Cooperation Dynamics on Collaborative Social Networks of Heterogeneous Population

Wei, Guiyi; Zhu, Ping; Vasilakos, Athanasios V.; Mao, Yuxin; Luo, Jun; Ling, Yun

doi:10.1109/jsac.2013.130615

Cited by 19 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use Public Goods Game (PGG) [3] to further evaluate the performance of SA-PGA in multiple agent cases. PGG is an extended version of the PD game in multiagent environment, which has attracted increasing attention to study cooperative behavior and, in particular, deviations from the rational equilibrium [22,28]. In a typical public goods experiment a group of players is endowed with one dollar each.…”

Section: Performance In Games With Multiple Agentsmentioning

confidence: 99%

SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

Zhang

Hao

et al. 2019

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system designer's perspective, it is desirable if the agents can learn to coordinate towards socially optimal outcomes, while also avoiding being exploited by selfish opponents. To this end, we propose a novel gradient ascent based algorithm (SA-IGA) which augments the basic gradient-ascent algorithm by incorporating social awareness into the policy update process. We theoretically analyze the learning dynamics of SA-IGA using dynamical system theory and SA-IGA is shown to have linear dynamics for a wide range of games including symmetric games. The learning dynamics of two representative games (the prisoner's dilemma game and the coordination game) are analyzed in details. Based on the idea of SA-IGA, we further propose a practical multiagent learning algorithm, called SA-PGA, based on Q-learning update rule. Simulation results show that SA-PGA agent can achieve higher social welfare than previous social-optimality oriented Conditional Joint Action Learner (CJAL) and also is robust against individually rational opponents by reaching Nash equilibrium solutions.

show abstract

Section: Performance In Games With Multiple Agentsmentioning

confidence: 99%

SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

Zhang

Hao

et al. 2019

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…Given the fact of explosive growth of online social networks such as Facebook, Twitter, and Weibo, some researches novelly leverage the social relationship to promote a cooperative D2D network [5]. There are also some papers trying to investigate the cooperation rate in a Public Goods Game (HPGG) and Prisoner's Dilemma Game (PDG) modeled collaborative social networks (CSNs) [15,16].…”

Section: Related Workmentioning

confidence: 99%

Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications

Deng

Wei

Xie

et al. 2016

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

The explosive use of smart devices enabled the emergence of collective resource sharing among mobile individuals. Mobile users need to cooperate with each other to improve the whole network’s quality of service. By modeling the cooperative behaviors in a mobile crowd into an evolutionary Prisoner’s dilemma game, we investigate the relationships between cooperation rate and some main influence factors, including crowd density, communication range, temptation to defect, and mobility attributes. Using evolutionary game theory, our analysis on the cooperative behaviors of mobile takes a deep insight into the cooperation promotion in a dynamical network with selfish autonomous users. The experiment results show that mobile user’s features, including speed, moving probability, and reaction radius, have an obvious influence on the formation of a cooperative mobile social network. We also found some optimal status when the crowd’s cooperation rate reaches the best. These findings are important if we want to establish a mobile social network with a good performance.

show abstract

“…Moreover, we will also construct the payoff matrix for players based on the payoff function. The game formulation presented here is an extension to our previous work in [28][29][30].…”

Section: Game Formulation For Energy-aware Dtn Routingmentioning

confidence: 99%

A Game Theoretical Model for Energy-Aware DTN Routing in MANETs with Nodes’ Selfishness

Mao

Zhu

2015

Mobile Netw Appl

Self Cite

View full text Add to dashboard Cite

Many existing studies just considered that nodes in a Mobile Ad Hoc NETwork (MANET) would fully cooperate to forward packets for one another. However, this assumption does not hold in many scenarios. The node selfishness in relay cooperation will certainly influence the overall performance of routing in MANE Ts. In this work, we investigate the energy-aware routing problem in MANETs with nodes' selfishness. We model the situation of node cooperation in energy-aware routing by using game theory. We consider the competitive and cooperative relationship between the nodes to formulate the game for MANETs. An incentive mechanism is given in order to encourage forwarding cooperation during energy-aware routing. Nodes are encouraged to forward more data packets for others in order to acquire more services from others. We also carry out a simulation for the proposed method and perform detailed analysis in this work. From the results of the simulation, we argue that it's possible to find out a suitable configuration for a MANET to support more efficient energy-aware routing through our method.

show abstract

Cooperation Dynamics on Collaborative Social Networks of Heterogeneous Population

Cited by 19 publications

References 31 publications

SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications

A Game Theoretical Model for Energy-Aware DTN Routing in MANETs with Nodes’ Selfishness

Contact Info

Product

Resources

About