Network effects in a human capital based economic growth model

Martins, Teresa Vaz; Araújo, Tanya; Santos, M.A.; Aubyn, Miguel St.

doi:10.1016/j.physa.2009.02.006

Cited by 6 publications

(4 citation statements)

References 14 publications

(29 reference statements)

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“…Assumption 1. Consider the parameters given in the time-varying network (1). Without loss of generality, the first node is taken as the reference node.…”

Section: Time-varying Complex Dynamical Network Model and Preliminariesmentioning

confidence: 99%

“…By acknowledging this situation， more and more scholars study human capital through the following four aspects. With regular and random networks being considered, an agent model has been introduced to investigate the influence of the agents' social network [1]. By overcoming the traditional fragmentation of specific research field, the case of the Theme Teams has been analyzed to develop the researchers' human capital [2].…”

Section: Introductionmentioning

confidence: 99%

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Strategic Human Resource Management and Corporate Performance

Wang¹

2019

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It is essential for success to build an organization filled with good, talented, well-trained people, which is also the center of human resource management. The author combined the complex network with human capital to solve the problems. The author employed fuzzy synthetic evaluation method to figure out the Euclidean distance between different departments in ICM, and then built the human capital network of various departments. Based on the Lyapunov stability theory, the author described the dynamic process of human capital in ICM. Through establishing the dynamic complex network model of human capital, the author drew the conclusion that the next two years' budget of recruiting and training is 34.22σ when the annual churn rate goes to 18%. By Pajek programming, the author used the visualization tools to simulate the staff flow. Through establishing the dynamic simulation model of the staff turnover, the author reached the position's integrity condition of ICM when the job churn rate is 25% and 35%, respectively. Then the author explained the costs caused by high turnover rates and the indirect effects of high churn rates. The author simulated the change of position's integrity degree of junior managers and experienced supervisors in the next two years. The author concluded that the HR health of the organization is below the expectation. In addition, the author made sensitivity analysis on the 7 factors of human capital value and consequently drew that the amount of employees of each hierarchy has the greatest influence on human capital value. At last, to make further discussion on the model, the author established the multilayer complex network model including flow, trust, influence and friendship.

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“…Assumption 1. Consider the parameters given in the time-varying network (1). Without loss of generality, the first node is taken as the reference node.…”

Section: Time-varying Complex Dynamical Network Model and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strategic Human Resource Management and Corporate Performance

Wang¹

2019

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“…The ABM is beginning to be used in diverse areas such as ecology [20], ecological economics [19,21], economic growth based on the production of ideas [22], and climate change [23,24]. Janssen and De Vries [25] presented a very interesting work on climate change that includes the concept of agents' choice.…”

Section: Introductionmentioning

confidence: 99%

Development of an Agent-Based Model and Its Application to the Estimation of Global Carbon Emissions

Castesana¹,

Puliafito²

2013

LCE

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With the purpose of studying the influence of population dynamics and economic growth on energy consumption and carbon emissions, an endogenous economic growth model is proposed incorporating physical and human capital and using an Agent-Based Model. The model can test different development strategies by identifying the key factors existing at the agent level that may speed up or slow down a given path, and therefore it is an interesting tool to develop and to test mitigation and/or adaptation measures. Favorable scenarios may be possible in societies that encourage investment in human capital through education and technological development, provided that this is accompanied by a reduction in consumption rates and the creation of physical capital by the population. Moreover, this model shows that human capital resulting from education not only raises productivity, but also plays a key role in the development and adoption of new technologies that drive long-term growth.

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“…A characteristic feature of stratified societies is that individuals tend to interact more strongly with others in their own group. This tendency has been observed in class endogamy [2], scientific communities and citations [3], population biology [4], human capital [5], opinion formation [6], epidemic dynamics [7], and economic exchanges between banks [8]. Recently, the effects of social stratification on the wealth distribution of a system of interacting economic agents have been studied [9].…”

Section: Introductionmentioning

confidence: 99%

Stratified economic exchange on networks

Herrera

Cosenza

Tucci

2011

Physica A: Statistical Mechanics and its Applications

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We investigate a model of stratified economic interactions between agents when the notion of spatial location is introduced. The agents are placed on a network with near-neighbor connections. Interactions between neighbors can occur only if the difference in their wealth is less than a threshold value that defines the width of the economic classes. By employing concepts from spatiotemporal dynamical systems, three types of patterns can be identified in the system as parameters are varied: laminar, intermittent and turbulent states. The transition from the laminar state to the turbulent state is characterized by the activity of the system, a quantity that measures the average exchange of wealth over long times. The degree of inequality in the wealth distribution for different parameter values is characterized by the Gini Coefficient. High levels of activity are associated to low values of the Gini coefficient. It is found that the topological properties of the network have little effect on the activity of the system, but the Gini coefficient increases when the clustering coefficient of the network is increased

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Network effects in a human capital based economic growth model

Cited by 6 publications

References 14 publications

Strategic Human Resource Management and Corporate Performance

Strategic Human Resource Management and Corporate Performance

Development of an Agent-Based Model and Its Application to the Estimation of Global Carbon Emissions

Stratified economic exchange on networks

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