Research on Public Rental Housing PPP Project Launching Dilemmas with Dynamic Evolutionary Game

The public housing PPP projects have encountered a cold reception from the government, which constrained solving the urban housing problem. This paper builds a dynamic game model under incomplete contract conditions, analyzes the key factors affecting the signing of PPP contracts by dynamic evolutionary game analysis, and verifies these factors by simulation. The results show that fiscal spending smoothing, risk transfer, and government performance can promote government to adopt cooperation strategy. Expected project benefits and government performance incentives can promote private capital to adopt cooperation strategy. Changes in transaction cost have a significant impact on the decision of cooperation strategy.

The Mechanism Causing an Increase in Farmland Transfer Rent and the Restraining Effect of High Rent on Grain Production

The rent of farmland transfer represents the economic realization of farmland contracts and management rights of agricultural households. A three-stage dynamic game model with three players is constructed in this paper to study the mechanism causing an increase in farmland transfer rent. Based on the theory of producer equilibrium and production possibility boundaries, this paper studies the restraining effect of high rent on grain production and analyzes the factors that prevent the decline in grain production at present. Combined with the process of farmland transfer in a village, both the mechanism causing an increase in farmland transfer rent and the restraining effect of high rent on grain production are empirically analyzed. The conclusion is as follows: the basic direction of farmland transfer is from farmers with a low production capacity to farmers with a high production capacity, and the rent level is determined by the transferors with high production capacity; about half of the economies of scale profits and two-thirds of transferees’ subsidies are converted into farmland transfer rents. High farmland transfer rent reinforces “nongrain” and “nonagricultural” behaviors. Finally, it is suggested that farmers should be given vocational training in agricultural production, “farmland transfer tax” should be levied on excessive farmland transfer rent, and transferees should be subsidized for grain production.

A Game Theoretic Analysis of Cryptocurrency Market for Sustainable Development of Smart Cities

Cryptocurrency was first began to use in 2009 and features technology such as blockchain that is used in the development of smart cities. Game theory play an important role when designing secure economic system such as Bitcoin. In this paper, we study a dynamic game model of brand advertising in continuous time with the finite time horizon. The total payoff or profit of players or firms is discounted over time and the rate at which the firms attract the customers from its rival, called attraction rate, is taken from the economic theory of contest. As a novelty of our game model, the attraction rate of a firm depends on both: player’s or firm’s advertising effort and the advertising effort of players or firm’s rivalry. We solve the game for Stackelberg equilibrium (with the leader and follower differing by information and/or the sequence of choosing strategies) as well as for Nash equilibrium. Further, we graphically compare the results obtained for the Stackelberg and the Nash equilibrium. Surprisingly, we have obtained that if the revenue per unit of market share is equal for Nash and Stackelberg equilibrium then the results for Nash player 1 overlaps with the leader and the results for Nash player 2 overlaps with follower. Finally, we present the application of our dynamic game model in the cryptocurrency market where two firms compete with each other for the cryptocurrency market share.

A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection

Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.

Tripartite Coordinated Regulation of New Energy Vehicles Based on Dynamic Bayesian Game

Manufacturers are disseminating false or ambiguous information regarding new energy vehicles (NEVs), which has led to skepticism from consumers about the quality of NEVs. In this research, we simultaneously considered the relationship among manufacturers, consumers, and governments from the perspective of stakeholders, and then we analyzed the tripartite coordinated regulation. In view of the serious information asymmetry of NEVs, we innovatively developed the Bayesian dynamic game model. By solving refined Bayesian equilibrium strategies, this study explores the effects of key influencing factors on strategic choices. On the basis of the conclusion, relevant countermeasures and suggestions are put forward to engender effective regulation by governments.

Green Remanufacturer’s Mixed Collection Channel Strategy Considering Enterprise’s Environmental Responsibility and the Fairness Concern in Reverse Green Supply Chain

In reverse green supply chain, the mixed collection channel strategy of green remanufacturer is analyzed by building a dynamic game model in which we consider that the green remanufacturer undertakes the environmental responsibility and the green collector shows strong fairness concern for the profit. We analyze the impact of the environmental responsibility level of the green remanufacturer, the preference coefficient of the green remanufacturer, the fairness concern coefficient of the green collector, and the coefficient of cross collection price on optimal decision and profit of the green remanufacturer. The result shows that (1) the green remanufacturer can further improve the collection price, so that it makes many more customers participate in the collection activity; (2) the green remanufacturer pays more attention to fulfill the environmental responsibility, which will increase the intensity of collection of the waste green product, and improve the collection price, as the old green product’s remanufacturing cost is lower than the production cost of the new product, and it can improve the green remanufacturer’s profits; (3) the green remanufacturer’s profit in the mixed collection channel is higher than those in online or offline collection channels.

To Isolate or Not To Isolate: The Impact of Changing Behavior on COVID-19 Transmission

The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. Neither vaccines nor therapeutic drugs are currently available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

To isolate or not to isolate: The impact of changing behavior on COVID-19 transmission

The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. Neither vaccines nor therapeutic drugs are currently available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.