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A Network Analytic Approach to Investigating a Land-Use Change Agent-Based Model

机译：一种调查土地利用改变代理的模型的网络分析方法

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Precise analysis of agent-based model (ABM) outputs can be a challenging and even onerous endeavor. Multiple runs or Monte Carlo sampling of one's model (for the purposes of calibration, sensitivity, or parameter-outcome analysis) often yields a large set of trajectories or state transitions which may, under certain measurements, characterize the model's behavior. These temporal state transitions can be represented as a directed graph (or network) which is then amenable to network analytic and graph theoretic measurements. Building on strategies of aggregating model outputs from multiple runs into graphs, we devise a temporally constrained graph aggregating state changes from runs and examine its properties in order to characterize the behavior of a land-use change ABM, the RHEA model. Features of these graphs are transformed into measures of complexity which in turn vary with different parameter or experimental conditions. This approach provides insights into the model behavior beyond traditional statistical analysis. We find that increasing the complexity in our experimental conditions can ironically decrease the complexity in the model behavior.
机译：基于代理的模型（ABM）产出的精确分析可能是一个具有挑战性，甚至繁重的努力。一个模型的多个运行或蒙特卡罗采样（用于校准，灵敏度或参数结果分析）通常会产生大量的轨迹或状态转换，这可能在某些测量下表征模型的行为。这些时间状态转换可以表示为定向图（或网络），然后是网络分析和图形理论测量。从多次运行到图形中，构建汇总模型输出的策略，我们设计了一个时间约束的图形聚合状态从运行的变化，并检查其属性以表征土地使用变化ABM的行为，RHEA模型。这些图的特征转变为复杂度的测量，这反过来随着不同的参数或实验条件而变化。这种方法提供了超越传统统计分析的模型行为的见解。我们发现，在实验条件下增加复杂性可以讽刺地降低模型行为中的复杂性。

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