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Heterogeneity has attracted mounting attention across multiple disciplines and is confirmed to be a greater promoter of cooperation. It is often the case that the heterogeneity always exists in investment and payoff allocation concurrently instead of separately. In addition, the factors that affect heterogeneous investment and payoff allocation are various. Inspired by this, this paper extends the previous models by incorporating heterogeneous investment and payoff allocation into the typical PGG model to further investigate the incentive mechanisms of cooperative behavior. In order to better understand the model, three different feedback mechanisms, namely the wealth-preference mechanism, the social-self-preference mechanism, and the mixed-preference mechanism, are addressed. The former two mechanisms correspond to the case of single factor and the latter corresponds to the case of double factors. The numerical simulations indicate that feedback mechanism by bridging the connections between the investment and the payoff allocation can reduce the free-rider problem. Furthermore, it is found that the cooperative frequency and average payoff perform better in the case of the mixed-preference mechanism where players will not only take previous payoff feedback as well as current investment but also their social status into their game decision-making process. In addition, full cooperation and profitability over all players can be promoted by means of increasing r or reducing α . At last, compared with another two classic networks, the extent of cooperation is promoted under the structures of the BA scale free networks. [ABSTRACT FROM AUTHOR]

Abstract The relationships in human society are heterogeneous and dynamically change with interactions, which have a strong effect on individual’s learning behaviors. In this light we present a new mechanism of preferential learning based on dynamic relationship into evolutionary spatial prisoner’s dilemma game to further investigate the incentive mechanisms of cooperative behaviors. In detail, we consider that the strength of relationship between pairwise individuals adaptively changes according to their mutual strategies and the adjusting rate is related to individuals’ sensitivity to interactions. Based on the heterogeneous and dynamic relationship, individuals prefer neighbors with stronger relationship to learn from instead of learning randomly The learning preference is measured by the preference intensity. By means of Monte Carlo simulations, we find that both the sensitivity factor and the preference intensity have multiple effects on the evolution of cooperation. Furthermore, to validate the multiple effects in a microcosmic view, strategy transitions during the evolution are also discussed. Interestingly, we find that there exists a trade-off between the influence of the sensitivity factor and the preference intensity on the evolution of cooperation. Presented results are robust to variations of the network structures and may provide a new understanding to the emergence of cooperative behaviors. Highlights • Introduce dynamic relationship-based preferential learning mechanism into evolutionary prisoner’s dilemma game model. • Construct a method to update the strength of relationship according to mutual strategies. • Both the sensitivity factor and the preference intensity has multiple effects on promotion of cooperation. • There exists a trade-off between the influences of the sensitivity factor and the preference intensity. [ABSTRACT FROM AUTHOR]

Food waste can meet diverse demand if treated harmlessly while may bring severe environmental problems if managed improperly. China is currently trying to facilitate food waste harmless disposal and resource utilization by urban food waste management. However, conflicts of interest among government departments, restaurants and waste disposal companies often make it difficult to achieve effective urban food waste management. This paper conducts a theoretical modeling and simulation analysis of behavioral strategy interaction of the above three participants by a combination of evolutionary game and system dynamics approach. To avoid opportunistic behaviors and regulation inefficiency that occurred in fixed penalty scheme (F-scheme), we introduce random inspection penalty scheme (R-scheme) to the tripartite evolutionary game in urban food waste management. Then an in-depth scenario analysis under F-scheme and R-scheme is carried out to investigate the influences of key variables on strategy evolution of participants. The results indicate that, compared with F-scheme, R-scheme contributes to both system stability promotion and practicality enhancement. Food waste treatment fee and regulation cost are influencing factors that respectively affect the strategic choices of restaurants and government departments. To a certain extent, exposure probability that represents informal regulation of the public and NGO can improve urban food waste management. Therefore, it is critical for government departments to adopt R-scheme, take advantage of the informal regulation of the public and NGO, gradually reduce food waste treatment fee and maintain a reasonable regulation cost to improve urban food waste management, thereby promoting food waste harmless disposal and resource utilization. • Penalty under R-scheme can be enforced according to random inspection results. • We use a SD model of evolutionary game to study urban food waste management. • R-scheme contributes to both system stability promotion and practicality enhancement. • Exposure probability can to a certain extent improve urban food waste management. • Policy recommendations for urban food waste management are proposed. [ABSTRACT FROM AUTHOR]

• We investigate the transmission of AQI fluctuation by complex network theory. • AQI networks of various time periods display small-world effect. • It is observed that some extreme short-term AQI fluctuation can be controlled. • Complex network analysis contributes to the prediction of AQI fluctuation. • Policy recommendations for environment management are proposed. Although many works have focused on the issues related to Air Quality Index (AQI) from the perspective of statistics or economics, the dynamic and temporal characteristics of AQI time series remain unclear. The main reason is that AQI time series has both non-linearity and complexity, which cannot be characterized by conventional ways. The inherent transmission laws of AQI volatility are of great importance for environment monitoring and management system. Thus, this paper has endeavored to address the issue from the interdisciplinary perspective of complex network theory. First, we collect the annual and the total AQI data of three cities (Shanghai, Wuhan and Guangzhou) in China from January 1, 2015 to December 31, 2017. Then the coarse graining method is employed in combination with the theory of phase space reconstruction to convert AQI time series into complex network. The coarse graining method is used to transform yearly and total AQI time series into the corresponding symbol sequences. In accordance with the theory of phase space reconstruction, we adopt C C method and false nearest neighbor algorithm to estimate the optimal time delay and embedding dimension of AQI time series, respectively. The optimal time delay is integrated with the embedding dimension to map symbol sequences into AQI complex network. Based on complex network theory, we investigate the transmission of AQI fluctuation by the temporal characteristics and dynamics of yearly AQI networks (YAN) and total AQI networks (TAN), which include node strength, betweenness centrality and community structure. The empirical results show: (1) the AQI networks of various periods display small-world effect; (2) network evolution tends to be more complex and the modality in YAN has less alternative transformation paths than that in TAN; (3) the node strength and betweenness centrality in measuring the importance of nodes achieve a relative consistency; (4) the three cities have the same dominant fluctuation modalities both in YAN and in TAN; (5) the uneven distribution of transmission ability between clusters implies a preferential transmission whether in YAN or TAN. These findings not only help us to interpret the frequent volatility of AQI and control some extreme short-term AQI fluctuation, but also contribute to the reliability assessment of AQI and the prediction of AQI fluctuation. In summary, this paper not only reveals the temporal dynamic properties of AQI time series, but also provides some policy recommendations for environmental monitoring and management. [ABSTRACT FROM AUTHOR]