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• Based on an activity-driven network, a model dividing individuals into two types, one is risk-ignorant, and the other is risk-averse who may reduce the potential risk of infection by being less social intensity is established. • The outbreak of an epidemic depends most on the proportion of risk-averse individuals. • A large number of risk-averse individuals who change their social intensity raises the theoretical threshold and reduces the epidemic size, thus suppressing the outbreak. • As risk-averse individuals increases, the epidemic is eliminated under small spreading rates. In human society, individual interactions intrinsically change, with profound implications for epidemics. The activity-driven model, a type of temporal network, offers an excellent framework to study epidemic processes in dynamical interaction. In this work, we study how social attitudes affect the transmission of infectious diseases in activity-driven networks. Here, we divide a population into "risk-ignorant" and "risk-averse", in which risk-averse individuals will reduce their social intensity (Social intensity refers to the number of social contacts in the social process) and risk-ignorant individuals will not. A parameter p controls the proportion of risk-averse individuals, and therefore risk-ignorant individuals by 1- p. With the aid of mean-field theory, we calculate epidemic thresholds, as well as validate theoretical predictions with extensive Monte Carlo simulations. It is shown numerically and theoretically that reducing the social intensity and increasing the number of risk-averse individuals are effective ways of controlling epidemic outbreaks. An appropriate proportion of the risk-averse individual will lead to an epidemic die-out, which is based on a small spreading rate. Our research provides a new perspective for understanding the effect of the population with different social attitudes in the epidemic process. [ABSTRACT FROM AUTHOR]

The maintenance of biological diversity has perpetually remained a central focus in the field of ecology. In the pursuit of enhanced survival rates, species have begun to explore cooperation with one another. However, the consequences of such collaboration remain largely unexplored. To delve into this matter, we introduce intraspecific cooperation within the framework of the classic rock-paper-scissors (RPS) game. In this model, the competition rate is intricately tied to interactions among individuals of the same species. A greater population of individuals from the same species tends to lead to an increased predation rate and a decreased prey rate. Through extensive simulations, we observe that (i) in the case of homogeneous intraspecific cooperation (all three species have intraspecific cooperation), increased cooperation between predators tends to increase the likelihood of species coexistence. In contrast, high levels of cooperation between prey appeared to decrease the favorability of species coexistence. Measurements of the characteristic length of spiral structures revealed that the characteristic length of spirals became longer when the intensity of prey cooperation increased. (ii) In the case of heterogeneous intraspecific cooperation (two species or only one species with intraspecific cooperation), neither an increase in the intensity of intraspecific cooperation of the predator nor an increase in the intensity of intraspecific cooperation of the prey is favorable for species coexistence. Our work underscores the critical role of intraspecific cooperation in maintaining biodiversity. • In rock-paper-scissors games, intraspecific cooperation promotes and jeopardizes biodiversity. • Under homogeneous intraspecific cooperation, predator cooperation promotes coexistence. • Under heterogeneous intraspecific cooperation, both predator and prey cooperation jeopardize coexistence. [ABSTRACT FROM AUTHOR]

In the real world, a single homogeneous or heterogeneous network is very rare, so we consider a compound model which contains two different types of subnetworks. The compound model may be applied to analyze the contagion dynamics of two different communities where they have a probability to communicate. The global infection threshold is obtained, and many factors affecting spread of epidemics are analyzed, including subnetwork density, subnetwork size, connecting probability and the maximum degree. An interesting phenomenon is that we find the transition points of the global threshold from increasing to decreasing, which was not mentioned in previous work. Therefore, we give many measures to improve infection threshold and prevent the spread of epidemics. The experimental results confirm the conclusions of epidemic analysis. • The spreading process of epidemics on a mixed model is studied. • The transition points of epidemic threshold from increasing to decreasing are given. • Many factors to affect the global epidemic threshold are analyzed. • Many measures to prevent the spread of epidemics are proposed. [ABSTRACT FROM AUTHOR]

• Aspiration-based exit option in prisoners dilemma game can promote cooperation. • In structured population, the intermediate aspiration level promotes cooperation, because defectors around the cooperative cluster exit the game, which weakens the exploitation of cooperators, and help the cooperators at the edge of the cooperative cluster to expand to inactive nodes or the defectors through network reciprocity. • The combined effect of aspiration level and dilemma strength affects the active state of players. Cooperation is promoted in the structured population when individual has the exit option and it is found that aspiration driven updating rules can also promote cooperation to a certain extent. The right to exit gives individuals the opportunity to get rid of exploitation, and the aspiration is the self-evaluation of individuals. Stimulated by these, we study the effect of aspiration driven exit on cooperation in the spatial prisoner dilemma game. In the game, the aspiration level and payoff of interactive determine the individual's state. When the individual gets more than aspiration level by interacting with other players, the individual will continue to participate in the game as an active player. On the contrary, when the payoff is lower than the aspiration level, individual exits the game as an inactive player. At the same time, inactive player who exits from the game will get random payoff and pay a certain cost. The promotion of cooperative behavior mainly depends on the aspiration level of players. An appropriate level of aspiration will lead to a high cooperation, which is based on the formation of cooperative clusters. These cooperators can get rid of the invasion of defectors, even if there is a great temptation of defection, this is because the inactive defectors who exit have cut off the interaction between the cooperative cluster and the defectors. Our research provides a feasible way to solve the social dilemma and will stimulate further application. [ABSTRACT FROM AUTHOR]

• We consider the repeated pairwise prisoner's dilemma game among two groups of agents. • We found that the mixed strategy-update rule (Q-learning-based update rule and Fermi-function-based update rule) can evaluate cooperation. • If the proportion of AI is moderate, cooperators among the whole population exhibit conditional behavior and moody conditional behavior. Recent studies show that different update rules are invariant regarding the evolutionary outcomes for a well-mixed population or homogeneous network. In this paper, we investigate how the Q-learning algorithm, one of the reinforcement learning methods, affects the evolutionary outcomes in square lattice. Especially, we consider the mixed strategy update rule, among which some agents adopt Q-learning method to update their strategies, the proportion of these agents (these agents are denoted as Artificial Intelligence (AI)) is controlled by a simple parameter ρ. The rest of other agents, the proportion is denoted by 1 − ρ, adopt the Fermi function to update their strategies. Through extensive numerical simulations, we found that the mixed strategy-update rule can facilitate cooperation compared with the pure Fermi- function-based update rule. Besides, if the proportion of AI is moderate, cooperators among the whole population exhibit conditional behavior and moody conditional behavior. However, if the whole population adopts the pure Fermi-function-based strategy update rule or the pure Q-learning-based strategy update rule, then cooperators among the whole population exhibit the hump-shaped conditional behavior. Our results provide a new insight to understand the evolution of cooperation from AI's view. [ABSTRACT FROM AUTHOR]