A Robust Optimization Approach to Designing Near-Optimal Strategies for Constant-Sum Monitoring Games

Rahmattalabi, Aida; Vayanos, Phebe; Tambe, Milind

doi:10.1007/978-3-030-01554-1_35

Cited by 7 publications

(14 citation statements)

References 12 publications

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“…In [31], the authors show that for bounded polyhedral uncertainty sets, linear two-stage robust optimization problems can be approximately reformulated as MILPs. Paper [39] extends this result to a special case of discrete uncertainty sets. We prove that we can leverage this approximation to reformulate robust graph covering problem with fairness constraints exactly for a much larger class of discrete uncertainty sets.…”

Section: Introductionmentioning

confidence: 69%

“…Other works rely on partitioning the uncertainty set into finite sets and applying constant decision rules on each partition [15,17,31,38,47]. The last stream of work investigates the so-called K-adaptability counterpart [11,20,31,39,46], in which K candidate policies are chosen in the first stage and the best of these policies is selected after the uncertain parameters are revealed. Our paper most closely relates to [31,39].…”

Section: Introductionmentioning

confidence: 99%

“…The last stream of work investigates the so-called K-adaptability counterpart [11,20,31,39,46], in which K candidate policies are chosen in the first stage and the best of these policies is selected after the uncertain parameters are revealed. Our paper most closely relates to [31,39]. In [31], the authors show that for bounded polyhedral uncertainty sets, linear two-stage robust optimization problems can be approximately reformulated as MILPs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Algorithmic Fairness in Robust Graph Covering Problems

Rahmattalabi,

Vayanos,

Fulginiti

et al. 2020

Preprint

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Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on robust graph covering problems subject to group fairness constraints. We show that, in the absence of fairness constraints, state-of-the-art algorithms for the robust graph covering problem result in biased node coverage: they tend to discriminate individuals (nodes) based on membership in traditionally marginalized groups. To mitigate this issue, we propose a novel formulation of the robust graph covering problem with group fairness constraints and a tractable approximation scheme applicable to real-world instances. We provide a formal analysis of the price of group fairness (PoF) for this problem, where we show that uncertainty can lead to greater PoF. We demonstrate the effectiveness of our approach on several real-world social networks. Our method yields competitive node coverage while significantly improving group fairness relative to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Algorithmic Fairness in Robust Graph Covering Problems

Rahmattalabi,

Vayanos,

Fulginiti

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Note that every element of σ 1 corresponds to a possible pure strategy from A 1 . Since the number of pure strategies grows quickly with b 1 , we cannot directly solve (16) due to the size of decision vector. However, the number of inequality constrains is always m, which allow us to use CGP to solve (16).…”

Section: B Set Cover/set Packing Based Strategiesmentioning

confidence: 99%

A Network Monitoring Game with Heterogeneous Component Criticality Levels

Milošević

Dahan

Amin

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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We consider an attacker-operator game for monitoring a large-scale network that is comprised on components that differ in their criticality levels. In this zero-sum game, the operator seeks to position a limited number of sensors to monitor the network against an attacker who strategically targets a network component. The operator (resp. attacker) seeks to minimize (resp. maximize) the network loss. To study the properties of mixed-strategy Nash Equilibria of this game, we first study two simple instances: (i) When component sets monitored by individual sensor locations are mutually disjoint; (ii) When only a single sensor is positioned, but with possibly overlapping monitoring component sets. Our analysis reveals new insights on how criticality levels impact the players equilibrium strategies. Next, we extend a previously known approach to obtain an approximate Nash equilibrium for the general case of the game. This approach uses solutions to minimum set cover and maximum set packing problems to construct an approximate Nash equilibrium. Finally, we implement a column generation procedure to improve this solution and numerically evaluate the performance of our approach.

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“…Recently, successful applications of AI based decision aids such as [21] and [5] have encouraged efforts to address such complicated social problems using techniques in AI and optimization [15]. In the present work, we aim to tackle the problem of deviancy training in substance abuse intervention by structuring more effective groups, one that effectively partitions the participants' social network.…”

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confidence: 99%

Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)

Rahmattalabi,

Adhikari,

Vayanos

et al. 2019

Preprint

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Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups to be formed, captures the impact of the intervention groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find nearoptimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes near-optimal solutions that are shown, via extensive simulations, to significantly improve over the traditional qualitative practices for forming intervention groups; (b) GUIDE is able to identify circumstances when an intervention will lead to deviancy training, thus saving time, money, and effort; (c) GUIDE can evaluate current strategies of group formation and discard strategies that will lead to deviancy training. In developing GUIDE, we are primarily interested in substance use interventions among homeless youth as a high risk and vulnerable population. GUIDE is developed in collaboration with Urban Peak, a homelessyouth serving organization in Denver, CO, and is under preparation for deployment.

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A Robust Optimization Approach to Designing Near-Optimal Strategies for Constant-Sum Monitoring Games

Cited by 7 publications

References 12 publications

Exploring Algorithmic Fairness in Robust Graph Covering Problems

Exploring Algorithmic Fairness in Robust Graph Covering Problems

A Network Monitoring Game with Heterogeneous Component Criticality Levels

Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)

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