PubMed Health⌕ Search

PubMed · 11930782

Wealth happens.

Abstract

The economic world is full of patterns, and one of the most controversial is the distribution of wealth. You might expect the balance between rich and poor to vary widely from country to country. But back in 1897, Vilfredo Pareto discovered a pattern of wealth distribution that appears to be universal. Whenever you double the amount of wealth within a country, the number of people in each successively higher wealth bracket falls by a constant factor. The factor varies among countries, but the pattern remains essentially the same. From a mathematical standpoint, Pareto's distribution has stubbornly defied explanation. But recently, researchers were able to replicate the curve by applying the principles of network organization. They began with two simple assumptions. First, wealth accumulates either by transfers from person to person or through investment returns, positive or negative. Second, rich people invest more money than poor people. Starting with a hypothetical group of 1,000 people of equal wealth and abilities, the model always produces Pareto's wealth distribution no matter how the links in the network are organized or how the balance between interpersonal transactions and investment returns are set. The model also indicates that the degree of wealth concentration can be influenced. Increasing the number of links in the network or the total amount of money flowing through an economy tends to decrease wealth disparities; increasing investment returns or volatility tends to increase it. Replete with public policy implications, the model is only one example of how network analysis can reshape our understanding of complex economic and social systems, which may have less to do with the behavior of individual members than with impersonal and seemingly insignificant forces.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mark Buchanan. 2002. Wealth happens.. https://pubmed.ncbi.nlm.nih.gov/11930782/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Rates and probabilities in economic modelling: transformation, translation and appropriate application.

Economic modelling is increasingly being used to evaluate the cost effectiveness of health technologies. One of the requirements for good practice in modelling is appropriate application of rates and probabilities. In spite of previous descriptions of appropriate use of rates and probabilities, confusions persist beyond a simple understanding of their definitions. The objective of this article is to provide a concise guide to understanding the issues surrounding the use of rates and probabilities reported in the literature in economic models, and an understanding of when and how to transform them appropriately. The article begins by defining rates and probabilities and shows the essential difference between the two measures. Appropriate conversions between rates and probabilities are discussed, and simple examples are provided to illustrate the techniques and pitfalls. How the transformed rates and probabilities may be used in economic models is then described and some recommendations are suggested.

Models, Economic↗

Improving option pricing with the product constrained hybrid neural network.

In the past decade, many studies across various financial markets have shown conventional option pricing models to be inaccurate. To improve their accuracy, various researchers have turned to artificial neural networks (ANNs). In this work a neural network is constrained in such a way that pricing must be rational at the option-pricing boundaries. The constraints serve to change the regression surface of the ANN so that option pricing accuracy is improved in the locale of the boundaries. These constraints lead to statistically and economically significant out-performance, relative to both the most accurate conventional and nonconventional option pricing models.

Models, Economic↗