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We propose and analyze a single-species population model subject to fear and its carry-over effect with the help of evolutionary game theory (EGT). We incorporate fear and carry-over cost in the growth of a single species resource population and the extensive analysis of our non-evolutionary model suggests that it can exhibit both weak and strong Allee effects. From the game theoretical viewpoint, we assume that the intrinsic growth rate r of the resource population and the attack rate a of the consumer population are functions of a mean phenotypic trait (u) of the resource, following a Normal distribution. Evolutionary stable strategies (ESS) are determined by using ESS maximum principle. Our study of ESS suggests that species extinction may be avoided as a result of evolution, though the extinct equilibrium can also be an ESS under certain conditions. The ratio of variation in the intrinsic growth rate and the attack rate plays a significant role in the ESS conditions of different equilibria as well as the global dynamics of our EGT model. Numerical simulations are performed to support our theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to addressing the complexity of current optimization challenges by developing hybrid metaheuristic algorithms. These algorithms combine the strengths of different strategies to enhance solution identification and refinement. In this work, the integration of metaheuristic operators using the Unstructured Evolutive Game Theory to regulate and merge the advantageous features of the Cheetah Optimizer and Particle Swarm Optimization to solve continuous optimization problems is proposed. The Cheetah Optimizer and Particle Swarm Optimization are chosen for their superior exploitation and exploration capabilities, respectively. Our methodology unfolds in two primary phases: combination and modulation. In the combination phase, Cheetah Optimizer and Particle Swarm Optimization search strategies are merged, creating a unified population that generates new candidate solution positions based on the best solution and a modulation factor. During the modulation phase, pairwise competitions based on Unstructured Evolutive Game Theory assess candidate solutions against the objective function, adjusting their modulation factor and position accordingly. We conducted various experiments to evaluate our approach against the original Cheetah Optimizer and Particle Swarm Optimization, as well as seven other well-known metaheuristic algorithms and hybrid schemes, across 30 benchmark functions in dimensions of 30, 50, and 100. The results reveal that our hybrid scheme outperforms the comparative algorithms, demonstrating enhanced performance, effectiveness, and robustness through a detailed convergence and significance analysis. [ABSTRACT FROM AUTHOR]

The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. Prior studies have focused minimally on the bidding mechanism and the possible collusive bidding behavior under this mode. The objectives of this study are to analyze the bidding mechanism and the dynamic evolution of collusive bidding behavior under the MMB system and provide targeted regulation countermeasures. To this end, this study develops an evolutionary game model among collusion initiators, free bidders, and regulators, explores possible scenarios for evolutionarily stable strategies, and performs sensitivity analysis of critical parameters utilizing MATLAB software (Version R2024a) based on empirical data. Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the "non-collude" strategy, the free bidder adopting the "bid" strategy, and the regulator adopting the "negative regulate" strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. This study supplements current knowledge on the regulation of collusive bidding behavior and enriches the knowledge framework of the MMB model. This study also provides insights for policymakers to guarantee the smooth implementation of the MMB. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of air transportation, reduce transportation costs and carbon emissions, the evolutionary game method was adopted to explore the influencing factors of the cargo alliance between the government and airlines. The system dynamics model (SD) was used to conduct simulation experiments on the game evolution process. The results indicate that the strategic choices between airlines are influenced by investment costs and alliance benefits, while the government's strategic choices are influenced by factors such as the carbon emission penalty coefficient paid by airlines, carbon tax subsidy amounts, social benefits, and regulatory costs. In the long run, the government should establish a dynamic subsidy mechanism and increase the diversity of subsidies to enhance the innovation and core competitiveness of airlines. [ABSTRACT FROM AUTHOR]

Facing the sanctions from the West since the annexation of Crimea in 2014, Russia quickly converged to a strong counter-sanction strategy. The US and the EU staggered the strengths of sanctions in turns, with the EU first imposing relatively stronger commercial sanctions first and the US relaying with stronger financial sanctions later. Using US-EU-Russia sanctions as an example, we develop a multilateral, evolutionary game to capture the strategic complementarity between the sanctioners and the sanctionee, as well as the strategic substitutability between the leading sanctioner and the co-sanctioner. In an extended model, the sanction technology is introduced to endogenize how sanctions are designed before deployment. The model is then calibrated to match the summarized stylized facts, to demonstrate the simulated evolutionary paths, and to verify the derived strategic dependence. [ABSTRACT FROM AUTHOR]

