Hewitt Richard J, Alcaraz-Castaño Manuel, Hernandez Vito C, Morley Mike W
Instituto de Economía, Geografía y Demografía, Spanish National Research Council (IEGD-CSIC), C/de Albasanz 26-8, Madrid, 28037 Spain.
Área de Prehistoria (Departamento de Historia y Filosofía), Universidad de Alcalá, Colegios 2, Alcalá de Henares, 28801 Spain.
J Archaeol Method Theory. 2025;32(4):57. doi: 10.1007/s10816-025-09726-4. Epub 2025 Aug 2.
Understanding mobility of past hunter-gatherer populations requires dynamic approaches which incorporate uncertainty. Least cost models assume complete knowledge of the terrain on the part of the traveller, while ethnographic examples tend to be specific to the groups and territories studied. Most least cost models also assume that origin points, destination points, or both, are known in advance, limiting their utility for exploring movement potential in landscapes where evidence for occupation is scarce. This research addresses these limitations through an agent-based model of movement grounded in cellular automata (CA) theory, called DISPERSCA. Agents depart from a point, which may be specified or determined at random, and transit a fitness landscape for a fixed number of iterations according to decisions made within a defined area at each time step (), the CA neighbourhood. If the decision catchment is unknown multiple runs are made at different CA neighbourhood sizes and the results are compared. Neighbourhoods may be square or hexagonal, the former producing on average longer displacements, the latter ensuring that individual walks are of equal length in any direction. The model is demonstrated by application to Late Pleistocene Central Iberia, where confirmed archaeological sites are scarce. Some support can be advanced for the hypothesis that the Central Iberian mountains, probably combined with the Iberian System range, presented a significant barrier to hunter-gatherer groups. The model can be modified to account for agents' prior knowledge, or to include fitness variables unrelated to terrain cost, such as water, the presence of game animals or vegetation.
了解过去狩猎采集人群的迁徙情况需要采用包含不确定性的动态方法。成本最小化模型假定旅行者对地形有全面的了解，而人种志实例往往特定于所研究的群体和地域。大多数成本最小化模型还假定起点、终点或两者均事先已知，这限制了它们在 occupation 证据稀少的景观中探索移动潜力的效用。本研究通过一种基于元胞自动机（CA）理论的基于主体的移动模型 DISPERSCA 来解决这些局限性。主体从一个点出发，该点可以指定或随机确定，并根据在每个时间步（CA 邻域）的定义区域内做出的决策，在适应度景观中进行固定次数的迭代。如果决策集未知，则在不同的 CA 邻域大小下进行多次运行并比较结果。邻域可以是正方形或六边形，前者平均产生更长的位移，后者确保个体在任何方向上的行走长度相等。通过将该模型应用于晚更新世的伊比利亚中部地区（那里已确认的考古遗址稀少）进行了演示。对于伊比利亚中部山脉可能与伊比利亚山脉系统相结合对狩猎采集群体构成重大障碍这一假设，可以提供一些支持。该模型可以修改以考虑主体的先验知识，或纳入与地形成本无关的适应度变量，如水、猎物或植被的存在。
