Browsing by Subject "game theory"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Celestina ludens: The Negotiation of Pain from Game Theory and the Phenomenology of Reading in Celestina
    (University of Valencia, Spain., 2022) Mallorquí-Ruscalleda, Enric
    Resumen: En este trabajo, en el que se recuperan algunas de mis ideas previas fundacionales sobre Celestina, parto de la teoría de juegos y la fenomenología de la lectura para analizar los diferentes juegos que organizan y estructuran el texto de Rojas (y “antiguo autor”). Esto me permite demostrar que la estrategia, negogiación y economía, siempre en relación al dolor, son fundamentales para entender el texto. Palabras clave: Celestina, teoría de juegos, fenomelogía de la lectura, dolor, juegos, estrategia, negociación, economía. Abstract: In this paper, which recovers some of my previous foundational ideas on Celestina, I use game theory and the phenomenology of reading to analyze the different games that organize and structure Rojas’s (and “old author”) text. This allows me to demonstrate that strategy, negotiation and economy, always in relation to pain, are fundamental to understanding the text.
  • Thumbnail Image
    Item
    A Correlated Equilibrium based Transaction Pricing Mechanism in Blockchain
    (IEEE, 2020-05) Hu, Qin; Nigam, Yash; Wang, Zhilin; Wang, Yawei; Xiao, Yinhao; Computer and Information Science, School of Science
    Although transaction fees are not obligatory in most of the current blockchain systems, extensive studies confirm their importance in maintaining the security and sustainability of blockchain. To enhance blockchain in the long term, it is crucial to design effective transaction pricing mechanisms. Different from the existing schemes based on auctions with more consideration about the profit of miners, we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions, which can achieve both the individual and global optimum. To avoid the computational complexity exponentially increasing with the number of transactions, we further improve the game-theoretic solution with an approximate algorithm, which can derive almost the same results as the original one but costs significantly reduced time. Experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.
  • Thumbnail Image
    Item
    Hospital Readmissions Reduction Program: An Economic and Operational Analysis
    (INFORMS, 2016-11) Zhang, Dennis J.; Gurvich, Itai; Van Mieghem, Jan A.; Park, Eric; Young, Robert S.; Williams, Mark V.; Department of Medicine, IU School of Medicine
    The Hospital Readmissions Reduction Program (HRRP), a part of the U.S. Patient Protection and Affordable Care Act, requires the Centers for Medicare and Medicaid Services to penalize hospitals with excess readmissions. We take an economic and operational (patient flow) perspective to analyze the effectiveness of this policy in encouraging hospitals to reduce readmissions. We develop a game-theoretic model that captures the competition among hospitals inherent in HRRP’s benchmarking mechanism. We show that this competition can be counterproductive: it increases the number of nonincentivized hospitals, which prefer paying penalties over reducing readmissions in any equilibrium. We calibrate our model with a data set of more than 3,000 hospitals in the United States and show that under the current policy, and for a large set of parameters, 4%–13% of the hospitals remain nonincentivized to reduce readmissions. We also validate our model against the actual performance of hospitals in the three years since the introduction of the policy. We draw several policy recommendations to improve this policy’s outcome. For example, localizing the benchmarking process—comparing hospitals against similar peers—improves the performance of the policy.
  • Thumbnail Image
    Item
    Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective
    (IEEE, 2022-07) Hu, Qin; Li, Feng; Zou, Xukai; Xiao, Yinhao; Computer and Information Science, School of Science
    An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium , we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages.
  • Thumbnail Image
    Item
    Strategic signaling for utility control in audit games
    (Elsevier, 2022-07) Chen, Jianan; Hu, Qin; Jiang, Honglu; Computer and Information Science, School of Science
    As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit game to warn visitors can improve the benefits of the defender. However, the previous approaches usually assume perfect information of the attacker, or simply concentrate on the utility of the defender. In this paper, we introduce a brand-new zero-determinant (ZD) strategy to study the sequential audit game with online signaling, which empowers the defender to unilaterally control the utility of visitors when accessing sensitive data. In addition, an optimization scheme based on the ZD strategy is designed to effectively maximize the utility difference between the defender and the attacker. Extensive simulation results show that our proposed scheme enhances the security management and control capabilities of the defender to better handle different access requests and safeguard the system security in a cost-efficient manner.
  • Thumbnail Image
    Item
    Transaction pricing mechanism design and assessment for blockchain
    (Elsevier, 2022-03) Wang, Zhilin; Hu, Qin; Wang, Yawei; Xiao, Yinhao; Computer and Information Science, School of Science
    The importance of transaction fees in maintaining blockchain security and sustainability has been confirmed by extensive research, although they are not mandatory in most current blockchain systems. To enhance blockchain in the long term, it is crucial to design effective transaction pricing mechanisms. Different from the existing schemes based on auctions with more consideration about the profit of miners, we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions, which can achieve both the individual and global optimum. To avoid the computational complexity exponentially increasing with the number of transactions, we further improve the game-theoretic solution with an approximate algorithm, which can derive almost the same results as the original one but costs significantly reduced time. We also propose a truthful assessment model for pricing mechanism to collect the feedback of users regarding the price suggestion. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.