The root cause of human conflict needs to be understood but it is currently unknown whether the decision to engage in conflict is an inherited or acquired trait. This article reports two experimental simulations which demonstrate that the level of confrontation in a population of simple organisms can be explained by the evolution of a simulated gene pool. Game theory and evolutionary algorithms were combined in a novel way to examine how six variables influenced the decision to confront in the competition for resources. The main variable was how the genetically determined rate of confrontation evolved as a function of environmental resources and cost of a conflict. The additional modulatory effects of four other variables were also considered in the first round of simulations. Two variables were responsive to the difference between resources and cost. Two other variables were responsive to the organism's health status. Taking a systematic approach, we examined how a population of 1000 organisms were evolving in environments with different levels of reward and punishment. During each cycle, each organism was paired with another organism and thus needed to decide whether to confront or cooperate. We used a genetic algorithm to simulate the evolution of the gene pool over 500 cycles. The first series of simulations demonstrated that the baseline rate of confrontation was very responsive to environmental conditions. Our results also indicate that the decision to confront or cooperate depended not only upon the immediate competitive conditions, in which the organisms evolved, but were also responsive to their own health status. The second series of simulations used zero‐sum games to explore how risk levels varied as a function of the potential cost of engaging in a confrontation. In the second round of simulations, a simple form of memory was implemented. The results indicated that memory had a limited, but significant effect, while the cost of a conflict was highly predictive of the level of risk taken by the organisms. Our two series of simulations show that AI could contribute to answering psychological and societal questions. Our unique combination of techniques has brought to light several new insights into the mechanisms that drive the population towards cooperation and confrontation. The degree of generalizability of our results and future avenues for deepening our understanding of these evolutionary dynamics are discussed. [ABSTRACT FROM AUTHOR]

Why do humans punish free riders at their own expense? This behavior represents an evolutionary puzzle in human societies. This study explores the role of exit strategies in fostering altruistic punishment within evolutionary game theory. We extend the traditional prisoner’s dilemma model by incorporating exiters, players who opt out for a small payoff while nullifying their opponent’s payoff, and altruistic punishers who cooperate and punish non-cooperators. Our findings indicate that in well-mixed populations, exiters destabilize defection but do not promote altruistic punishment. In social networks, however, exiters enable altruistic punishment via cyclic dominance among altruistic punishers, defectors, and exiters. Notably, this cyclic dominance is sensitive to exit payoffs; adjustments can lead to cyclic dominance of non-punishing cooperators, defectors, and exiters, or to a bi-stable state between these two types of cyclic dominance. These results highlight the nuanced impact of exiters on altruistic punishment, emphasizing the need for careful incentivization of exit behavior. While exiters can support altruistic punishment in networked populations, their effectiveness is not a panacea and is highly sensitive to exit payoffs, indicating limits to the voluntary participation mechanism. [ABSTRACT FROM AUTHOR]

In this work we analyze, from the perspective of game theory, the interaction between the ruling elite, the opposition (to the government) and citizens during electoral times. In particular, we focus on the role that corruption plays (understood in a broad sense) in both contenders, government and opposition, and the level of information on the part of citizens when casting their vote to elect their representatives. To do this, we propose a game (the political game of the corruption and information $ C \&I $- political game), the result of which will depend on the level of corruption in the political sectors and the information of citizens about the actions of politicians. We affirm that - in a democracy in which the citizen vote is universal - although the proposals of the different political actors play an important role when deciding the vote, the level of information and interest of citizens in the political life of the country is the determining factor. Let us show that a low level of information on the part of citizens can lead to the re-election of a government with high levels of corruption regardless of the campaign carried out by the opposition. While an informed citizenry tends to favor the empowerment of an opposition with viable proposals against a corrupt government. [ABSTRACT FROM AUTHOR]

Direct reciprocity is a wide-spread mechanism for the evolution of cooperation. In repeated interactions, players can condition their behavior on previous outcomes. A well-known approach is given by reactive strategies, which respond to the coplayer's previous move. Here, we extend reactive strategies to longer memories. A reactive-n strategy takes into account the sequence of the last n moves of the coplayer. A reactive-n counting strategy responds to how often the coplayer cooperated during the last n rounds. We derive an algorithm to identify the partner strategies within these strategy sets. Partner strategies are those that ensure mutual cooperation without exploitation. We give explicit conditions for all partner strategies among reactive-2, reactive-3 strategies, and reactive-n counting strategies. To further explore the role of memory, we perform evolutionary simulations. We vary several key parameters, such as the cost-to-benefit ratio of cooperation, the error rate, and the strength of selection. Within the strategy sets we consider, we find that longer memory tends to promote cooperation. This positive effect of memory is particularly pronounced when individuals take into account the precise sequence of moves. [ABSTRACT FROM AUTHOR]

