Agent-Based modelling to assess the quality and bias of the archaeological record
Archaeological data is often biased and incomplete. This is a well-known issue for most archaeologists. Although studies of specific sites and small regions can have this into account, the effect of this problem increases exponentially as archaeologists try to expand their chronological and geographic frame, and try to answer questions related to general dynamics and broad human processes, such as the European Neolithisation. Moreover, the increase of archaeological activity in the past years, along with the heterogeneity of archaeological methods and the accumulation of legacy datasets, often collected with specific purposes, adds great difficulty to a detailed analysis of the interrelation of similar processes through different geographic regions. In this regard, synthetic and computational archaeology, because they usually rely on large datasets and focus their research in broad-frame dynamics, are particularly sensitive to this kind of problems.   ArchBiMod will address this issue to improve archaeological inference and interpretation of wide-range dynamics. This project will perform a comprehensive Agent-Based simulation, which will have into account different aspects related to how biases in the archaeological record affect hypothesis building. It will do so in two stages. First, it will assess common issues, such as record formation, record loss or differential research, and then it will add to those common factors specific research topics related the demography of the European Neolithisation. By doing this, ArchBiMod will build a modular open and easy-to-use model which can then be used by other archaeologists to assess their own problems, and enlarged with different research topics through collaboration.    Ultimately, ArchBiMod will bring a significant contribution in two ways: by providing a thorough and comprehensive analysis of archaeological bias factors and by creating an open and useful tool which will improve how archaeologists understand the past.