New Exact Solutions for the (3+1)-Dimensional Generalized BKP Equation

The Wronskian technique is used to investigate a (3+1)-dimensional generalized BKP equation. Based on Hirota’s bilinear form, new exact solutions including rational solutions, soliton solutions, positon solutions, negaton solutions, and their interaction solutions are formally derived. Moreover we analyze the strangely mechanical behavior of the Wronskian determinant solutions. The study of these solutions will enrich the variety of the dynamics of the nonlinear evolution equations

Matrix approach to the Shapley value and dual similar associated consistency

Replacing associated consistency in Hamiache's axiom system by dual similar associated consistency, we axiomatize the Shapley value as the unique value verifying the inessential game property, continuity and dual similar associated consistency. Continuing the matrix analysis for Hamiache's axiomatization of the Shapley value, we construct the dual similar associated game and introduce the dual similar associated transformation matrix $M_\lambda^{DSh}$ as well. In the game theoretic framework we show that the dual game of the dual similar associated game is Hamiache's associated game of the dual game. For the purpose of matrix analysis, we derive the similarity relationship $M_\lambda^{DSh}=QM_\lambda Q^{-1}$ between the dual similar associated transformation matrix $M_\lambda^{DSh}$ and associated transformation matrix $M_\lambda$ for Hamiache's associated game, where the transformation matrix $Q$ represents the duality operator on games. This similarity of matrices transfers associated consistency into dual similar associated consistency, and also implies the inessential property for the limit game of the convergent sequence of repeated dual similar associated games. We conclude this paper with three tables summarizing all matrix results

Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism

Compromise for the complaint:an optimization approach to the ENSC value and the CIS value

The main goal of this paper is to introduce a new solution concept: the optimal compromise value. We propose two kinds of complaint criteria based on which the optimistic complaint and the pessimistic complaint are defined. Two optimal compromise values are obtained by lexicographically minimizing the optimistic maximal complaint and the pessimistic maximal complaint, respectively. Interestingly, these two optimal compromise values coincide with the ENSC value and the CIS value, respectively. Moreover, these values are characterized in terms of equal maximal complaint property and efficiency. As an adjunct, we reveal the coincidence of the Nucleolus and the ENSC value of 1-convex games

Matrix approach to cooperative game theory

In this monograph, the algebraic representation and the matrix approach are applied to study linear operators on the game space, more precisely, linear transformations on games and linear values. In terms of the essential notion of a coalitional matrix, these linear operators are represented algebraically by products of the corresponding coalitional matrix and the worth vector (representing the game). We perform a matrix analysis in the setting of cooperative game theory, to study axiomatizations of linear values, by\ud investigating appropriate properties of these representation matrices. Particularly, the Shapley value is the most important representative. In summary, the concepts of eigenvalues, eigenvectors, null space, the diagonalization procedure and the similarity property for matrices, the system of linear equations and its solution set, the Moebius transformation\ud and the complementary Moebius transformation, the basis for a linear space and so on, can be applied successfully to cooperative game theory. We conclude that the matrix analysis is a new and powerful technique for research in the eld of cooperative game theory

Matrix approach to dual similar associated consistency for the Shapley value

In terms of the similarity of matrices, by combining the dual operator and the linear mapping with respect to Hamiache’s associated game on the game space, the Shapley value for TU-games is axiomatized as the unique value verifying dual similar associated consistency, continuity, and the inessential game property.\ud \u

Maximizing the Minimal Satisfaction—Characterizations of Two Proportional Values

A class of solutions are introduced by lexicographically minimizing the complaint of coalitions for cooperative games with transferable utility. Among them, the nucleolus is an important representative. From the perspective of measuring the satisfaction of coalitions with respect to a payoff vector, we define a family of optimal satisfaction values in this paper. The proportional division value and the proportional allocation of non-separable contribution value are then obtained by lexicographically maximizing two types of satisfaction criteria, respectively, which are defined by the lower bound and the upper bound of the core from the viewpoint of optimism and pessimism respectively. Correspondingly, we characterize these two proportional values by introducing the equal minimal satisfaction property and the associated consistency property. Furthermore, we analyze the duality of these axioms and propose more approaches to characterize these two values on basis of the dual axioms