The reconstruction of past demographic patterns is a fundamental step towards a better understanding of human-environment relations, especially in terms of quantifiable anthropic impact and population susceptibility to environmental changes. The recently developed Summed Calibrated Probability Distributions (SCPD) approach, based on large collections of archaeological radiocarbon dates, provides a new tool to obtain continuous prehistoric population curves suitable for comparison with palaeoenvironmental time series. Despite a wide application in Mesolithic and Neolithic contexts worldwide, the use of the SCPD method remains rare for post-Neolithic societies. Our aim is to address this visible gap and apply the SCPD approach to South European archeological contexts between the Bronze Age and the transition into the Iron Age (1800–800 cal. BC), then evaluating these results against local archeological narratives and palaeoecological data. We first test the SCPD method at a supra regional scale, ranging from the Ebro to the Danube rivers, and subsequently in five selected regions within this area. We then compare the regional population curves to climate data reconstructed from local palynological records. Our results highlight the contrast between a stable supra regional demographic trend and more dynamic regional patterns. We do not observe any convincing long-term correlations between population and climate, but localized episodes of demographic stagnation or decline are present in conjunction with climatic shifts or extremes. Nevertheless, climate change as a triggering factor should be considered with caution, especially in peripheral areas where the archaeological data is faint, or where local evidence points to contemporaneous, ongoing landscape overexploitation.

Archaeologists continue to search for techniques that enable them to analyze archaeological data efficiently with artificial intelligence approaches increasingly employed to create new knowledge from archaeological data. The purpose of this paper is to investigate the application of Pattern Recognition methods in detection of buried archaeological sites of the semi-arid Khorramabad Plain located in west Iran. This environment has provided suitable conditions for human habitation for over 40,000 years. However, environmental changes in the late Pleistocene and Holocene have caused erosion and sedimentation resulting in burial of some archaeological sites making archaeological landscape reconstructions more challenging. In this paper, the environmental variables that have influenced formation of archaeological sites of the Khorramabad Plain are identified through the application of Arc GIS. These variables are utilized to create an accurate predictive model based on the application of One-Class classification Pattern Recognition techniques. These techniques can be built using data from one class only, when the data from other classes are difficult to obtain, and are highly suitable in this context. The experimental results of this paper confirm one-class classifiers, including Auto-encoder Neural Network, k-means, principal component analysis data descriptor, minimum spanning tree data descriptor, k-nearest neighbour and Gaussian distribution as promising applications in creating an effective model for detecting buried archaeological sites. Among the investigated classifiers, minimum spanning tree data descriptor achieved the best performance on the Khorramabad Plain data set. © 2016 Elsevier Ltd

This article describes the design and operation of an agent-based model that represents aspects of hunter-gatherer subsistence, technology, and cultural identity. The agents (representing families or households) in the model interact in a world that has a seasonally variable resource density. Agents must collect resources every step, either independently (by “gathering”) or with cooperation from neighboring agents (“hunting”). Several parameters affect cooperation: understanding these effects is one of the main focus of this article. Another focus is understanding the effects of cooperation in terms of cultural diversity/homogeneity. Some input parameters are historically and ethnographically calibrated, and the results are contrasted, with ongoing archaeological research of Patagonian hunter-gatherers (from 7000 BC or even earlier to 19th century). Specific research questions include how do processes of convergence and divergence occur between groups of hunter-gatherers over the long term? How could the autonomous local interactions of heterogeneous bounded rational agents generate this kind of regularity? What role outside influence plays on the formation of ethnic identities? Our aim is to integrate the state-of-the-art knowledge from different social sciences and technological developments into a fruitful approach to develop sociohistorical studies