@conference {1505635,
	title = {Improved Computational Models of Human Behavior in Security Games},
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (Extended Abstract)},
	year = {2011},
	abstract = {Security games refer to a special class of attacker-defender
Stackelberg games. In these non zero-sum games, the attacker{\textquoteright}s utility of attacking a target decreases as the defender
allocates more resources to protect it (and vice versa for the
defender). The defender (leader) first commits to a mixed
strategy, assuming the attacker (follower) decides on a pure
strategy after observing the defender{\textquoteright}s strategy. This models the situation where an attacker conducts surveillance to
learn the defender{\textquoteright}s mixed strategy and then launches an
attack on a single target. Given that the defender has limited resources, she must design her mixed-strategy optimally
against the adversaries{\textquoteright} response to maximize effectiveness.
One leading family of algorithms to compute such mixed
strategies are DOBSS and its successors [3, 5], which are
used in the deployed ARMOR [5] and IRIS [8] applications. One key set of assumptions these systems make is about
how attackers choose strategies based on their knowledge
of the security strategy. Typically, such systems apply the
standard game-theoretic assumption that attackers are perfectly rational. This is a reasonable proxy for the worst case
of a highly intelligent attacker, but it can lead to a defense
strategy that is not robust against attackers using different
decision procedures, and it fails to exploit known weaknesses in human decision-making. Indeed, it is widely accepted
that standard game-theoretic assumptions of perfect rationality are not ideal for predicting the behavior of humans in
multi-agent decision problems [1]. Thus, integrating more realistic models of human decision-making has become necessary in solving real-world security problems.
The current leading contender that accounts for human
behavior in security games is COBRA [6], which assumes
that adversaries can deviate to -optimal strategies and
that they have an anchoring bias when interpreting a probability distribution. It remains an open question whether
other models yield better solutions than COBRA against
human adversaries. The literature has introduced a multitude of candidate models, but there is an important empirical question of which model best represents the salient
features of human behavior in applied security contexts.
We address these open questions by developing three new
algorithms to generate defender strategies in security games,
based on using two fundamental theories of human behavior to predict an attacker{\textquoteright}s decision: Prospect Theory (PT)
[2] and Quantal Response Equilibrium (QRE) [4]. PT is a
noble-prize-winning theory, which describes human decision
making as a process of maximizing {\textquoteleft}prospect{\textquoteright}. {\textquoteleft}Prospect{\textquoteright}
is defined as the weighted sum of the benefit of all possible outcomes for each action. QRE suggests that instead of
strictly maximizing utility, individuals respond stochastically in games: the chance of selecting a non-optimal strategy
increases as the cost of such an error decreases.},
	author = {Yang, Rong and Kiekintveld, Christopher and Ordonez, Fernando and Tambe, Milind and Richard John}
}
