Any Non-Welfarist Method of Policy Assessment Violates the Pareto Principle: Reply

2004 ◽  
Author(s):  
Louis Kaplow ◽  
Steven Shavell
Keyword(s):  
Pareto Principle ◽  
Policy Assessment

Any Non‐welfarist Method of Policy Assessment Violates the Pareto Principle: A Comment

2003 ◽  
Vol 111 (6) ◽  
pp. 1382-1385 ◽  
Author(s):  
Marc Fleurbaey ◽  
Bertil Tungodden ◽  
Howard F. Chang
Keyword(s):  
Pareto Principle ◽  
Policy Assessment

Any Non‐welfarist Method of Policy Assessment Violates the Pareto Principle: Reply

2004 ◽  
Vol 112 (1) ◽  
pp. 249-251 ◽  
Author(s):  
Louis Kaplow ◽  
Steven Shavell
Keyword(s):  
Pareto Principle ◽  
Policy Assessment

scholarly journals Any Non‐welfarist Method of Policy Assessment Violates the Pareto Principle

2001 ◽  
Vol 109 (2) ◽  
pp. 281-286 ◽  
Author(s):  
Louis Kaplow ◽  
Steven Shavell
Keyword(s):  
Pareto Principle ◽  
Policy Assessment

scholarly journals Drawing transmission graphs for COVID-19 in the perspective of network science

2020 ◽  
Vol 148 ◽  
Author(s):  
N. Gürsakal ◽  
B. Batmaz ◽  
G. Aktuna
Keyword(s):  
Probability Distribution ◽  
Network Science ◽  
Pareto Distribution ◽  
Transmission Probability ◽  
Transmission Network ◽  
Pareto Principle ◽  
Infected People ◽  
Discrete Pareto Distribution ◽  
Local Transmission

Abstract When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.

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