Multicriteria analysis in decision making under information uncertainty

This paper presents results of research related to multicriteria decision making under information uncertainty. The Bellman–Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models (X,M models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called X,R models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example.     

模糊支付矩阵混合策略模型及其求解

在各局中人没有最优纯策略时,考虑选取不同策略的概率分布和支付矩阵的模糊性,建立模糊支付混合策略的对策模型.基于线性规划和三角模糊数排序准则,给出模型求解方法.模型在实例中得到验证,其计算简单,结果合理,可以为决策提供科学依据.     

Computing payoff allocations in the approximate core of linear programming games in a privacy-preserving manner

We investigate privacy-preserving ways of allocating payoffs among players participating in a joint venture, using tools from cooperative game theory and differential privacy. In particular, we examine linear programming games, an important class of cooperative games that model a myriad of payoff sharing problems, including those from logistics and network design. We show that we can compute a payoff allocation in the approximate core of these games in a way that satisfies joint differential privacy.     

Adjusting learning motivation to promote cooperation

An evolutionary prisoner’s dilemma game with players adjusting their learning motivation is studied. At each time step, each player can adjust his/her learning motivation according to the difference between the current payoff and payoff aspiration. Greater payoff aspiration means stronger learning motivation, and vice versa. We find that the density of cooperation in a spatial prisoner’s dilemma game is enhanced when the learning motivation mechanism is considered. Meanwhile, we show that proper noise can not only induce the highest cooperation level but also can maintain the cooperation phenomenon even though there is more temptation to defect.     

Natural games

A course of a game is formulated as a physical process that will consume free energy in the least time. Accordingly, the rate of entropy increase is the payoff function that will subsume all forms of free energy that motivate diverse decisions. Also other concepts of game theory are related to their profound physical counterparts. When the physical portrayal of behavior is mathematically analyzed, the course of a game is found to be inherently unpredictable because each move affects motives in the future. Despite the non-holonomic character of the natural process, the objective of consuming free energy in the least time will direct an extensive-form game toward a Lyapunov-stable point that satisfies the minimax theorem.     

收益模糊合作对策Shapley值的公理化

研究一类收益模糊的合作对策,这类对策联盟的模糊收益值可以用一个闭区间的形式来表示,本文定义了一个拓展的闭区间空间和一些闭区间线性运算算子,证明了这类对策的Shapley值可以用承载性、可替代性和可加性进行了公理化.     

允许卖空的log-最优资产组合投资模型

本文研究了允许卖空的离散时间金融市场，在有风险控制和无风险的条件下，当每一周期的收益向量相互独立（可不同分布）时，分别得到关于log-最优资产组合的几个性质。     

Stability and Shapley value for an n-persons fuzzy game

The aim of the paper is to explain new concepts of solutions for n-persons fuzzy games. Precisely, it contains new definitions for ‘core’ and ‘Shapley value’ in the case of the n-persons fuzzy games. The basic mathematical results contained in the paper are these which assert the consistency of the ‘core’ and of the ‘Shapley value’. It is proved that the core (defined in the paper) is consistent for any n-persons fuzzy game and that the Shapley values exists and it is unique for any fuzzy game with proportional values.     

The evolution of cooperation within the multigame environment based on the Particle Swarm Optimization algorithm

This paper studies the evolution of cooperation in a so-called multigame environment based on the Particle Swarm Optimization (PSO) algorithm. In a multigame environment, players use different game payoff matrices and acquire their utilities from their interactions with their neighbors. According to the PSO algorithm, each player updates its strategy according to both the strategy adopted by the player with the highest utility in its neighborhood and the most profitable strategy in their own past actions. Simulation results show that the multigame environment is conducive to the promotion of cooperation. Besides, within the multigame environment, for any player, imitating the most profitable strategy in its past actions promotes cooperation more effectively than imitating the strategy adopted by the player with the highest utility in its neighborhood.     

A strict partial order on payoff configurations and its some properties

This paper proposes a method for comparison of payoff configurations in the framework of characteristic function form games with non-transferable utility. The proposed method in this paper is a relation derived from objections and counter-objections. Some examples which show how the proposed method works are given. This paper presents propositions which show that the proposed method satisfies the properties, called strict partial order and independence from linear transformation and parallel shift. An example verifies that the proposed method does not satisfy the property called monotonicity.