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In efforts to resolve social dilemmas, reinforcement learning is an alternative to imitation and exploration in evolutionary game theory. While imitation and exploration rely on the performance of neighbors, in reinforcement learning individuals alter their strategies based on their own performance in the past. For example, according to the Bush–Mosteller model of reinforcement learning, an individual's strategy choice is driven by whether the received payoff satisfies a preset aspiration or not. Stimuli also play a key role in reinforcement learning in that they can determine whether a strategy should be kept or not. Here we use the Monte Carlo method to study pattern formation and phase transitions towards cooperation in social dilemmas that are driven by reinforcement learning. We distinguish local and global players according to the source of the stimulus they experience. While global players receive their stimuli from the whole neighborhood, local players focus solely on individual performance. We show that global players play a decisive role in ensuring cooperation, while local players fail in this regard, although both types of players show properties of 'moody cooperators'. In particular, global players evoke stronger conditional cooperation in their neighborhoods based on direct reciprocity, which is rooted in the emerging spatial patterns and stronger interfaces around cooperative clusters. [ABSTRACT FROM AUTHOR]

Highlight • The result exists an obvious piecewise phenomenon for individual's aspiration level on promoting cooperation Abstract In real life, everyone is full of yearning and pursuit for future life. The difference between reality and ideals encourage people to constantly adjust the current state, so that they can better adapt to changes in the surrounding environment. Thus, we propose a spatial prisoner's dilemma game model where individuals have expectation towards the payoff obtained from nearest neighbors, and individuals can adaptively adjust their update willingness based on the satisfaction of income. Through numerical calculation, we find that different b values will have an appropriate aspiration level to promote cooperation. In addition, the results have an obvious phase transition phenomenon for individual's aspiration level on promoting cooperation. Moreover, only moderate aspiration level can promote the evolution of cooperative behavior, due to it can lead to the heterogeneous distribution of update willingness, which will be beneficial to the evolution of cooperation. [ABSTRACT FROM AUTHOR]

Abstract In real world, cooperation among unrelated agents remains to represent one open challenge in many disciplines. In some cases, subjects can even change their social dilemmas to ensure their own benefit. Inspired by this fact, here we propose a multigame (composed of prisoner's dilemma game and snowdrift game) and its coevolution mechanism in networked populations: if a player succeeds to study the strategy of its opponent, it also learns the game type of its opponent. Based on numerous computation simulations, it is unveiled that compared with the setup of multigame, coevolution mechanism can effectively resolve the problem of collective cooperation. While for this observation, it is attributed to the fact that vast majority of players changed their games to snowdrift game (i.e. lower social dilemma), where cooperation-defection bistable state guarantees cooperation clusters. We hope our finding can inspire more studies for resolving the social dilemmas. [ABSTRACT FROM AUTHOR]

In this paper, we explore the effects of inertia on the evolution of cooperation in voluntary prisoner’s dilemma on a square lattice. In real life, individual inertia is hard to change due to the inherent characteristics of players. Only when he meets lazy and rich people, he will try to change his inertia. Inspired by this, the evolution of inertia is constructed as follows: initially each individual sets a different inertia value due to the heterogeneities among them. Considering loner is a risk averse player, we assume that he has the largest inertia during the simulation. Subsequently, as long as an individual encounters a loner and decides to change his strategy, he will learn his neighbor’s inertia simultaneously. Otherwise, his inertia will remain unchanged. Through a large number of simulations we find that, counterintuitively, individual inertia will impede cooperation. Especially, the larger value of inertia, the less likely the individual to change his strategy, and it severely weakened the effect of rock-scissor-paper cyclic dominance in promoting cooperation by making it happen less and less frequently, further leading to a full loner phase. For a comprehensive understanding, we give an explanation from macroscopic and microscopic aspects. Besides, we use a small-world network to verify the robustness of this mechanism. The results also suggest that individuals can maintain the diversity of the population only if they maintain appropriate enthusiasm to participate in social activities. Our work may provide further insight in understanding the role of inertia on the evolution of cooperation. [ABSTRACT FROM AUTHOR]

• This paper introduces expectation-based reinforcement learning methods in the public goods game. • The results under total population density show that individuals behavior exhibits conditional cooperation and its variant moody conditional cooperation. • In the network with empty nodes, conditional cooperation shows opposite trends. In social dilemmas, individual behavior generally follows the characteristics of conditional cooperation and emotional conditional cooperation. However, it is hard to adequately explain the behavior patterns of conditional cooperation with the evolutionary game theory. This paper introduces expectation-based reinforcement learning methods in the public goods game to investigate and account for the behavior patterns. Instead of letting individuals occupy the entire network as previous studies have done, we focus on studying individual behavior patterns on a network with empty nodes. The results under total population density show the effectivity of our model as they are consistent with those of the previous studies, that is, individuals' behavior exhibits conditional cooperation and its variant moody conditional cooperation. However, in the network with empty nodes, conditional cooperation shows opposite trends. We finally demonstrate that an appropriate population density can facilitate the maintenance and development of cooperation. [ABSTRACT FROM AUTHOR]

