Risk Averse Stackelberg Security Games with Quantal Response

Chicoisne, Renaud; Ordóñez, Fernando

doi:10.1007/978-3-319-47413-7_5

Cited by 5 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the ideas in [30,10], we can iteratively make guesses about the optimal value η * with a binary search: when fixing η = η, the latter problem reduces to investigate whether there exists x ∈ X satisfying:…”

Section: Minimizing a Riskmentioning

confidence: 99%

“…These observations suggest a binary search scheme iteratively looking for the index v * ∈ V that corresponds to the true optimal value Vv * of Problem (9). We summarize the procedure in Algorithm 1 where the routine solve( v) takes as argument an index v ∈ V and returns a tuple x v , u v corresponding respectively to an optimal solution and the optimal value of Problem (11).…”

Section: Value At Riskmentioning

confidence: 99%

“…Defining R := max i∈{1,...,n} Ri, for theoretical complexity and computational tractability purposes, it is better [10] to divide by e λR both the numerator and denominator in (15): yi(x) = e λ(U i (x i )−R) / n j=1 e λ(U j (x j )−R) . Defining βi := e λ(R i −R) ⩾ 0, γi := λ(Ri − Pi) ⩾ 0 and δi := Ri − Pi ⩾ 0 we obtain that…”

Section: Algorithm 3: Improved Cvar Algorithmmentioning

confidence: 99%

“…In [10], we studied risk-neutral and risk-averse objective models that minimize either the expected value E[D(x)] or an entropic risk measure α ln E[exp(D(x)/α)]. The resulting models were able to solve instances within an hour with up to n = 10.000 targets for 1) a basic model where…”

Section: Expected Value and Entropy Minimization With Qr Adversariesmentioning

confidence: 99%

See 3 more Smart Citations

Location and Strategies in Stackelberg Security Games with Risk Aversion

Chicoisne,

Ordóñez,

Castro

2023

International Series in Operations Research &Amp; Management Science

Self Cite

View full text Add to dashboard Cite

In Stackelberg security games, a leader locates security resources to protect a set of targets from strategic adversaries that aim to attack these targets after observing the leader's strategy. In this setting, the leader decision problem is to optimize an uncertain reward that can take a discrete set of values with a probability distribution that depends on the decision variable.We show how diverse risk aversion models of the leader decision problem can be formulated as tractable optimization problems, such as imposing: a bound on the expected disutility, chance constraints, bounded distortion risk, first and second order stochastic dominance constraints, or optimizing a value-at-risk and conditional value-at-risk. We detail the resulting optimization problems and present computational results that show how the solution changes in two specific settings: 1) an entropic risk measure or value-at-risk minimization with a quantal response follower and 2) a prospect theory model with optimal follower response.

show abstract

Section: Minimizing a Riskmentioning

confidence: 99%

Section: Value At Riskmentioning

confidence: 99%

Section: Algorithm 3: Improved Cvar Algorithmmentioning

confidence: 99%

Section: Expected Value and Entropy Minimization With Qr Adversariesmentioning

confidence: 99%

See 2 more Smart Citations

Location and Strategies in Stackelberg Security Games with Risk Aversion

Chicoisne,

Ordóñez,

Castro

2023

International Series in Operations Research &Amp; Management Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…All the models considered in this paper aim to maximize an expected payoff, whereas some other models aim to minimize the

r i s k

associated with a defense strategy (Chicoisne and Ordóñez, 2016). Besides that, our models consider an adversary following the best response, whereas some models consider—among others—quantal response adversaries (McKelvey and Palfrey, 1995).…”

Section: Introductionmentioning

confidence: 99%

Modeling bluffing behavior in signaling security games

Chen

Liu

2020

Int Trans Operational Res

View full text Add to dashboard Cite

In some security domains, the defender holds more private information than the attacker, such as the authenticity of resources. A mock camera can be adopted by the defender to deter potential attackers while the attacker may see it as a real one. This provides an opportunity for a defender with fake resources to bluff, as if all resources she owns are real. We model the bluffing behavior as a signaling game where the attacker is uncertain whether the defender is strong (all resources are real) or weak (fake resources disguised as real resources). We propose a mixed integer cubic programming formulation to solve for the solution concept based on perfect Bayesian equilibrium. We compare our bluffing security game model with a model without signaling. The experiments indicate that our model outperforms the no-signaling model in terms of the defender's utility.

show abstract

Dynamic Games in Cyber-Physical Security: An Overview

Etesami

Başar

2019

Dyn Games Appl

View full text Add to dashboard Cite

Risk Averse Stackelberg Security Games with Quantal Response

Cited by 5 publications

References 12 publications

Location and Strategies in Stackelberg Security Games with Risk Aversion

Location and Strategies in Stackelberg Security Games with Risk Aversion

Modeling bluffing behavior in signaling security games

Dynamic Games in Cyber-Physical Security: An Overview

Contact Info

Product

Resources

About