Ten Broeke Guus A, van Voorn George A K, Ligtenberg Arend, Molenaar Jaap
Biometris, Wageningen University & Research, Wageningen, Netherlands.
Laboratory for Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen, Netherlands.
PLoS One. 2017 Feb 14;12(2):e0171833. doi: 10.1371/journal.pone.0171833. eCollection 2017.
Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs) provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover's distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system.
通过学习或进化实现智能体的适应性是复杂适应系统（CAS）恢复力的一个重要组成部分。没有适应性，此类系统应对外部压力的灵活性将会低得多。为了研究CAS的适应能力，基于智能体模型（ABM）的社会模拟提供了一个有用的工具。然而，ABM对于研究适应性的价值取决于敏感性分析方法的可用性，这种方法能够量化ABM中的恢复力和适应性。在本文中，我们提出了一种敏感性分析方法，该方法基于比较有智能体适应性和无智能体适应性的ABM输出的时间相关概率密度函数。概率密度函数之间的差异通过所谓的推土机距离来量化。我们使用这种敏感性分析方法来量化关键转变发生的概率以及智能体适应性的其他长期影响。为了测试这种新方法的潜力，它被用于分析一个争夺公共资源的适应性智能体ABM的恢复力。结果表明，适应性对该ABM的恢复力有积极贡献。如果适应性进展足够快，它可能会延迟或避免该系统的崩溃。
