Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models
Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models
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DOI：
10.1287/mnsc.1070.0787
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发表时间：
2008-05-01
影响因子：
5.4
通讯作者：
Sterman, John
中科院分区：
文献类型：
--
作者：
Rahmandad, Hazhir;Sterman, John
什么时候使用基于主体的（AB）模型更好，什么时候应该使用微分方程（DE）模型？DE模型假设了隔间内的同质性和完美混合，而AB模型可以捕获个体之间以及个体之间相互作用网络中的异质性。AB模型放宽了聚合假设，但需要计算和认知成本，可能会限制敏感性分析和模型范围。由于资源有限，这种分类的成本和效益应成为选择政策分析模式的指导。以传染病为例，我们对比了随机AB模型与类似的确定性房室DE模型的动力学。我们研究了个体异质性和不同网络拓扑结构的影响，包括完全连接，随机，Watts-Strogatz小世界，无标度和格型网络。显然，确定性模型为每个参数集产生单一轨迹，而随机模型产生结果分布。更有趣的是，DE和平均AB动态不同的几个指标相关的公共卫生，包括扩散速度，卫生服务基础设施的峰值负荷，总疾病负担。模型对策略的响应也可能不同，即使它们的基本情况行为相似。然而，在某些条件下，这些差异的平均值是小的相比，由随机事件，参数不确定性，和模型边界引起的变异性。我们讨论了模型类型之间的选择的影响，侧重于政策设计。研究结果不仅适用于流行病学：从创新采纳到金融恐慌，许多重要的社会现象都涉及类似的扩散和社会传染过程。
When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes. More interestingly, the DE and mean AB dynamics differ for several metrics relevant to public health, including diffusion speed, peak load on health services infrastructure, and total disease burden. The response of the models to policies can also differ even when their base case behavior is similar. In some conditions, however, these differences in means are small compared to variability caused by stochastic events, parameter uncertainty, and model boundary. We discuss implications for the choice among model types, focusing on policy design. The results apply beyond epidemiology: from innovation adoption to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion.