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∞ Evolutionary Game Strategies of a Population of Evolutionary Biological Networks
Published in Bor-Sen Chen, Stochastic Game Strategies and Their Applications, 2019
In the last decades, evolutionary game theory is well established in both biology and economics. In the biological version of von Neumann’s game theory, the concept of fitness is treated as an equivalent of “payoff”. Evolutionary game theory has demonstrated that the basic ideas of game theory can be applied even to situations in which no individual is overtly reasoning or making explicit decisions [27, 28, 487–489]. In these cases, game-theoretic analysis can rather be applied to settings in which individuals can exhibit different forms of behavior which may include some that are not the result of conscious choices. This also allows exploration of which forms of behavior have the ability to persist in a population and which have a tendency to be driven out by others. In the evolution of biological systems, the key insight of evolutionary game theory is that many behaviors involve the interaction of multiple organisms in a population, and the success of any one of these organisms depends on how its behavior interacts with that of others. In this situation, the fitness of an individual organism cannot be measured in isolation; rather, it has to be evaluated in the context of the full population. In other words, an organism’s genetically determined characteristics and behaviors are like its strategy in a game, its fitness is like its payoff, and this payoff depends on the strategies (characteristics) of the organisms with which it interacts [487, 490, 491].
Genetic Algorithms
Published in James A. Momoh, Electric Power System Applications of Optimization, 2017
GAs can be used to evolve behaviors for playing games. Work in evolutionary game theory typically encompasses the evolution of a population of players who meet randomly to play a game, which they each must adopt one of a limited number of moves. Suppose it is just two moves, X and Y. The players receive a reward analogous to Darwinian fitness, depending on which combination of moves occurs and which move they adopted. In more complicated models there may be several players and several moves.
Agents in Economic Markets and Games
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
Recent interest of economists and biologists has moved from traditional game theory to evolutionary game theory as it provides more insights and analysis of systems, particularly reducing the number of assumptions.
Evolutionary game analysis of pedestrian-autonomous vehicle interactions at unsignalized road sections: a policy intervention perspective
Published in Transportation Letters, 2022
Rong Rui, Xusheng Yao, Shunqiang Ye, Shoufeng Ma
Existing studies on the relationship between pedestrians, AVs and traffic managers are mostly capable of depicting the characteristics of traffic agents, while the interpretation of the dynamic game process of interactions is deficient. With dynamic characteristics, evolutionary game theory can effectively compensate for this disadvantage through the mathematical modeling of multiperson interactions (Yi et al. 2020). According to Y. Zhang (2021), bounded rationality and repeated games are two main characteristics of evolutionary game theory. People may not show rationality when facing complex decision-making problems in real life, which makes evolutionary game theory an appropriate method for investigating such issues. Evolutionary game theory aims to find a stable state of the focal system through repeated games and replicator dynamic equations (Cai and Kock 2009; Taylor and Jonker 1978). It has recently been widely adopted and developed in behavior and decision analytics (Cheng, Gong, and Li 2018; Xing, Hu, and Luo 2020).
Modelling and strategy optimisation for a kind of networked evolutionary games with memories under the bankruptcy mechanism
Published in International Journal of Control, 2018
Shihua Fu, Haitao Li, Guodong Zhao
The investigation for the emergence and maintenance of cooperative behaviours among selfish individuals is a fundamental problem in biological and social sciences. In previous studies (Li & Yong, 2014; Liu, Guan, & Wu, 2013; Szolnoki & Perc, 2010; Szolnoki, Wang, & Perc, 2012; Dorogovtsev & Mendes, 2014; Xu, Ji, Jiun, Zheng, & Hui, 2011), evolutionary game theory is usually utilised as a powerful tool to study the evolution of cooperation, and the paradigms which are most commonly used for investigating this issue are prisoner's dilemma game (PDG), snowdrift game (SG) and public goods game (PGG). In recent years, evolutionary game on graphs, which is called networked evolutionary game (NEG), has attracted a lot of attention because the evolution of biological systems is naturally over a networked environment. In the network, nodes represent players and edges describe the interaction relationship among players, and based on some special strategy adjustment rule, players update their strategies only depending on their neighbours. With the development of complex networks, the theory of NEG has been considered by many scholars, and been applied to many practical fields, including biology, economy, social science and so on (Friedman, 1998; Nowak & Sigmund, 2004; Tarnita & Tibor, 2009; Taylor, Fudenberg, Sasaki, & Nowak, 2004).
Influencing factors in the application of RFID technology in the supply chain
Published in The Engineering Economist, 2018
Bo Yan, Lifeng Liu, Si Liu, Jianbo Yang
In natural populations, the fitness of an organism often depends both on its own strategy and on the strategies of other population members (McNamara 2013). Evolutionary game theory is a result of the application of game theory to biological evolutionary contexts. Evolutionary dynamics provide a powerful set of tools for investigating a range of issues in biology and the social sciences (Hodgson and Huang 2012; Rand and Nowak 2012). The theory has been utilized in other areas of social science, such as business, culture, and economics (Antocia et al. 2014; G. S. Cai and Ned 2009; Mattei 2014). In evolutionary game theory, evolution and natural selection replace the rationality of the actors appropriately (Hummert et al. 2014). The theory studies the evolution process of the whole system, strategy, and distribution characteristics when limited rational individuals of populations are repeating a game process (Young 2011). The dynamic process provides the coordination device that brings beliefs in line with behavior through the individual learning process. The process also provides the context for play that may be useful in assessing multiple equilibriums. Furthermore, the dynamic process views equilibrium as the outcome of an adjustment process and a realistic version of human interactions (Fudenberg and Levine 1997; Samuelson 1997). An attempt has been made to find a comprehensive mathematical framework to investigate the problems of well-posedness and asymptotic analysis for fully nonlinear evolutionary game theoretic models (Cleveland and Ackleh 2013; Veloz et al. 2014).