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Resource and Interference Management
Published in Wen Sun, Qubeijian Wang, Nan Zhao, Haibin Zhang, Chao Shen, Ultra-Dense Heterogeneous Networks, 2023
Wen Sun, Haibin Zhang, Nan Zhao, Chao Shen, Lawrence Wai-Choong Wong
The prisoner's dilemma is a static game, and the repeated prisoner's dilemma is a dynamic game. By repeating, the player can decide the current choice according to the opponent's past choices, and each player has the opportunity to “punish” another player's uncooperative behavior in the previous round. The best strategy is considered to be a “tit-for-tat” strategy, which is to cooperate at the beginning of the repeated game and then adopt the strategy of the opponent's previous round.
Application of Game Theory for Big Data Analytics
Published in Mohiuddin Ahmed, Al-Sakib Khan Pathan, Data Analytics, 2018
Mohammad Muhtady Muhaisin, Taseef Rahman
In repeated games, a set of players will play the game repeatedly with the same strategy, taking into consideration the history of the past behavior. For better understanding, let us consider a repeated prisoner’s dilemma game.
Enhancing cyber-physical security in manufacturing through game-theoretic analysis
Published in Cyber-Physical Systems, 2018
Zach DeSmit, Aditya U. Kulkarni, Christian Wernz
Repeated games model cases where the players play the same game multiple times. Repeated games can either be finite, where the number of repetitions of the same game is finite, or infinite, where the players are assumed to repeat the same base game forever. For finitely repeated games, backward induction and SPNE is used to solve the game and find a player’s optimal strategies. For infinite games, one distinguishes between discounted and constant rewards. Constant reward games are those where rewards stay the same in each repetition of the game. In discounted games, future rewards are reduced by a discount factor There are two reasons for using a discount factor in infinitely repeated games. The first is due to mathematical tractability. The net expected future rewards for a player in repeated games with constant rewards often approach infinity, which makes the characterisation of an equilibrium strategy profile difficult. By multiplying all future rewards by an appropriate discount factor, mathematical tractability is ensured since the net future expected rewards of a player converge. The second reason is approximate a model of human behaviour [63]. Humans place more value on immediate rewards than those far in the future. Thus, by appropriately discounting future rewards, game-theorists can approximate human behaviour in infinitely repeated games. Infinitely repeated games are solved using strategies provided by the folk theorem [64,65].
A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
In non-cooperative, each user takes care of its benefit and selects optimal actions to maximise its payoff function. The author in (Yu, 2013) presented cooperative games for spectrum sensing and sharing and found that the applicability of various games depends on several factors. A non-cooperative game can be classified based on information i.e. either complete information or an incomplete information game. In the complete information games, each player observes other players’ information i.e. payoff and their action. On the other hand, with incomplete information, the game can be modelled as Bayesian game for decision making in which outcome can be estimated based on Bayesian analysis (Wang et al., 2010). Several types of games have been adapted to model various situations in CR networks. For example, in repeated games, each stage is usually repeated. Let us consider the set of CRs in the network and denotes the action taken by a player at the stage of the game. In each stage , players tend to maximise their payoff function, while considering the history of action collected in . In other words, players map their actions from history . In CR networks, their action is the selection of channel available and mapping of action depends on the history of PUs activity as well as activity pattern of other CRs. In dynamic/repeated games, players came across a similar game number of times. The cooperation among the players in repeated games can be introduced to get long term benefits. The repeated games are applied for spectrum sharing scenarios where multiple CRs exists in (Li et al., 2010). In this context, repeated games apply punishments to achieve a desirable outcome. When the PUs activity is considered as stationary with an unknown environment then Stochastic games, also known as Markov games, are introduced to model the network. The authors in (W. Wang et al., 2018) addressed the routing problem using stochastic games. Stochastic routing game is decomposed into several stages and at each stage stochastic learning is proposed to learn equilibrium strategy of channel selection.