The past has always offered new and interesting insights that could be simulated, modelled and evaluated with computational approaches. In recent years the applications of advanced geospatial statistics, as well as modelling have become a central methodological framework to analyse past human behaviour and societies in general. However, often archaeological applications falls short on the capacities of these methods or massively overestimate their potential. On the one hand it is clearly related to the pursuit of model and test assumptions. On the other hand causal expectations are strongly simplified and in general more basic statistics are used. Predominantly, this leads to rather simple, purely environmentally constraint versions of reality, neglecting the presence of more than a topographical landscape with certain resources. Other factors, such as a “landscape of ancestors”, differing perception of space, or unknown human factors are mostly ignored in the models. The social sciences have constantly stressed the complexity of human decision making and have successfully implemented complex statistical procedures, such as sophisticated self-learning algorithms in order to achieve a better representation of reality. However, societies modelled in archaeology are often devoid of this cognitive human factor, which cannot be represented in the predominantly deterministic, almost Darwinian models. Furthermore – if at all – theoretical frameworks which were long since updated in social sciences are used to retrospectively interpret the model’s outcome. In this session we wish to address and discuss this problem in current archaeological human behavioural research with an interdisciplinary approach of archaeology and sociology. We welcome theoretical as well as practical contributions on the inclusion of social theory in geospatial analyses and predictive modelling, new ideas for a theoretical framework, and how archaeology can deal with the fuzziness of human decision making, which is never purely environmentally driven.
