Multi-strategy evolutionary games: A Markov chain approach

Hajihashemi, Mahdi; Samani, Keivan Aghababaei

doi:10.1371/journal.pone.0263979

Cited by 7 publications

(5 citation statements)

References 70 publications

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“…4.1.1 Markov chain. The Markov chain is the method used in this paper to discretize the continuous data in the geographical space and divide it into n types [44]. The transfer probability of each spatial type in continuous time is observed, the value being between 0 and 1.…”

Section: Industrial Development Analysis Methodsmentioning

confidence: 99%

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era

He,

Zhang,

Qiu

et al. 2024

PLoS ONE

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Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China’s resource-based regions’ industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China’s resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.

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Section: Industrial Development Analysis Methodsmentioning

confidence: 99%

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era

He,

Zhang,

Qiu

et al. 2024

PLoS ONE

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show abstract

“…Secondly, considering the attacker's intention to attack, the three-way dynamic adjustment strategy is used for the weighted calculation of network security attributes. Finally, the Markov decision process is introduced to consider the multi-stage future expected returns, to find the evolutionary stable equilibrium [30], and to select the optimal attack and defense strategies.…”

Section: Three-way Evolutionary Game Defense Model Constructionmentioning

confidence: 99%

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Zhang,

Liu,

Zhang

et al. 2024

Electronics

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Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.

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“…In other words, this is equivalent to performing reverse engineering to infer the procedure used to generate the time series from the observable time series. Based on this idea, we can used the Markov model as a predictive model, which has been used in many studies [26][27][28][29]. In addition, strategy estimation metaheuristics has been also considered effective in many conventional studies [30][31][32][33].…”

Section: Strategy Inference From Rps Time Series Using Genetic Progra...mentioning

confidence: 99%

“…It is feasible to model the time series of human moves as an output time series from a Markov chain, which allows the player to formulate an effective strategy and estimate the opponent's strategy [26][27][28][29]. Similarly, it is natural idea to employ evolutionary algorithms e.g., genetic algorithms (GA)and genetic programming (GP), to estimate and formulate strategies [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Human Randomness in the Rock-Paper-Scissors Game

2022

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In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

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Multi-strategy evolutionary games: A Markov chain approach

Cited by 7 publications

References 70 publications

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Human Randomness in the Rock-Paper-Scissors Game

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