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.