Imitative Attacker Deception in Stackelberg Security Games

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/75 ◽

2019 ◽

Author(s):

Thanh Nguyen ◽

Haifeng Xu

Keyword(s):

Recent Work ◽

Side Effect ◽

Learning Algorithms ◽

Optimal Algorithms ◽

Potential Side Effect ◽

Optimal Defense ◽

Defense Strategy ◽

Game Equilibrium ◽

Security Games ◽

Attack Data

To address the challenge of uncertainty regarding the attacker’s payoffs, capabilities, and other characteristics, recent work in security games has focused on learning the optimal defense strategy from observed attack data. This raises a natural concern that the strategic attacker may mislead the defender by deceptively reacting to the learning algorithms. This paper focuses on understanding how such attacker deception affects the game equilibrium. We examine a basic deception strategy termed imitative deception, in which the attacker simply pretends to have a different payoff assuming his true payoff is unknown to the defender. We provide a clean characterization about the game equilibrium as well as optimal algorithms to compute the equilibrium. Our experiments illustrate significant defender loss due to imitative attacker deception, suggesting the potential side effect of learning from the attacker.

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Cascade targeting tumor mitochondria with CuS nanoparticles for enhanced photothermal therapy in the second near-infrared window

Biomaterials Science ◽

10.1039/d1bm00589h ◽

2021 ◽

Author(s):

Haiyan Wu ◽

Pengpeng Jia ◽

Yu Zou ◽

Jiang Jiang

Keyword(s):

Treatment Outcome ◽

Heat Generation ◽

Side Effect ◽

Photothermal Therapy ◽

Near Infrared ◽

Local Heat ◽

Potential Side Effect ◽

Improve Treatment ◽

Improve Treatment Outcome ◽

Local Heat Generation

Photothermal therapy, assisted by local heat generation using photothermal nanoparticles (NPs), is an emerging strategy to treat tumors noninvasively. To improve treatment outcome and to alleviate potential side effect on...

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Pustular Rash in Crohn’s Patient on Ustekinumab Raises Concern for Drug-Induced Paradoxical Psoriasis

Case Reports in Gastroenterology ◽

10.1159/000514952 ◽

2021 ◽

pp. 662-666

Author(s):

Mitra Barahimi ◽

Scott Lee ◽

Kindra Clark-Snustad

Keyword(s):

Side Effect ◽

De Novo ◽

Pustular Psoriasis ◽

Initial Weight ◽

Drug Induced ◽

Clinical Recurrence ◽

Potential Side Effect ◽

First Case ◽

Tnf Antagonist ◽

Subcorneal Pustular Dermatosis

We report the case of a 51-year-old male with Crohn’s disease (CD) who developed a reproducible pustular rash after ustekinumab (UST) administration. The patient first presented with a pustular rash on his hands, body, extremities, and scalp starting 5 weeks after his initial weight-based UST induction. The rash resolved spontaneously, then recurred 4 weeks after his first subcutaneous maintenance dose of UST 90 mg. Biopsy of the affected area demonstrated subcorneal pustular dermatosis (SPD). UST was discontinued and the rash resolved. Unfortunately, the patient experienced clinical recurrence of CD, and given prior failure of multiple CD medications, UST was restarted with premedication. Two weeks after UST re-induction, the rash recurred, though less severe. Given improvement in CD symptoms, UST was continued and the rash managed with topical corticosteroids. This is the first case of drug-induced SPD associated with UST. One case report has previously described de novo pustular psoriasis associated with UST in a patient with CD and enteropathic arthritis. Notably, SPD and pustular psoriasis can be histologically indistinguishable. The development of a paradoxical psoriasiform rash is thought to be one of the few dose and duration dependent side effects of TNF-antagonist therapy but has not previously been established as a side effect of UST. This case demonstrates a new potential side effect of UST.

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Selection of Optimal Defense Strategy Based on Dynamic Evolutionary Game of Incomplete Information

2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac50856.2021.9390663 ◽

2021 ◽

Author(s):

Wenting Bi ◽

Haitao Lin ◽

Liqun Zhang ◽

Wenming Huan ◽

Kexin Liu

Keyword(s):

Incomplete Information ◽

Evolutionary Game ◽

Optimal Defense ◽

Defense Strategy ◽

Selection Of

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Propagation of Economic Inequality Through Reciprocity and Reputation

Psychological Science ◽

10.1177/0956797617741720 ◽

2018 ◽

Vol 29 (4) ◽

pp. 604-613 ◽

Cited By ~ 9

Author(s):

Leor M. Hackel ◽

Jamil Zaki

Keyword(s):

Reinforcement Learning ◽

Side Effect ◽

Formal Model ◽

Economic Inequality ◽

Social Processes ◽

Social Phenomena ◽

Potential Side Effect ◽

Economic Games ◽

Broad Scale

Reciprocity and reputation are powerful tools for encouraging cooperation on a broad scale. Here, we highlight a potential side effect of these social phenomena: exacerbating economic inequality. In two novel economic games, we manipulated the amount of money with which participants were endowed and then gave them the opportunity to share resources with others. We found that people reciprocated more toward higher-wealth givers, compared with lower-wealth givers, even when those givers were equally generous. Wealthier givers also achieved better reputations than less wealthy ones and therefore received more investments in a social marketplace. These discrepancies were well described by a formal model of reinforcement learning: Individuals who weighted monetary outcomes, rather than generosity, when learning about interlocutors also most strongly helped wealthier individuals. This work demonstrates that reciprocity and reputation—although globally increasing prosociality—can widen wealth gaps and provides a precise account of how inequality grows through social processes.

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Tackling the Grand Challenge of Algorithmic Opacity Through Principled Robust Action

Morals & Machines ◽

10.5771/2747-5174-2021-1-76 ◽

2021 ◽

Vol 1 (1) ◽

pp. 76-87

Author(s):

Alexander Buhmann ◽

Christian Fieseler

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Recent Work ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Grand Challenge ◽

Negative Effects ◽

Grand Challenges ◽

Fluid Design

Organizations increasingly delegate agency to artificial intelligence. However, such systems can yield unintended negative effects as they may produce biases against users or reinforce social injustices. What pronounces them as a unique grand challenge, however, are not their potentially problematic outcomes but their fluid design. Machine learning algorithms are continuously evolving; as a result, their functioning frequently remains opaque to humans. In this article, we apply recent work on tackling grand challenges though robust action to assess the potential and obstacles of managing the challenge of algorithmic opacity. We stress that although this approach is fruitful, it can be gainfully complemented by a discussion regarding the accountability and legitimacy of solutions. In our discussion, we extend the robust action approach by linking it to a set of principles that can serve to evaluate organisational approaches of tackling grand challenges with respect to their ability to foster accountable outcomes under the intricate conditions of algorithmic opacity.

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