Traditional models of social learning by imitation are based on simple contagion--where an individual may imitate a more successful neighbor following a single interaction. But real-world contagion processes are often complex, meaning that multiple exposures may be required before an individual considers changing their type. We introduce a framework that combines the concepts of simple payoff-biased imitation with complex contagion, to describe how social behaviors spread through a population. We formulate this model as a discrete time and state stochastic process in a finite population, and we derive its continuum limit as an ordinary differential equation that generalizes the replicator equation, a widely used dynamical model in evolutionary game theory. When applied to linear frequency-dependent games, social learning with complex contagion produces qualitatively different outcomes than traditional imitation dynamics: it can shift the Prisoner's Dilemma from a unique all-defector equilibrium to either a stable mixture of cooperators and defectors in the population, or a bistable system; it changes the Snowdrift game from a single to a bistable equilibrium; and it can alter the Coordination game from bistability at the boundaries to two internal equilibria. The long-term outcome depends on the balance between the complexity of the contagion process and the strength of selection that biases imitation toward more successful types. Our analysis intercalates the fields of evolutionary game theory with complex contagions, and it provides a synthetic framework to describe more realistic forms of behavioral change in social systems. [ABSTRACT FROM AUTHOR]

Prefabricated construction has gained traction in sustainable construction practices. As a modular construction method, the prefabricated component (PC) supply chain involves key participants—producer, transporter, and assembler—operating in a point-to-point cooperation pattern (i.e., single project–single producer–single transporter). However, a disparity in resource allocation between the supply and demand sides leads to inefficiencies within the PC supply chain. Thus, this paper introduces the platform service pattern concept combined with evolutionary game theory, constructing a tripartite evolutionary game model to analyze the dynamic evolution mechanism of their behavioral strategies. Extending from a point-to-point cooperation pattern, this paper analyzes a multi-to-multi network structure (i.e., multiple projects–multiple producers–multiple transporters) and utilizes evolutionary game theory to simulate a stakeholder's strategies. Beyond maximizing the economic profits, we also include sustainable objectives into consideration, and we explore how project and environmental subsidies impact multiparty collaborations and their strategy selections. Specifically, three prefabricated components supply chain cooperation patterns (PCSCCPs) with multiparty dynamics are derived: standardized PC supply chain cooperation pattern (ST-PCSCCP), green and standardized PC supply chain cooperation pattern (GS-PCSCCP), and mixed PC supply chain cooperation pattern (MX-PCSCCP). They are developed and compared by using empirical data for model parameter selections. The results show that all patterns achieved simultaneous production and transportation for multiple project orders and all ensure that construction projects meet sustainability requirements. Additionally, MX-PCSCCP requires the minimum subsidies. [ABSTRACT FROM AUTHOR]

Within the intricate dynamics of a blockchain system, participants with bounded rationality may engage in dishonest behavior, thereby disrupting the consensus of the system in the absence of incentive mechanisms. This vulnerability is particularly common in voting-based consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT), yet existing analyses often overlook the rational perspectives of participants. In this paper, we introduce an evolutionary game-theoretic approach to extend the current incentive mechanisms, enhancing the system's stability and security. In permissioned blockchain environments, we developed a three-party evolutionary game model involving proposers, validators and auditors that dynamically assesses node strategies under various economic incentives and attack scenarios on an improved mechanism basis. Using replicator dynamic equations, we identified the conditions under which nodes converge to different evolutionarily stable strategies. Through simulation experiments, we validated the incentive mechanism's effectiveness in promoting honest participation and resisting bribery attacks. When dishonest nodes do not exceed 80%, the system can quickly evolve to the honest state and can cope with changes in the payoff for malfeasance, whereas the original mechanism could not reach the ideal state under any condition. This achievement is attributed to the design of the mechanism and the judicious adjustment of reward and punishment parameters. The research contributes valuable findings to the discourse on consensus security. It provides key insights for developing more robust and rational incentive mechanisms in future protocols. [ABSTRACT FROM AUTHOR]

