An Agent–Based Model of Tax Compliance with Social Networks

2007 ◽  
Vol 60 (3) ◽  
pp. 589-610 ◽  
Author(s):  
Adam Korobow ◽  
Chris Johnson ◽  
Robert Axtell
2008 ◽  
pp. 13-25 ◽  
Author(s):  
Kim M. Bloomquist

This chapter describes the development of a prototype multi-agent based model – the Tax Compliance Simulator (TCS) – designed to help tax administrators think about ways to reduce tax evasion. TCS allows the user to define unique behavioral, income, and tax enforcement characteristics for one or two distinctive taxpayer populations. The capabilities of the model are demonstrated in a simulation of the deterrent effects of taxpayer audits. The simulation finds that a significant portion of audit-based deterrence may depend on the influence of taxpayers’ social networks rather than the probability of detection or penalty for underreporting as indicated by economic theory (Allingham and Sandmo, 1972).


2014 ◽  
Vol 40 ◽  
pp. 119-133 ◽  
Author(s):  
Amanda L. Andrei ◽  
Kevin Comer ◽  
Matthew Koehler

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-1
Author(s):  
Mario A. Bertella ◽  
Jonathas N. Silva ◽  
André L. Correa ◽  
Didier Sornette


2019 ◽  
Vol 28 (4) ◽  
pp. 394-412 ◽  
Author(s):  
Björn Ross ◽  
Laura Pilz ◽  
Benjamin Cabrera ◽  
Florian Brachten ◽  
German Neubaum ◽  
...  

2020 ◽  
Author(s):  
Giuseppe Giacopelli

UNSTRUCTURED COVID-19 outbreak is an awful event. However it gives to the scientists the possibility to test theories about epidemic. The aim of this contribution is to propose a individual-based model of Lombardy COVID-19 outbreak at full-scale, where full-scale means that will be simulated all the 10 millions inhabitant population of Lombardy person by person, in a commercial computer. All this to test the impact of our daily actions in epidemic, investigate social networks connectivity and in the end have an insight on the impact of an hypothetical vaccine.


Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Jun Pan ◽  
Huizhang Shen ◽  
Zhong Chen

Mass incidents, which may influence the stability and security of the society in China, are getting more and more attentions not only from policy makers but also from Chinese social researchers. Catching the diffusion mechanism is believed to be critical to understand essential of these mass incidents since message dissemination plays important roles in every stage of mass incident. Recently, online social networks including Weibo (Chinese Twitter) become more and more popular in China. There are reports showing that Weibo discussion has accompanied the processes of most mass incidents happening in China in these few years. So, in this paper, we aim at introducing K-Core decomposition method from complex network to the analysis on how to manage the diffusion of mass incident in Weibo based on agent based model which can simulate Weibo user’s actions when mass incident happens. This work can help people understand how mass incident messages spread across the network. And then, people may have better strategy to manage the diffusion of mass incidents.


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