ε-Nash Mean Field Game theory for nonlinear stochastic dynamical systems with mixed agents

2012 ◽  
Author(s):  
Mojtaba Nourian ◽  
Peter E. Caines
Keyword(s):  
Game Theory ◽  
Dynamical Systems ◽  
Mean Field ◽  
Stochastic Dynamical Systems ◽  
Mean Field Game

scholarly journals Describing nonequilibrium soft matter with mean field game theory

2019 ◽  
Vol 150 (17) ◽  
pp. 174905 ◽  
Author(s):  
P. M. Welch ◽  
K. Ø. Rasmussen ◽  
C. F. Welch
Keyword(s):  
Game Theory ◽  
Soft Matter ◽  
Mean Field ◽  
Mean Field Game

Mean Field Game Theory with a Partially Observed Major Agent

2016 ◽  
Vol 54 (6) ◽  
pp. 3174-3224 ◽  
Author(s):  
Nevroz Şen ◽  
Peter E. Caines
Keyword(s):  
Game Theory ◽  
Mean Field ◽  
Mean Field Game ◽  
Partially Observed

Mobile Ad Hoc Network Security using Mean Field Game Theoretic Threshold-Based Scheme

2020 ◽  
Vol 13 ◽  
Author(s):  
Khyati Chopra
Keyword(s):  
Game Theory ◽  
Ad Hoc ◽  
Mean Field ◽  
Theoretic Approach ◽  
Distributed Network ◽  
Mean Field Game ◽  
Data Packets ◽  
Game Theoretic ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Background: Mobile ad hoc networks (MANET’s) have recently attracted attention as it is independent of any pre-existing network infrastructure or centralized administration. Security in MANET’s thus becomes a major concern due to its open and dynamic architecture. Objective: We have introduced a dynamic mean field game theoretic approach to enable an individual node in MANETs to make strategic security defense decisions without centralized administration. Method: The nodes in MANET’s act as a router to forward data packets and exchange routing information. Ad-hoc On demand Distance Vector (AODV) protocol is one of the standard MANET routing protocols which can be easily attacked by the fraudulent nodes. The fraudulent nodes can be deceptive and mislead the transmission of data packets in the network by providing shorter path and highest destination sequence number. Game theory finds wide application as a statistical and mathematical tool to model such dynamic networks and provide security. Results: We have implemented mean field game theory for addressing security issue in MANET’s. Each node in this dynamic distributed network, knows the information about its own state as well as the average reflection of the whole mean field. The players can strategically make distributed security defense decisions under adverse conditions. Unlike static threshold-based scheme for security, in our study the threshold is estimated dynamically. Each node checks whether the received Route REPly (RREP) sequence number is higher than a dynamically updated threshold value. Conclusion: The comparative performance analysis of throughput (TR), packet delivery rate (PDR) and average cost (AC) has been demonstrated. Game theory has a vital role to validate and justify the intuitive strategic actions taken by each player to maximize their utility by playing optimal strategy. On the basis of the dynamic threshold calculated, the higher throughput and PDR could be achieved by eliminating the misleading paths. Simulation results corroborate that our dynamic mean field game theoretic scheme outperforms the static scheme. Discussion: A dynamic approach for mobile ad hoc networks is presented in this paper to improve the performance of the network in hostile environment. We have introduced a dynamic mean field game theoretic approach to enable an individual node in MANETs to make strategic security defense decisions without centralized administration. In this dynamic distributed network, each node in the proposed scheme only needs to know its own state information and the average reflection of the whole mean field.

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