Sustainable development is critical today due to increasing demand for natural resources in a fast-growing globe. To facilitate efficient used products recycling management, this paper develops a tripartite evolutionary game model in order to examine the complex interactions between suppliers, manufacturers, and retailers while deciding on their used products collection strategies. Evolutionary stable strategies of the system entities are determined by solving the replicator dynamics and evaluating the stability requirements of the critical points. Numerical simulations are done to verify the plausibility of the model and explore the effects of parameters on the participants' long-term strategies. The results show that, in a high procurement competition and low-profit environment, the manufacturer's best long-term stable strategy is to procure used products, whereas the entire population of suppliers and retailers is expected to use a non-procurement strategy. However, under weak procurement competition and significant gain environment, each population's stable behavior trends to favor purchasing of used products. Even though suppliers and retailers exhibit different stable behaviors in different situations, the manufacturer never changes; it always prefers the used products collection strategy. [ABSTRACT FROM AUTHOR]

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In order to investigate the relationship between the management and executive components of the mine and to improve the safety level of the system, this paper introduces a new method for evaluating and managing the safety of the mining system. For this purpose, game theory has been used to solve mining safety issues. Game theory is a structured tool that can examine interactions between two or more players to understand their actions in certain situations. In this paper, evolutionary game is used because of expressing the dynamics of relationships between players. The introduced model simulates the interactions between the government, managers, and miners under different conditions; also, the influence of regulatory factors on them in the system is evaluated. The results show that the effect of each factor on system safety is different and players affect the system safety according to each regulatory factor. Among these factors, the influence of punishments applied by game supervisors on the behavior of the mine management and mine workers was the greatest. So that with the increase of fines, the players adopted strategies aligned with safe production in a short time. Subsequently, the impact of government incentives was also investigated and it was determined that in exchange for increasing the reward factor to the management and mine workers, a change in strategy and safe production will be established in the mine. [ABSTRACT FROM AUTHOR]

Green retrofitting is a potent strategy to improve energy efficiency and support the cultural conservation of historical buildings. China has many historical buildings with significant potential for green retrofitting. Nevertheless, the Chinese green retrofit industry remains in its early stages due to the lack of a comprehensive promotion mechanism. Drawing upon evolutionary game theory, an evolutionary game model was formulated to analyze the interplay among governmental regulators, Energy Service Companies (ESCOs), the owners of historical buildings, and tourists. At an extreme disturbance intensity of 100%, a striking decline is observed in the likelihood of ESCOs maintaining the "green retrofit" strategy, with the probability dropping to a minimum of 24.68%. Under specific conditions however this interplay attains an optimal equilibrium state, denoted as (1,1,1,1). It is concluded that governmental regulators exhibit a greater degree of stability compared to other stakeholders, as they maintain their strategies despite external disturbances. Therefore, it is recommended that effective incentives and regulatory frameworks should consider the sensitivities of non-regulatory stakeholders to enhance the success of green retrofitting projects for historical buildings. [ABSTRACT FROM AUTHOR]

Evolutionary game theory provides a platform to investigate the emergence of cooperation in population consisting of selfish agents. In this work, we study evolutionary games on networks in which agents cooperate or defect according to Q-learning algorithms with extended state space. Extended state space provides agents two types of information, local environment information based on the cooperation level in agents' neighborhood and personal information based on the last action of agents. Through numerical simulations, we find that rich information on local environment tends to improve cooperation in the population no matter whether personal information is present or not. Moreover, we show that, for the same local environment information, the introduction of personal information may improve cooperation except for the situations with low amount of local environment information where personal information deteriorates cooperation in bad-condition environment. For the same total information, the absence of personal information promotes cooperation in bad-condition environment while the presence of personal information promotes cooperation in good-condition environment. By investigating the distributions and temporal behaviors of Q-values, we present explanations for the above statements. This work suggests an effective way of extending the state space in evolutionary games incorporated with Q-learning algorithm to enhance cooperation. [ABSTRACT FROM AUTHOR]

Evolutionary game theory offers an interesting avenue of exploration for populations that are subdivided into smaller groups based on shared traits. Despite being self-contained, interactions between individuals within each group are crucial. These interactions lead to a game with a block-diagonal payoff matrix having blocks of order two or three. A constant negative payoff is assigned to each player, while the background fitness function is inversely proportional to the density of players in the given territory. Through the lens of reaction–diffusion systems, we examine the circumstances necessary for diffusion-driven instability or Turing instability. We derive a set of necessary conditions for Turing instability around the interior equilibrium state. These results reveal that Turing instability occurs when some diagonal elements are positive, or diagonal cofactors of 3-order blocks are negative in the payoff matrix of the game. In summary, this article explores the dynamics of group interactions in population games and identifies key conditions that lead to instability. [ABSTRACT FROM AUTHOR]