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An approach for the accurate measurement of social morality levels.

Liu H, Chen X, Zhang B - PLoS ONE (2013)

Bottom Line: Based on the NetLogo platform, this article examines the effect of various factors on social morality levels, using agents modeling moral behavior, immoral behavior, and a range of environmental social resources.The results show that: (1) Population size may accelerate or impede the speed with which immoral behavior comes to determine the overall level of social morality, but it has no effect on the level of social morality itself; (2) The impact of rewards and punishment on social morality levels follows the "5∶1 rewards-to-punishment rule," which is to say that 5 units of rewards have the same effect as 1 unit of punishment; (3) The abundance of public resources is inversely related to the level of social morality; (4) When the cost of population mobility reaches 10% of the total energy level, immoral behavior begins to be suppressed (i.e. the 1/10 moral cost rule).The research approach and methods presented in this paper successfully address the difficulties involved in measuring social morality levels, and promise extensive application potentials.

View Article: PubMed Central - PubMed

Affiliation: School of Education in Ideology and Politics, China University of Geosciences (Beijing), Beijing, China.

ABSTRACT
In the social sciences, computer-based modeling has become an increasingly important tool receiving widespread attention. However, the derivation of the quantitative relationships linking individual moral behavior and social morality levels, so as to provide a useful basis for social policy-making, remains a challenge in the scholarly literature today. A quantitative measurement of morality from the perspective of complexity science constitutes an innovative attempt. Based on the NetLogo platform, this article examines the effect of various factors on social morality levels, using agents modeling moral behavior, immoral behavior, and a range of environmental social resources. Threshold values for the various parameters are obtained through sensitivity analysis; and practical solutions are proposed for reversing declines in social morality levels. The results show that: (1) Population size may accelerate or impede the speed with which immoral behavior comes to determine the overall level of social morality, but it has no effect on the level of social morality itself; (2) The impact of rewards and punishment on social morality levels follows the "5∶1 rewards-to-punishment rule," which is to say that 5 units of rewards have the same effect as 1 unit of punishment; (3) The abundance of public resources is inversely related to the level of social morality; (4) When the cost of population mobility reaches 10% of the total energy level, immoral behavior begins to be suppressed (i.e. the 1/10 moral cost rule). The research approach and methods presented in this paper successfully address the difficulties involved in measuring social morality levels, and promise extensive application potentials.

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Moral computing simulation flowchart.This figure shows the basic principle of social moral level by quantitative analysis. As a starting point, we divide social agents into moral and immoral actors, and given their initial energy value. They move to gain or lose energy in the social environment. Public resources, agent energy value accord to set rules for interaction. Finally, we can get the level of social morality.
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pone-0079852-g001: Moral computing simulation flowchart.This figure shows the basic principle of social moral level by quantitative analysis. As a starting point, we divide social agents into moral and immoral actors, and given their initial energy value. They move to gain or lose energy in the social environment. Public resources, agent energy value accord to set rules for interaction. Finally, we can get the level of social morality.

Mentions: Patches are a static background which form a grid, don't move and have integer coordinates; turtles move on top of the patches, not necessarily in their center and have decimal coordinates and orientation and have different forms in different models, such as person, cattle, triangle, etc.; observer can create new turtles, and can have read/write access to all the agents and variables. Modelers can set the properties or rules of agents through NetLogo commands, in order to achieve a variety of simulation or emulation. Netlogo is a free software; and one can download the latest version software from the web site of NetLogo homepage [28]. The NetLogo interface of the simulation model is shown in Figure 1.


An approach for the accurate measurement of social morality levels.

Liu H, Chen X, Zhang B - PLoS ONE (2013)

Moral computing simulation flowchart.This figure shows the basic principle of social moral level by quantitative analysis. As a starting point, we divide social agents into moral and immoral actors, and given their initial energy value. They move to gain or lose energy in the social environment. Public resources, agent energy value accord to set rules for interaction. Finally, we can get the level of social morality.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3842262&req=5

pone-0079852-g001: Moral computing simulation flowchart.This figure shows the basic principle of social moral level by quantitative analysis. As a starting point, we divide social agents into moral and immoral actors, and given their initial energy value. They move to gain or lose energy in the social environment. Public resources, agent energy value accord to set rules for interaction. Finally, we can get the level of social morality.
Mentions: Patches are a static background which form a grid, don't move and have integer coordinates; turtles move on top of the patches, not necessarily in their center and have decimal coordinates and orientation and have different forms in different models, such as person, cattle, triangle, etc.; observer can create new turtles, and can have read/write access to all the agents and variables. Modelers can set the properties or rules of agents through NetLogo commands, in order to achieve a variety of simulation or emulation. Netlogo is a free software; and one can download the latest version software from the web site of NetLogo homepage [28]. The NetLogo interface of the simulation model is shown in Figure 1.

Bottom Line: Based on the NetLogo platform, this article examines the effect of various factors on social morality levels, using agents modeling moral behavior, immoral behavior, and a range of environmental social resources.The results show that: (1) Population size may accelerate or impede the speed with which immoral behavior comes to determine the overall level of social morality, but it has no effect on the level of social morality itself; (2) The impact of rewards and punishment on social morality levels follows the "5∶1 rewards-to-punishment rule," which is to say that 5 units of rewards have the same effect as 1 unit of punishment; (3) The abundance of public resources is inversely related to the level of social morality; (4) When the cost of population mobility reaches 10% of the total energy level, immoral behavior begins to be suppressed (i.e. the 1/10 moral cost rule).The research approach and methods presented in this paper successfully address the difficulties involved in measuring social morality levels, and promise extensive application potentials.

View Article: PubMed Central - PubMed

Affiliation: School of Education in Ideology and Politics, China University of Geosciences (Beijing), Beijing, China.

ABSTRACT
In the social sciences, computer-based modeling has become an increasingly important tool receiving widespread attention. However, the derivation of the quantitative relationships linking individual moral behavior and social morality levels, so as to provide a useful basis for social policy-making, remains a challenge in the scholarly literature today. A quantitative measurement of morality from the perspective of complexity science constitutes an innovative attempt. Based on the NetLogo platform, this article examines the effect of various factors on social morality levels, using agents modeling moral behavior, immoral behavior, and a range of environmental social resources. Threshold values for the various parameters are obtained through sensitivity analysis; and practical solutions are proposed for reversing declines in social morality levels. The results show that: (1) Population size may accelerate or impede the speed with which immoral behavior comes to determine the overall level of social morality, but it has no effect on the level of social morality itself; (2) The impact of rewards and punishment on social morality levels follows the "5∶1 rewards-to-punishment rule," which is to say that 5 units of rewards have the same effect as 1 unit of punishment; (3) The abundance of public resources is inversely related to the level of social morality; (4) When the cost of population mobility reaches 10% of the total energy level, immoral behavior begins to be suppressed (i.e. the 1/10 moral cost rule). The research approach and methods presented in this paper successfully address the difficulties involved in measuring social morality levels, and promise extensive application potentials.

Show MeSH