We investigate the ability-based fitness calculation in an asymmetric setup to the evolution of cooperation on square lattice. In particular, if players can maintain its strategy for a round, then its abilities will increase 1, otherwise, its ability will turn to 0. We also assume that if players' abilities exceed a certain threshold M , then its environment is integrated into its fitness calculation via a simple parameter α. Through extensive numerical simulation, we find that large threshold M decreases cooperation, and large α, players' fitness is mainly decided by its dynamical environment, promotes cooperation. We also provide theoretical analysis and get the same results with simulation. Finally, we test its robustness on homogeneous random network and small-world network. Our work provides a deeper insight to network reciprocity. [ABSTRACT FROM AUTHOR]

Our social interactions vary over time and they depend on various factors that determine our preferences and goals, both in personal and professional terms. Researches have shown that this plays an important role in promoting cooperation and prosocial behavior in general. Indeed, it is natural to assume that ties among cooperators would become stronger over time, while ties with defectors (non-cooperators) would eventually be severed. Here we introduce reinforcement learning as a determinant of adaptive interaction intensity in social dilemmas and study how this translates into the structure of the social network and its propensity to sustain cooperation. We merge the iterated prisoner's dilemma game with the Bush-Mostelle reinforcement learning model and show that there exists a moderate switching dynamics of the interaction intensity that is optimal for the evolution of cooperation. Besides, the results of Monte Carlo simulations are further supported by the calculations of dynamical pair approximation. These observations show that reinforcement learning is sufficient for the emergence of optimal social interaction patterns that facilitate cooperation. This in turn supports the social capital hypothesis with a minimal set of assumptions that guide the self-organization of our social fabric. [ABSTRACT FROM AUTHOR]

Cooperation in human interactions often involves voluntary participation rather than obligatory requirements. This paper investigates the impact of voluntary exit strategies on cooperation within scale-free networks, employing spatial prisoner's dilemma games as the framework. We analyze three distinct exit strategies: leader priority exit, fringe priority exit, and random exit, across varying dilemma strengths. Our findings reveal that in scenarios of weak dilemma strength, leader priority exit significantly diminishes cooperation, particularly when only a small subset of players is granted exit rights. In contrast, under strong dilemma strength conditions, both leader priority and random exits serve to unexpectedly bolster cooperation. This enhancement is primarily facilitated by high-degree nodes opting out, thereby enabling low-degree nodes to establish stable cooperation. Furthermore, our study underscores the pivotal role of network structure. In assortative networks, random exit emerges as the most effective strategy in promoting cooperation. On the other hand, disassortative networks exhibit a substantial increase in cooperation levels when employing leader priority exit. These results highlight the intricate interplay between exit strategies, dilemma strength, and network topology, offering critical insights for promoting cooperation in varied social contexts. • Study on leaders' priority exit's impact on cooperation dynamics. • Cooperation affected by leader priority exit varies with dilemma strength and network type. • Leader priority exit boosts cooperation in disassortative networks. • Random exit boosts cooperation in assortative networks. [ABSTRACT FROM AUTHOR]

• In this paper, we establish a two-layered multiplex network model which combines SIR epidemic process and vaccination decision-making. • We discover that imitating behavior would restrain the increase of herd immunity, • The model is verified by Monte Carlo simulations and mean-field equa- tions, respectively. • A suitable quantity of conformity motivated individual could be beneficial to save the social costs. When confronting the epidemics or pandemics, there often exists an interplay between epidemic incidence and the vaccination strategies of individuals. Recently, the characteristics of human behaviors, such as imitating nature or bandwagon effect, have been proven critical to the final herd immunity. In this paper, by establishing a two-layered multiplex network model which combines SIR epidemic process, vaccination decision-making and imitating human nature, we discover that imitating behavior would restrain the increase of herd immunity, which is verified by Monte Carlo simulations and mean-field equations, respectively. Interestingly, a suitable quantity of conformity motivated individual, counter-intuitively, could be beneficial to save the social costs. At the same time, we analytically determine the precise conditions for the minimum total social costs. The current results can help to understand the behavior of social group in depth and then explore public attitudes concerning the vaccine, which usually has a tremendous impact on social vaccine take-up within the population. [ABSTRACT FROM AUTHOR]

