Qualitative network models (QNMs) have become a popular tool to assess how ecosystems respond to a perturbation within ecosystem-based fisheries management strategies. Yet, the incorporation of humans into these models is often rudimentary, potentially limiting the accuracy and reliability of the model results. We developed QNMs focusing solely on the social components, derived from content analysis of the literature on the effects of the US Pacific halibut Individual Fishing Quota (IFQ) Program and evaluated how the QNMs performed with respect to simulating the programmatic effects on individual well-being components. The QNMs were effective at reproducing IFQ programmatic effects and demonstrating how well-being heterogeneity across user groups can be incorporated into network models. However, key mechanistic variables were omitted to maintain model stability, reducing our ability to fully replicate the IFQ system. We conclude that QNMs require improvement to incorporate human dimensions that reflect broader social realities. Yet, given the current structural limitations of these modelling frameworks coupled with the complexity of human decision making, there are likely to be continued issues with integrating humans accurately and representatively into these models.

中文翻译：

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检查定性网络模型向整合多方面人体维度的扩展

定性网络模型 (QNM) 已成为评估生态系统如何应对基于生态系统的渔业管理战略中的扰动的流行工具。然而，将人类纳入这些模型通常是初级的，可能会限制模型结果的准确性和可靠性。我们开发了仅关注社会成分的 QNM，源自对美国太平洋大比目鱼个体捕捞配额 (IFQ) 计划影响的文献内容分析，并评估了 QNM 在模拟计划对个人福祉的影响方面的表现成分。QNM 有效地再现了 IFQ 程序效应，并展示了如何将跨用户组的幸福异质性纳入网络模型。然而，省略了关键机械变量以保持模型稳定性，从而降低了我们完全复制 IFQ 系统的能力。我们得出结论，QNM 需要改进以纳入反映更广泛社会现实的人类维度。然而，鉴于这些建模框架当前的结构限制以及人类决策的复杂性，将人类准确和有代表性地集成到这些模型中可能会继续存在问题。