects using a web-based game, comparing them with models previously proposed in the literature to address the perfect rationality assumption on part of the adversary. Our experimental results show that the subjects’ responses follow the assumptions of our new models more closely than the previous perfect rationality assumption. We also show that the defender strategy produced by our new stochastic discrete choice model outperform the previous leading contender for relaxing the assumption of perfect rationality. Furthermore, in a separate set of experiments, we show the benefits of our modified stochastic model (QRRU) over the standard model (QR)

When Will Negotiation Agents Be Able to Represent Us? The Challenges and Opportunities for Autonomous Negotiators

“…People adopt a variety of interpretations as to what is fair and negotiations often involve disputes over which principle to apply [Welsh, 2003]. For example, in the context of organ donation, the equity principle would allocate resources on the basis of ability, effort or merit, the equality rule would treat individuals the same, whereas the principle of need is usually achieved by allocating according to individuals medical condition, socioeconomical status or other relevant needs.…”

“…This approach is therefore called symmetrically prescriptive [Sebenius, 1992]. The focus is on either equilibrium strategies or protocols that can guarantee a good outcome for both players through mechanism design [Young, 1991]. Agents have a reduced scope for self-directedness in such settings, as they are relatively simple and need to conform to certain strategies (e.g.…”

“…Another approach is to incorporate modest psychological extensions to rational methods. For example, framing effects can be handled through the use of prospect theory (e.g., [Yang et al, 2011]). …”

“…Whenever actors meet and influence each other to forge a mutually beneficial agreement, a form of negotiation is at work [Young, 1991].…”

Planning and learning in security games

“…In essence, three types of algorithms were developed: (i) scalable algorithms Jain et al 2013]; (ii) algorithms for games with boundedly rational attackers [Yang et al 2011;Nguyen et al 2013]; and (iii) algorithms robust against execution and observation uncertainty [Yin et al 2011;.…”