Abstract Modeling terrorism is both necessary and difficult. While the necessity comes from the all too obvious real-world pressures our society is facing, the difficulty stems from the underlying complexity of the phenomena itself – there are many variables to account for, they are hard to measure, and the relationships between them are confounding. Since modeling terrorism is at its most onerous when it comes to predicting specific attacks, their timing and scale, we opted to work around this using observed probabilistic distribution and integrate power laws into our system dynamics model. After evaluating thousands of simulations runs, this allows us to replicate historical data as well as produce prognostic scenarios, while maintaining what we believe to be authentic behavior. Compromises need to be made, but we believe that this approach can be useful for systems highly dependent on events or parameters which we are unable to predict but whose distributions are known.

中文翻译：

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非线性恐怖主义动态的随机建模

摘要 恐怖主义建模既必要又困难。虽然必要性来自我们社会所面临的非常明显的现实压力，但困难源于现象本身的潜在复杂性——有许多变量需要解释，它们很难衡量，它们之间的关系是令人困惑。由于在预测特定攻击、它们的时间和规模方面为恐怖主义建模是最繁重的，我们选择使用观察到的概率分布来解决这个问题，并将幂律整合到我们的系统动力学模型中。在评估了数千次模拟运行后，这使我们能够复制历史数据并产生预测场景，同时保持我们认为真实的行为。需要做出妥协，