基于多目标遗传规划的逆向生成社会科学研究。

Tuong Manh Vu, Charlotte Probst, Joshua M Epstein, Alan Brennan, Mark Strong, Robin C Purshouse
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引用次数: 26

摘要

基于生成机制的社会系统模型,例如那些由基于代理的模拟所代表的模型,需要指定代理内部方程(或规则)。然而,通常有许多不同的选择可用于指定这些方程,这些方程仍然可以被解释为属于特定类型的机制。虽然生成模型重现历史上观察到的动态很重要,但模型在理论上具有启发性也很重要。遗传程序(包括我们自己的)经常产生高度可预测但复杂且难以从理论上解释的串联。在此,我们开发了一种基于多目标遗传规划的新方法,用于同时自动探索两个目标。我们通过发展现有的基于社会规范理论的基于主体的酒精使用行为模拟方程来证明该方法,该模型的初始结构是由一组人类建模者开发的。我们发现在经验拟合和理论可解释性之间存在一种权衡关系,从而深入了解影响酒精使用行为随时间变化和停滞的社会规范过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Toward inverse generative social science using multi-objective genetic programming.

Toward inverse generative social science using multi-objective genetic programming.

Toward inverse generative social science using multi-objective genetic programming.

Toward inverse generative social science using multi-objective genetic programming.

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.

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