On the Inducibility of Stackelberg Equilibrium for Security Games


  • Qingyu Guo Nanyang Technological University
  • Jiarui Gan University of Oxford
  • Fei Fang Carnegie Mellon University
  • Long Tran-Thanh University of Southampton
  • Milind Tambe University of Southern California
  • Bo An Nanyang Technological University




Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. As opposed to the weak Stackelberg equilibrium (WSE), the SSE assumes that the follower breaks ties in favor of the leader and this is widely acknowledged and justified by the assertion that the defender can often induce the attacker to choose a preferred action by making an infinitesimal adjustment to her strategy. Unfortunately, in security games with resource assignment constraints, the assertion might not be valid; it is possible that the defender cannot induce the desired outcome. As a result, many results claimed in the literature may be overly optimistic. To remedy, we first formally define the utility guarantee of a defender strategy and provide examples to show that the utility of SSE can be higher than its utility guarantee. Second, inspired by the analysis of leader’s payoff by Von Stengel and Zamir (2004), we provide the solution concept called the inducible Stackelberg equilibrium (ISE), which owns the highest utility guarantee and always exists. Third, we show the conditions when ISE coincides with SSE and the fact that in general case, SSE can be extremely worse with respect to utility guarantee. Moreover, introducing the ISE does not invalidate existing algorithmic results as the problem of computing an ISE polynomially reduces to that of computing an SSE. We also provide an algorithmic implementation for computing ISE, with which our experiments unveil the empirical advantage of the ISE over the SSE.




How to Cite

Guo, Q., Gan, J., Fang, F., Tran-Thanh, L., Tambe, M., & An, B. (2019). On the Inducibility of Stackelberg Equilibrium for Security Games. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2020-2028. https://doi.org/10.1609/aaai.v33i01.33012020



AAAI Technical Track: Game Theory and Economic Paradigms