AI 中文总结
该研究将欧洲林业从业者的经验行为类型学整合进基于智能体的土地利用模型，发现行为异质性可削弱土地利用的同步响应，提升模型真实性，且个体决策行为依赖所处种群环境。
AI 中文摘要
土地利用模型通常将决策者表征为同质且理性的个体，忽略了社会心理层面的多样性，可能生成与实际土地利用模式不符的快速、协调的土地利用响应。本研究将基于调查的行为特征转化为支配内生决策过程的认知参数，把欧洲林业从业者的经验类型学整合进基于智能体的土地利用模型中。我们区分了五类从业者类型，并根据这些类型的经验观测分布对异质智能体种群进行参数化。借助一个简化的模型景观，我们将这些异质种群与同质理性选择基准种群以及单一类型种群进行对比。与同质理性选择基准相比，异质种群削弱了对生态系统服务需求变化的同步响应，产生了更渐进的土地利用动态，且中等强度管理的占比更高，这更符合经验观测的土地利用模式。实验结果显示，相似的土地利用模式可通过不同的行为机制产生，而个体决策类型的行为强烈依赖于其所处的种群环境。这两项发现共同表明，个体行为响应与土地利用结果是共同演化的，而非决策类型对应固定的管理实践。因此，明确表征社会心理多样性可通过捕捉认知、社会互动与涌现的土地利用动态之间的反馈，提升基于智能体的土地利用模型的真实性和政策相关性。经验数据可支撑这种表征，而当数据缺失时，模型也可利用该能力将行为异质性作为关键不确定性来源进行探索。
英文摘要
Land use models often represent decision-makers as homogeneous and rational, overlooking socio-psychological diversity and potentially generating rapid, coordinated land use responses that contrast with observed land use patterns. Here, we integrate an empirical typology of European forestry practitioners into an agent-based land use model by translating survey-based behavioural profiles into cognitive parameters governing endogenous decision-making processes. We distinguish five practitioner types and parametrise heterogeneous agent populations according to the empirically observed distribution of these types. Using a stylised model landscape, we compare these heterogeneous populations against a homogeneous rational-choice baseline and single-type populations. Compared with the homogeneous rational-choice baseline, heterogeneous populations dampen synchronised responses to changing ecosystem service demand, producing more gradual land use dynamics and a higher prevalence of medium intensity management, that better reflect empirical land use patterns. Our experiments reveal that similar land use patterns can emerge through different behavioural mechanisms, while the behaviour of individual decision-making types depends strongly on the population context in which they are embedded. Together, these two findings show that individual behavioural responses and land use outcomes co-evolve rather than decision-making types mapping onto fixed management practices. Explicitly representing socio-psychological diversity can therefore improve the realism and policy-relevance of agent-based land-use models by capturing feedbacks between cognition, social interactions, and emergent land use dynamics. Empirical data may make this representation possible, but models can also use this capability to explore behavioural heterogeneity as a key source of uncertainty when data are absent.
Comments30 pages, 24 figures. Submitted to SESMO