The free-rider behavior is widespread in the system, which will not only lead to social dilemma, and even make the entire system collapse. In order to overcome this complex problem, scientists have done a strenuous endeavor. The public goods game is significant for the study of cooperative behavior among complex interactive social system. And much attention has been paid to the proposal of reward and punishment system. Although people always want to reward cooperative behavior in many cases, antisocial behavior is also common in complex human and biological communities, and free-riders may be rewarded, especially without their information. Therefore, we study the public good game with neutral reward in order to explore the evolution of cooperation. In our public good game, individuals with a few strategies reward other individuals with most strategies in a same group, and the dominant players will receive a fixed bonus provide by other vulnerable players. We show that increasing the bonus will directly promote cooperation and resolve the social dilemma. [ABSTRACT FROM AUTHOR]

Collective behavior, from murmurations to synchronized beating of heart cells, governs some of the most beautiful and important aspects of nature. Likewise, cooperation—the act of sacrificing personal benefits for the common good—is one of the pillars of social evolution, and it is the basis for the emergence of collective organized actions from single-cell organisms to modern human societies. Here we merge these two phenomena into a single model, considering an ensemble of networked oscillators, where each oscillator can be either a cooperator or a defector, and with only cooperators contributing to synchrony. At the same time, the value of the order parameter in the neighborhood of each oscillator is considered as an effective local temperature which determines the strategy updating procedure in the evolutionary game. The emergence of cooperation is thus intertwined with that of synchronization, producing a novel and fascinating dynamics which includes a double explosive transition. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the feasibility of combining fused deposition modeling (FDM) 3D printing technology with hot melt extrusion (HME) to fabricate a novel controlled-release drug delivery device. Glipizide used in the treatment of diabetes was selected as model drug, and was successfully loaded into commercial polyvinyl alcohol (PVA) filaments by HME method. The drug-loaded filaments were printed through a dual-nozzle 3D printer, and finally formed a double-chamber device composed by a tablet embedded within a larger tablet (DuoTablet), each chamber contains different contents of glipizide. The drug-loaded 3D printed device was evaluated for drug release under in vitro dissolution condition, and we found the release profile fit Korsmeyer–Peppas release kinetics. With the double-chamber design, it is feasible to design either controlled drug release or delayed drug release behavior by reasonably arranging the concentration distribution of the drug in the device. The characteristics of the external layer performed main influence on the release profile of the internal compartment such as lag-time or rate of release. The results of this study suggest the potential of 3D printing to fabricate controlled-release drug delivery system containing multiple drug concentration distributions via hot melt extrusion method and specialized design configurations. [ABSTRACT FROM AUTHOR]

• We introduce third parties and disputants on interdependent network. • Disputants play prisoner's dilemma game and bring third parties extra payoffs. • Third parties intervene disputants play snowdrift dilemma game or not. • Conflicts in disputants are relieved though non-interventions exist in third party layer. Interventions from third parties, such as governmental agencies or organizations, play an important role in mitigating conflicts in modern human societies. The goal thereby is to pacify disputants, although this is subject to failure on account of self-interest from all involved. To study how relationships between disputants and third parties evolve, we propose an interdependent network model, where one layer is occupied by disputants and the other layer is occupied by third parties. Disputants play a prisoner's dilemma game, where defection is the dominant strategy, whereas third parties play a snowdrift game, where cooperation and defection coexist more commonly. Moreover, third parties have the ability to mediate a conflict on the other layer by enforcing a snowdrift game onto disputants, for which they can receive a fee. We show that third party interventions improve the evolution of cooperation between disputants, and also, that the improvement of cooperation in turn promotes interventions. Nonetheless, non-intervention does not go extinct, which enables defectors to survive, thus creating a feedback loop between the two networks layers. The evolutionary dynamics is characterized by fascinating spatial pattern formation, which we explore by means of Monte Carlo simulations and via replicator equations. [ABSTRACT FROM AUTHOR]

• Edge dynamics means that each player in the network can use different strategies for four neighbors. • Interactive diversity is one way of selecting policy referees. • Introduce individual reputation and explore how the co-evolution of reputation and strategy affects cooperative behavior. In view of social interactions, people are more inclined to make different decision-making behaviors based on the credibility of different individuals. In this article, based on edge dynamics, that is, players can freely choose different strategies for different opponents, we introduce individual reputation, which is judged by others based on individual performance, thereby exploring how co-evolution of reputation and strategy affects cooperation behavior. A large number of simulation results show that under the condition of diversity interaction, other people's greater decision-making power on individuals' reputation can significantly promote cooperative behavior. It provides scholars with new insights into the social dilemma under the framework of edge dynamic. [ABSTRACT FROM AUTHOR]

