On the role of hypocrisy in escaping the tragedy of the commons

We study the emergence of cooperation in large spatial public goods games. Without employing severe social-pressure against “defectors”, or alternatively, significantly rewarding “cooperators”, theoretical models typically predict a system collapse in a way that is reminiscent of the “tragedy-of-the-commons” metaphor. Drawing on a dynamic network model, this paper demonstrates how cooperation can emerge when the social-pressure is mild. This is achieved with the aid of an additional behavior called “hypocrisy”, which appears to be cooperative from the external observer’s perspective but in fact hardly contributes to the social-welfare. Our model assumes that social-pressure is induced over both defectors and hypocritical players, though the extent of which may differ. Our main result indicates that the emergence of cooperation highly depends on the extent of social-pressure applied against hypocritical players. Setting it to be at some intermediate range below the one employed against defectors allows a system composed almost exclusively of defectors to transform into a fully cooperative one quickly. Conversely, when the social-pressure against hypocritical players is either too low or too high, the system remains locked in a degenerate configuration.

Infection Percolation: A Dynamic Network Model of Disease Spreading

Models of disease spreading are critical for predicting infection growth in a population and evaluating public health policies. However, standard models typically represent the dynamics of disease transmission between individuals using macroscopic parameters that do not accurately represent person-to-person variability. To address this issue, we present a dynamic network model that provides a straightforward way to incorporate both disease transmission dynamics at the individual scale as well as the full spatiotemporal history of infection at the population scale. We find that disease spreads through a social network as a traveling wave of infection, followed by a traveling wave of recovery, with the onset and dynamics of spreading determined by the interplay between disease transmission and recovery. We use these insights to develop a scaling theory that predicts the dynamics of infection for diverse diseases and populations. Furthermore, we show how spatial heterogeneities in susceptibility to infection can either exacerbate or quell the spread of disease, depending on its infectivity. Ultimately, our dynamic network approach provides a simple way to model disease spreading that unifies previous findings and can be generalized to diverse diseases, containment strategies, seasonal conditions, and community structures.

A smooth dynamic network model for patent collaboration data

The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.

SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

Associated Effects and Efficiency Evaluation between Wastewater Pollution and Water Disease Based on the Dynamic Two-Stage DEA Model

The lack of basic water supply and treatment facilities during China’s urbanization and industrialization process has resulted in a large amount of wastewater pollution, with the most serious water diseases being water-borne endemic fluorosis and arsenic poisoning, which have affected more than 20 million people. This research therefore uses the improved modified undesirable dynamic network model to analyze data of 31 provincial administrative regions to focus on the associated effects and efficiency evaluation between wastewater pollution and water disease in China. The results show that the efficiency of water pollution disease in all four regions of the country and the total efficiency in the east, west, and central regions all show a decreasing trend, while the efficiency scores and rankings of all provinces and cities within the region fluctuate greatly. The eastern region with the most developed economy has the best overall performance, with higher efficiency in water consumption and water disease control. However, the efficiency of wastewater treatment in northeast China is stable and better. Given the high level of the nation’s economic development and the results of efficiency in water pollution and water diseases, improving the efficiency and quality of wastewater treatment in China is regarded as an important factor for achieving the strategic goal of green growth.

A Dynamic Network Model of Interbank Lending—Systemic Risk and Liquidity Provisioning

We develop a dynamic model of interbank borrowing and lending activities in which banks are organized into clusters, and adjust their monetary reserve levels to meet prescribed capital requirements. Each bank has its own initial monetary reserve level and faces idiosyncratic risks characterized by an independent Brownian motion, whereas system wide, the banks form a hierarchical structure of clusters. We model the interbank transactional dynamics through a set of interacting measure-valued processes. Each individual process describes the intracluster borrowing/lending activities, and the interactions among the processes capture the intercluster financial transactions. We establish the weak limit of the interacting measure-valued processes as the number of banks in the system grows large. We then use the weak limit to develop asymptotic approximations of two proposed macromeasures (the liquidity stress index and the concentration index), both capturing the dynamics of systemic risk. We use numerical examples to illustrate the applications of the asymptotics and conduct-related sensitivity analysis with respect to various indicators of financial activity.