This paper considers the problem of estimating the distribution of payoffs in a discrete dynamic game, focusing on models where the goal is to learn about the distribution of firms' entry and exit
In this era of overabundant information and content, people increasingly rely on recommender systems to identify those information items that best meet their needs and interests. Movie recommender systems, like the one used by Netflix, attempt to predict which films a given person will enjoy watching. While these systems help single individuals making decisions, they provide limited support for groups of people. This work explores how to create recommender systems for groups that can combine multiple user profiles and predict which movies a group of users will collectively enjoy the most. We built a prototype using Netflix REST API based on the results of a formative study of the watching habits of 60 actual Netflix users and examined their views of how a group recommendation system would fit in with their current habits. We conducted a preliminary evaluation with a focus group which validated our approach to group recommendations, revealing that this type of system could facilitate social interactions by sparking discussion about movies, directors, and actors among viewers. This prototype provides a valuable platform for further exploring group decision making in this context.
To enable fast and accurate models of SiC MOS-FETs for transient simulation, a hybrid data-driven modeling methodology of SiC MOSFETs is proposed. Unlike conventional modeling methods that are based on complex nonlinear equations, data-driven Artificial Neural Networks (ANNs) are used in this paper. For model accuracy, the I-V characteristics are measured in the whole operation region to train the ANN. The ANN model is then combined with behavior-based equations to model the cutoff region and to avoid overfitting the ANN. In addition, the C-V characteristics are modeled by ANNs with a logarithmic scale for accuracy. The proposed model is implemented and simulated in SPICE simulator SIMetrix. The simulation results are compared with experimental results from a double-pulse tester to validate the proposed modeling methodology. The model is also compared with the Angelov model created by the Keysight MOSFET modeling software. The comparison results show that the proposed model is more accurate than the Angelov model. Besides, when compared to the Angelov model, the proposed model requires 30% less computation time when simulating a double pulse tester. In addition, the proposed modeling method also has better adaptability to model different types of SiC MOSFETs.Index Terms-SiC MOSFET, transient model, hybrid modeling, artificial neural network.
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