Incentive Mechanisms For The Emergence Of Cooperation And Trust In Network-based Collaborative Systems

With the rapid development of the Internet,network-based collaborative systems have been widely used in our lives.Individual users are the basic unit of network-based collaborative systems,as a result,the performance of network-based collaborative systems will be directly affected by the behaviors of individual users.However,due to the fact that it is costly to collaborate with others in terms of time and energy,therefore,rational users who aim to maximize their benefits will:(1)refuse to provide services,which leads to a cooperation dilemma;(2)provide services with low quality,which leads to a trust dilemma.In order to ensure the performance of network-based collaborative systems,efficient incentive mechanisms which can promote users to be cooperative and trustworthy should be introduced In.Based on the related work,we use game theory,evolutionary game theory,similarity theory,graph-based recommendation system,multi-objective optimization,reinforcement learning to further study the methods on solving the cooperation dilemma and trust dilemma.The main contributions of this dissertation are listed as follows:(1)The qualitative study of recommendation incentive mechanism.Firstly,we use the typical graph-based recommendation algorithm EigenTrust to build a recommendation incentive mechanism,as a result,users who adopt the proposed recommendation incentive mechanism can have higher probabilities of been served.Secondly,we use evolutionary game theory to qualitatively study the performance of the proposed recommendation incentive mechanism on promoting cooperation.To further study the robustness of the proposed mechanism,IRIM(Imperfect Recommendation Incentive Mechanism),ORMP(One Requester Multi-service Providers).FS(Four-Strategies)and QS(Quantity Sensitivity)scenarios have been researched.Finally,extensive numerical and simulation experiments are conducted to highlight the performance and validate the theoretical properties of our mechanism.(2)The qualitative study of rational defection mechanism.Under a practical scenario which considers the reciprocity cost,unconditional cooperation strategy would inhibit the emergence of cooperation.To solve this issue.we propose a rational defection mechanism.Firstly,we still use evolutionary game theory to model the interaction between users,and R strategy users who use the rational defection mechanism are assumed to cooperate with unconditional cooperation strategy with the c(c ?[0,1))probability.Particularly,if c=0,it is proved that R strategies users can dominate the system,as a result,the system can achieve the Pareto optimality.Secondly,to distinguish the R strategy users and unconditional cooperation strategy users,we propose a strategy recognition method based on the cosine similarity.Fmally,extensive numerical and simulation experiments are conducted to highlight the performance of our mechanism on promoting cooperation.(3)The study of cooperation and trust-aware incentive mechanism.Firstly,we propose a modified EigenTrust algorithm to simultaneously calculate the global cooperation value and global trust value of each user based on the past behaviors.Secondly,we propose a cooperation and trust-aware worker recommendation mechanism by determining a Pareto front from all users.Thirdly,we use evolutionary game theory to study the acceptance and effectiveness of the proposed incentive mechanism by strategically modeling the interaction between users.And then,the Lyapunov stability theory is employed to mathematically investigate the stability of the cooperation and trust-aware incentive mechanism.Finally,both numerical simulations and simulator-driving experiments illustrate that our mechanism has an outstanding performance in promoting cooperation and inhibiting malicious activity,(4)The study of quality assurance by incorporating multi-auditing and truth inference mechanisms.Firstly,in order to improve the performance of multi-auditing mechanisms on executing crowd tasks,we introduce a task-based temporal reputation mechanism to measure the reliability of each crowd worker.Secondly,we propose an unsupervised truth inference algorithm RPM(Reputation-based Participant-Mine voting)which can be used to discover the truth when the requester does not audit the feedbacks from one certain task.Thirdly,rational requesters and rational workers who will constantly change their strategies to obtain more benefits are considered in this dissertation.Specially,we use reinforcement learning and 1-?accuracy learning to model the updating policy of a requester's strategy and use K-armed bandits learning and neighborhood learning to model the updating policy of rational workers.By using Lyapunov stability theory,it is qualitatively proved that the trustful provision of sensed data provides a unique evolutionary stable equilibrium for each rational worker in our proposed model.Finally,extensive simulations and real data experiments illustrate that the proposed mechanism has an outstanding performance on quality assurance.