Causality and Cross-Purposes in Archaeological Predictive Modeling (original) (raw)
Related papers
H. Kamermans, M. van Leusen and P. Verhagen (eds.), Archaeological prediction and risk assessment. Alternatives to current practice. Leiden University Press, Leiden (ASLU 17), pp. 19-25., 2009
In general, academic archaeologists have always been sceptical of, and sometimes even averse to, predictive modelling as practiced in archaeological heritage management (AHM) (see Van Leusen et al. 2005). The models produced and used in AHM are not considered sophisticated enough, and many of the methodological and theoretical problems associated with predictive modelling have not been taken aboard in AHM. At the same time, the production and use of predictive models has become a standard procedure in Dutch AHM (Deeben et al. 1997; 2002; Deeben 2008), and it has clearly attracted interest in other countries as well. The main reason for using predictive models in AHM is efficiency. In ‘post-Malta’ archaeology, the financial, human and technical resources allocated to archaeology have increased enormously. But at the same time, these resources have to be spent both effectively and efficiently. So why not create and use tools that will allow us to do so? Archaeological predictive models will tell us where we have the best chances of encountering archaeology. Searching for archaeology in the high probability areas will ‘pay off’, as more archaeology will be found there than in low probability zones. It is a matter of priorities: we can’t survey everything, and we don’t want to spend money and energy on finding nothing. And there is also the political dimension: the general public wants something in return for the taxpayers’ money invested in archaeology. It’s not much use telling politicians to spend money on research that will not deliver an ‘archaeological return’. But how can we be so sure that the low probability zones are really not interesting? And where do we draw the line between interesting and not interesting? These are hard choices indeed for those involved in AHM. Archaeologists who don’t have to make these choices have an easy job: they can criticize the current approach to predictive modelling from the sidelines, and don’t have to come up with an alternative. Within the BBO program we have been trying to provide such an alternative to the archaeological community (Kamermans et al. 2005). However, at the end of the project, we have to conclude that we have only been partly successful. We have done a fair amount of research, published three books and many papers, made the problems with predictive modelling internationally visible but failed to change the procedures of predictive modelling in the Netherlands. In this paper we venture to offer some explanations for the lack of success of new approaches to predictive modelling in AHM in the Netherlands up to now. And finally, we will try to sketch the future of archaeological predictive modelling, for which we can see three distinct scenarios.
5. New developments in archaeological predictive modelling
Amsterdam University Press eBooks, 2011
In this paper the authors present an overview of their research on improving predictive modelling into true risk assessment tools. Predictive modelling as it is used in archaeological heritage management today is often considered to be a rather crude way of predicting the distribution of archaeological remains. This is partly because of its lack of consideration of archaeological theory but also because of a neglect of the effect of the quality of archaeological data sets on the models. Furthermore, it seems that more appropriate statistical methods are available for predictive modelling than are currently used. There is also the issue of quality control, a large number of predictive maps have been made but how do we know how good they are? The authors have experimented with two novel techniques that can include measures of uncertainty in the models and thus specify model quality in a more sophisticated way, namely Bayesian statistics and Dempster-Shafer modelling. The results of the experiments show that there is room for considerable improvement of current modelling practice but that this will come at a price because more investment is needed for model building and data analysis than is currently allowed for. It
Journal of Archaeological Method and Theory 19, 49-100., 2012
Archaeological predictive modeling has been used successfully for over 20 years as a decision-making tool in cultural resources management. Its appreciation in academic circles however has been mixed because of its perceived theoretical poverty. In this paper, we discuss the issue of integrating current archaeological theoretical approaches and predictive modeling. We suggest a methodology for doing so based on cognitive archaeology, middle range theory, and paleoeconomic modeling. We also discuss the problems associated with testing predictive models.
Theoretical remarks on predictive models in Landscape Archaeology
La arqueología actual se caracteriza por su pluralismo teórico y metodológico. La idea de reflexionar sobre la práctica arqueológica desde la noción de “modelo predictivo” invita, sin embargo, a profundizar en la racionalidad científica de los esquemas argumentales subyacentes bajo las estrategias de investigación. El objetivo de la contribución es explorar los distintos sentidos en los que esas estrategias usan “modelos predictivos” en el contexto de la Arqueología del Paisaje, considerando distintos enfoques teóricos.
P. Verhagen (2008). Testing archaeological predictive models: a rough guide.
A. Posluschny, K. Lambers and I. Herzog (eds.): Layers of Perception. Proceedings of the 35th International Conference on Computer Applications and Quantitative Methods in Archaeology (CAA), Berlin, Germany, April 2–6, 2007. Dr. Rudolf Habelt GmbH, Bonn, pp. 285-291., 2008
Archaeological predictive modelling is an essential instrument for archaeological heritage management in the Netherlands. It is used to decide where to do archaeological survey in the case of development plans. However, very little attention is paid to testing the predictions made. Model quality is established by means of peer review, rather than by quantitative criteria. In this paper the main issues involved with predictive model testing are discussed. The potential of resampling methods for improving predictive model quality is investigated, and the problems associated with obtaining representative test data sets are highlighted.
Archaeological predictive modelling in cultural resource management
Predictive models are becoming increasingly often used in archaeological cultural resource management. Beside this, extremely successful and productive application, predictive models can be and are used as an effective tool in archaeological site location explanation. The main objective of this presentation is to discuss and present some aspects of practical applications of predictive modelling. The presentation will start with theoretical introduction to predictive modelling and will be followed with some methodological issues. Special emphasis will be paid to the presentation of several case studies. The first set of case studies will focus on the application of multiple overlays of spatial information layers for modelling potential of Bronze Age settlement sites location and barrows. Further more some results of the multivariate statistics for the analysis of Roman settlement patterns will be presented. Finally, we will demonstrate how site locations were predicted in Slovenian highway constructions project. Presentation will be concluded with some general remarks and practical suggestions for future work.