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Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

机译：进化计算和基于主体的建模：受生物学启发的理解复杂社会系统的方法

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Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results suggest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.
机译：总体而言，计算社会科学，尤其是基于社会代理的建模（ABM）模拟，面临着通过建模和分析复杂的适应性社会系统而面临的挑战，这些系统具有难以理解的新兴属性，即使组成组件的组织是难以理解的知道。进化计算（EC）是一个成熟的领域，它提供了一种受生物启发的方法和一套适用于复杂适应性社会系统并提供新见解的技术。本文演示了通过简单但完整的政体系统的RebeLand模型说明的组合EC-ABM方法。结果强调了税率和公共发行的频率，它们强调社会是稳定和不稳定治理制度之间相变的重要特征。这些初步结果表明，在具有异构代理的多人口模型，多目标优化，动态环境以及不断发展的可执行对象（用于建模社会变革）方面，EC在ABM方面的进一步应用。

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