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Browsing by Subject "Game theory"
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Item Economía y juego en Celestina.(2010) Mallorquí-Ruscalleda, EnricIn this work, I explore the “deep structure” inherent in the text of Celestina. By “deep structure” I mean a playful structure with which we can systemize the relationships that occur between the characters and their decision-making, especially between Celestina and Calisto. My approach to this thesis is interdisciplinary; more specifically, I apply the critical claims offered by modern game theory and sociology, with the intention of bringing to light an aspect of the work which has been up to now largely disregarded by Celestina critics: the role of games. This analysis is accomplished while keeping in mind the political, sociocultural and economic background of the times in which the text is situated.Item Social Welfare Maximization in Cross-Silo Federated Learning(IEEE, 2022-05-23) Chen, Jianan; Hu, Qin; Jiang, Honglu; Computer and Information Science, School of ScienceAs one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To over-come this social dilemma, we employ the Multi-player Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Experimental results validate that the MMZD strategy is effective in maximizing the social welfare.Item A Trustworthy Human–Machine framework for collective decision making in Food–Energy–Water management: The role of trust sensitivity(Elsevier, 2021-02) Uslu, Suleyman; Kaur, Davinder; Rivera, Samuel J.; Durresi, Arjan; Babbar-Sebens, Meghna; Tilt, Jenna H.; Computer and Information Science, School of ScienceWe propose a hybrid Trustworthy Human–Machine collective decision-making framework to manage Food–Energy–Water (FEW) resources. Decisions for managing such resources impact not only the environment but also influence the economic productivity of FEW sectors and the well-being of society. Therefore, while algorithms can be used to develop optimal solutions under various criteria, it is essential to explain such solutions to the community. More importantly, the community should accept such solutions to be able realistically to apply them. In our collaborative computational framework for decision support, machines and humans interact to converge on the best solutions accepted by the community. In this framework, trust among human actors during decision making is measured and managed using a novel trust management framework. Furthermore, such trust is used to encourage human actors, depending on their trust sensitivity, to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. In this paper, we show different scenarios of decision making with continuous and discrete solutions. Then, we propose a game-theory approach where actors maximize their payoff regarding their share and trust weighted by their trust sensitivity. We run simulations for decision-making scenarios with actors having different distributions of trust sensitivities. Results showed that when actors have high trust sensitivity, a consensus is reached 52% faster than scenarios with low trust sensitivity. The utilization of ratings of ratings increased the solution trustworthiness by 50%. Also, the same level of solution trustworthiness is reached 2.7 times faster when ratings of ratings included.