Game Theory Approaches for the Solution of Power System Problems: A Comprehensive Review

Abapour, Saeed; Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam; Hagh, Mehrdad Tarafdar

doi:10.1007/s11831-018-9299-7

Cited by 72 publications

(28 citation statements)

References 91 publications

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“…Based on Eq. (1), for the general three-population n-strategy (where n2) evolutionary game (3PnSEG), when its payoff parameters are symmetric, it is a symmetric evolutionary game, and at this point, all participants in the game know each other's preferences [39]. Otherwise, if the payoff parameters are asymmetric, then it is an asymmetric evolutionary game, such that the degree of information mastered by each population is asymmetric.…”

Section: B Symmetric and Asymmetric Evolutionary Gamesmentioning

confidence: 99%

General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

et al. 2021

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Section: B Symmetric and Asymmetric Evolutionary Gamesmentioning

confidence: 99%

General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

et al. 2021

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“…Game theory is a mathematically formalized strategic interaction theory; that is, the author not only pays attention to the outcome of the selected scheme, but also considers the possible outcome of other schemes in the decision-making process, which simply analyzes the gains and losses of users affected by other users' decisions [26]. It has been commonly used in marketing, in order to model competition between companies or customers [27]. Ozkane et al employed game theory to simulate individual behavior in the process of the generation and evolution of innovation.…”

Section: Agent-based Modeling and Game Theorymentioning

confidence: 99%

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Zhou

Lin

et al. 2020

Sustainability

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Mobile application innovation diffusion can be used to enhance the reputation and competitiveness of Internet enterprises. However, few works have explored the process of mobile application innovation diffusion from the individual perspective. Therefore, this paper employs multi-agent methods to simulate the innovation diffusion of mobile applications from the perspective of social networks. Specifically, we combine psychology, sociology, game theory and network effect theory to model user behaviors of adoption and rejection decisions for mobile applications. The multi-agent model was built in Anylogic 8 to simulate the communication and interaction between individual users. Then, this paper discusses the evolution of decision-making of social network user groups with different network structures and network effects. We also investigate the impact of different firms' promotion on innovation diffusion. Our findings suggest firms could make better strategies and achieve better diffusion effects from mobile applications. IntroductionNowadays, market competition in mobile applications is increasingly fierce. The success of the mobile application in market promotion not only helps Internet enterprises to seize the market and create business value, but also allows firms establish a good reputation and gain a competitive advantage [1][2][3].The innovation diffusion of mobile applications always occurs in user groups. Since users can communicate and interact with others, most of the diffusion of mobile applications is based on each user in a social network [4,5]. Numerous studies indicate that the development of mobile internet and technology social networks, significantly reducing the cost of communication between users, makes communication and interaction very convenient [6,7]. As various mobile applications allow users to share happiness and dissatisfaction with others on the Internet, users' decisions on mobile applications are also influenced by social relations and social network structure [8,9]. In addition, research evidence also supports the fact that there are network effects in the diffusion of mobile applications; that is, the utility of mobile applications to users will increase with an increase in the number of applications [10]. Specifically, network effects can be divided into global and local network effects. Global network effect means that, when more users adopt innovative technology in the global environment, the value of mobile applications to users will be higher. While the local network effect refers to the effect of related users on the utility value, which emphasizing the interaction between users. The decision-making of users will largely depend on the decision-making of their surroundings. As the number of users adopting mobile applications increases, their neighbors also would like to adopt the same applications. Then, the value and utility of the mobile applications to the users will also increase, which ultimately Sustainability 2020, 12, 2884 2 of 17 affects user decision-making. In additio...

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“…The latter involves multiple populations and meanwhile, individuals from different populations may have different pure strategies playing in an asymmetric game. In particular, in an asymmetric game, participants have asymmetric information to the preferences of each other [10].…”

Section: Modelling Of a General 2paegmentioning

confidence: 99%

Two-population Asymmetric Evolutionary Game Dynamics-based Decision-making Behavior Analysis for A Supply-side Electric Power Bidding Market

et al. 2020

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This paper systematically discusses two-population asymmetric evolutionary games (2PAEGs) from the perspective of decision-making behavior characteristics, and applies these game models to a two-population supply-side electric power bidding market. First, a 2PAEG model is established. Then, complete evolutionary equilibrium rules of this model are revealed during decision-making processes. Discussion shows that final evolutionary game equilibria achieved in the 2PAEG model are only determined by some payoff parameters, which are defined as relative net payoff (RNP) parameters in this paper. Finally, a case study of supply-side bidding simulation for two generator populations is conducted, which can effectively verify the universality and effectiveness of the evolutionary dynamics results obtained in the established general 2PAEG model. Moreover, it shows that reasonable policies made by the government can guide more appropriate power bidding for onto-grid electricity.

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Game Theory Approaches for the Solution of Power System Problems: A Comprehensive Review

Cited by 72 publications

References 91 publications

General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Two-population Asymmetric Evolutionary Game Dynamics-based Decision-making Behavior Analysis for A Supply-side Electric Power Bidding Market

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