• a mechanism is proposed aiming to investigate the co-evolution of personal reputation and strategy under a general framework of interactive diversity. • Numerous simulations are conducted with sufficient analyses of the obtained results being followed. • The consideration of interaction diversity is able to ensure the maintenance of cooperation even if the temptation of adopting antisocial behavior (here, defection is considered) is relatively large. • Various discussions are performed in order to explain the reason of this effect from various perspectives, for instance, by counting the number of various chains and the evolution of clusters. In practice, there exists numerous examples indicating the phenomenon that individuals are inclined to employ different strategies when interacting with the others. This is incurred by the fact that individuals are able to adjust their strategies adaptively under different interacting environments. Scholars have devoted their efforts to this topic; nevertheless, the impact of diverse interactions on cooperation still needs to be further explored. In this manuscript, we propose a mechanism aiming to investigate the co-evolution of personal reputation and strategy under a general framework of interactive diversity (being referred to as edge-strategy for simplicity). Numerous simulations are conducted with sufficient analyses of the obtained results being provided. As illustrated by the evolutionary dynamics, we find that there exists an optimal reputation value which can promote the frequency of cooperation by a large extent. Furthermore, we can clearly conclude that the consideration of interaction diversity is able to ensure the maintenance of cooperation even if the temptation to adopt antisocial behavior is relatively large. Aiming to understand the phenomenon better, we also quantitatively analyze the results by investigating the statistics of interaction chain, cluster size and other microscopic information. Overall, we hope the findings here can provide some interesting insights in solving social dilemmas. [ABSTRACT FROM AUTHOR]

The emergence and maintenance of cooperation remains to be one of the overreaching problems and a number of scholars have devoted endless efforts to resolve such problems. To further explore this issue, we focus on the impact of heterogeneity of updating rules on cooperative behavior in voluntary prisoner's dilemma; here, two types of strategy updating rules are considered to mimic social facts which are listed as strategy learning mechanism and strategy teaching mechanism. The stability of different mechanisms on promoting cooperation is thoroughly investigated in this manuscript. Numerical simulations are conducted while corresponding results are presented with sufficient clarifications. We find that the strategy learning mechanism can promote cooperation steadily. However, with the variance in the proportion of the two update rules in the investigated community, the strategy teaching mechanism seems to be more sensitive. Moreover, compared with strategy learning mechanism, strategy teaching mechanism plays a more important and apparent role in promoting cooperation. We also find that personal strategy is sensitive to the varying of Loner's payoff σ, especially when σ is close to 1. We hope our findings will shed some lights on resolving the social dilemmas. [ABSTRACT FROM AUTHOR]

Three dimensional (3D) extrusion-based printing is a paste-based rapid prototyping process, which is capable of building complex 3D structures. The aim of this study was to explore the feasibility of 3D extrusion-based printing as a pharmaceutical manufacture technique for the fabrication of gastro-floating tablets. Novel low-density lattice internal structure gastro-floating tablets of dipyridamole were developed to prolong the gastric residence time in order to improve drug release rate and consequently, improve bioavailability and therapeutic efficacy. Excipients commonly employed in the pharmaceutical study could be efficiently applied in the room temperature 3D extrusion-based printing process. The tablets were designed with three kinds of infill percentage and prepared by hydroxypropyl methylcellulose (HPMC K4M) and hydroxypropyl methylcellulose (HPMC E15) as hydrophilic matrices and microcrystalline cellulose (MCC PH101) as extrusion molding agent. In vitro evaluation of the 3D printed gastro-floating tablets was performed by determining mechanical properties, content uniformity, and weight variation. Furthermore, re-floating ability, floating duration time, and drug release behavior were also evaluated. Dissolution profiles revealed the relationship between infill percentage and drug release behavior. The results of this study revealed the potential of 3D extrusion-based printing to fabricate gastro-floating tablets with more than 8 h floating process with traditional pharmaceutical excipients and lattice internal structure design. [ABSTRACT FROM AUTHOR]

The question of resource allocation has been the focus issue in social and natural science. Should we narrow the gap between the rich and poor? Or should we increase the income inequality? Inspired by this matter, we propose a payoff redistribution model and explore which case can evaluate the cooperative level in public goods game. Specifically, we use a single parameter α to control the above two scenarios. Positive α increases the income inequality between rich and poor, while negative α ensures this equality and the case of α = 0 turns our model to the symmetric case, where there is no payoff redistribution effect. Extensive simulation shows that when we narrow the gap between the rich and poor, cooperation can be maintained, conversely, if this income inequality is increased, the cooperative groups are destroyed. Our work thus provides a novel clue to explore this problem especially from the viewpoint of evolutionary game theory. [ABSTRACT FROM AUTHOR]