在重复囚徒困境博弈中，同样结构的博弈被反复地进行，参与者的最佳策略选择主要依赖于对手可能采取的策略和他们对抵赖和坦白如何作出选择。有限次重复博弈在决策行动时一般使用逆向归纳法，即从重复博弈的最后一个回合开始往前推理从而决定每一步的选择。在无限次重复博弈中，对于任何一个参与者的欺骗和违约行为，其他参与者总会有机会给予报复。

Since the game with same structure is carried out repeatedly in the repeated prisoner''s dilem-ma game, the best strategy of participants mainly depends on the strategy that the opponent may adopt and their choice to denial and frank. Finite repeated game use generally backward induction in the decision-making ac-tion, namely from the last round of repeated game started forward reasoning to determine every step of the se-lection. In the repeated infinitely game, the other participants will have opportunity to give revenge for any one participant fraud and breach.

比较收益影响烟农种植意愿,烟草公司通过引入政府强制,实现种烟博弈均衡,但烟农利益、政府声誉受损,多次重复博弈的均衡结果发生改变;机会主义导致虚假烟叶合同、不按合同交售,烟草公司、政府通过惩戒等承诺行动影响烟农期望收益,在多次重复博弈中实现合作。但重复博弈的声誉机制不会自我实施,问题的解决需要其他方式的配合,提高种烟比较收益,提高烟农博弈地位,保障烟农切身利益。

Comparative profits affect farmers''decision on tobacco growing .By introducing government enforcement ,the tobacco company achieves game equilibrium in tobacco growing ,but this impairs famers''interest and the government''s reputation ,thereby changing the equilibrium result of the repeated game . The opportunism leads to false tobacco contract and default tobacco sales .Through the commitment such as disciplinary action ,the government and tobacco companies influence the farmers''expected profits and a-chieve cooperation in the repeated game .The root cause of relevant problems is that the vital interest of farmers is not guaranteed .

粒子群算法作为一种新兴的进化优化方法,能够大大减轻复杂的大规模优化问题的计算负担.根据博弈论的思想,在传统粒子群基础上提出了一种基于博弈模型的合作式粒子群优化算法,算法基于重复博弈模型,在重复博弈中利用一个博弈序列,使得每次博弈都能够产生最大效益,并得到了相应博弈过程的纳什均衡.通过典型基准测试函数对算法的性能进行对比实验,实验结果表明算法是可行的、有效的,对拓展粒子群算法研究具有重要的理论意义与实际意义.

Particle Swarm algorithm as a new evolutionary optimization method can greatly reduce the computational burden of complex, large-scale optimization problems. This article is based on game theory. On the basis of the particle swarm it proposeda non-cooperative game model based on particle swarm optimization algorithm, it wses a game sequence repeated game model. And in repeated games, each game all hope to produce maximum benefits. Nash equilibrium of the corresponding game process. Function through multiple benchmarks, comparing with the performance of the algorithm experimental results show that the algorithm is feasible and effective. The study has important theoretical significance and practical significance onexpand swarm intelligence algorithm.

人类基因组测序工作初步结果显示,人类基因数量只有大约3.5万个,活细胞使用这些基因来产生更有活力且更加充裕的指示源.可变剪接现象是活细胞具有惊人多样性的很好解释,因此发展生物信息学方法进行可变剪接研究显得尤为重要。本文提出了基于重复-动态博弈的可变剪接预测模型,该模型利用了动态博弈和重复博弈的思想,使用模糊聚类法提取博弈模型的参与人,将自上而下和自下而上相结合,并选择了具有高效率且比较依赖特征选择的朴素贝叶斯作为分类器.实验结果显示,基于重复-动态博弈的可变剪接预测模型具有最高的分类准确率耗时最短、使用的分类次数最少、特征选择机制更加优秀.

Preliminary result of human genome sequencing shows that the number of human genes is only about 35 , 000 .Living cells use these genes to produce more abundant and more dynamic indication source .The phenomenon of al-ternative splicing is a good explanation that living cells have amazing diversity ,therefore ,developing bioinformatics meth-ods for alternative splicing study is particularly important .The paper presents an improved alternative splicing prediction model based on repeated-dynamic game .This model adopts dynamic game and repeated game theory ,uses fuzzy cluste-ring method to extract participants of the game model ,combines sequential forward selection with sequential backward selection ,and selects naive bayes as classifier which efficiency is high and more feature selection dependent according to the characteristic of the model .Experimental results show that alternative splicing prediction model based on repeated-dy-namic game has the highest classification ac

贫困生认定是高校贫困生帮扶工作的重要基础,然而在贫困生认定中伪贫困生现象频现,给公平公正的教育环境带来了危害。从伪贫困生现象产生的博弈分析可知,在信息不透明、不对称的情况下,人从理性角度出发选择"不诚实"是博弈双方的最佳策略,双方均采取"不诚实"策略,从而产生纳什均衡,这种双方不诚实的纳什均衡点破坏了贫困生资助公平公正的基础。研究发现:运用大数据破解博弈前提,建立诚信档案,将单次博弈转变为重复博弈,营造公开透明环境避免陷入"囚徒困境",强化诚信激励改变博弈得益,树立救济帮扶为博弈目标是避免伪贫困生现象出现的有效措施。

Identification of poor students is an important fundamental work to help the poor students in univer-sities,while there are some pseudo - poor phenomenon existing in the process of identification,which is detrimental to fair education environment. By using the method of game theory,under the circumstance of unclear and asym-metric message,from the viewpoint of rationality people choose the dishonest as the best tactic of the two partici-pants of gaming,leading to the Nash equilibrium which seriously damage the foundation to help the poor students in just and fair. Based on that,using the big data to solve the premise of gaming,the honest files are to be estab-lished,turning the single gaming to the repeated gaming,creating clear and open environment avoiding the“pris-oners’dilemma”. Setting the helping and relieving as the purpose of gaming is the effective measure to avoid the phenomenon of pseudo - poor students.