In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models generate observable time series driven by evolving, partially-latent microstates. Latent states must be estimated to align simulations with real-world data, a task traditionally addressed by DA, particularly in continuous and equation-based models used in weather forecasting. However, the nature of ABMs poses challenges for standard DA methods. Solving such issues requires adapting previous DA techniques or using ad hoc alternatives such as LBI. DA approximates the likelihood in a model-agnostic way, making it broadly applicable but potentially less precise. In contrast, LBI provides more accurate state estimation by directly leveraging the model's likelihood, but at the cost of requiring a hand-crafted, model-specific likelihood function, which may be complex or infeasible to derive. We compare the two methods on the Bounded-Confidence Model, a well-known opinion dynamics ABM, where agents are affected only by others holding sufficiently similar opinions. We find that LBI better recovers latent agent-level opinions, even under model mis-specification, leading to improved individual-level forecasts. At the aggregate level, however, both methods perform comparably, and DA remains competitive across levels of aggregation under certain parameter settings. Our findings suggest that DA is well-suited for aggregate predictions, while LBI is preferable for agent-level inference.

翻译：本文首次系统对比了数据同化（Data Assimilation, DA）与基于似然的推断（Likelihood-Based Inference, LBI）在智能体模型（Agent-Based Model, ABM）中的应用。此类模型由演化中的部分可观测微观隐状态驱动，生成可观测的时间序列。为使模型模拟与现实数据对齐，需估计隐状态，这一任务传统上由DA处理，尤其在天气预报中使用的连续型及基于方程式的模型中。然而，ABM的特性对标准DA方法构成挑战。解决此类问题需调整现有DA技术或采用LBI等专用替代方案。DA以模型无关的方式近似似然，虽适用范围广但精度可能较低；而LBI通过直接利用模型似然提供更精准的状态估计，但需手动构建模型专用的似然函数，这一过程可能复杂甚至不可行。我们在有界置信模型（Bounded-Confidence Model，一种经典的舆论动力学ABM，其中智能体仅受观点足够相近的其他智能体影响）上对比两种方法。研究发现：即使在模型设定错误的情况下，LBI也能更准确地恢复隐层智能体观点，从而提升个体级预测精度；但在聚合层面，两种方法表现相当，且DA在特定参数设置下仍保持跨聚合层次的竞争力。我们的结果表明：DA适用于聚合预测，而LBI更适用于智能体级推断。