Wednesday, November 9, 2011

CAA 2012 [Call for Roundtable]: Models and Simulations in Archaeology: where we are and where we are headed?

Following the previous post, I just wanted to let you know that we (Mark Lake, Bernardo Rondelli, Xavier Rubio and myself) are also proposing a roundtable (details below). This year's (again, academic year) CAA will have two sessions on Simulation ("Archaeological Simulation Modelling as Computational Social Science: Next Steps Forward" and "Artificial Societies in Prehistory and Ancient Times") so it's a good opportunity to discuss "hands-on", having solid basis to start from.... In the last couple of years the number of publications using ABM, and computer simulations in general is growing, so we really need to sit back and think carefully about it...

Here's the details of our proposal:

Models and Simulations in Archaeology: where we are and where we are headed?

Computer simulation is a well-established technique which has long provided substantive insights in the physical sciences and increasingly does so in the life sciences. In addition, the field of social simulation has developed rapidly since the mid 1990s. The archaeological application of computer models dates back to the early 1970s, but despite forty years of activity, the impact of simulation has, with a few exceptions, been relatively slight, and it is largely still viewed as a fringe activity (e.g. Lake 2010). However, the first decade of the new ,millennium has witnessed a resurgence of interest, made manifest in a growing number of publications which provide the potential to change this perspective. In large part this is due to the way that agent-based modelling has captured archaeologists’ imagination, especially given the increased availability of simple software environments (e.g. NetLogo) that do not require advanced knowledge in programming and can easily be run on desktop computers.

Since this increasing “democratisation” of simulation seems likely to lead to more widespread application of the technique to substantive archaeological problems this seems a good moment to take stock and consider whether the appropriate epistemic foundations are in place to support the growth of productive archaeological simulation. It is vitally important to recognise that the availability and accessibility of user-friendly software environments does not solve per se issues arising from the complexity of the model building process and, in particular, the validation and portability of the experiment’s results. Consequently, an epistemological reflection and methodological overview of the use of modelling and simulation in archaeology is very timely. We think that a discussion between specialist and non-specialist, experienced and non-experienced users can stimulate a reflection on where we are, and more importantly, where we are headed in the application of computer simulation to archaeological research questions.


Over tthe last decade the archaeological application of computer simulation has taken two distinct directions. On the one hand, a number of models have been developed to test specific hypotheses. This approach quantitatively or semi-quantitatively compares a existing archaeological data with artificial data produced by the simulations. The other direction is the development of abstract models derived from assumptions developed within our discipline or elsewhere (behavioural ecology, evolutionary anthropology, biology etc.), which have been used in to generate new
theories, or to explore the implications of previously formulated ones in a dynamic and computational environment.

The two directions are not mutually exclusive, but each leads to a series of important question: How can we validate abstract models? How can we translate archaeological and anthropological theories in terms computational and/or mathematical algorithms? How do the limits of computational representation affect our model building exercise? How does the scientific audience evaluate extremely complex and realistic models? How do we communicate our models, especially to non- specialists?
By discussing these concrete questions we hope that the roundtable will approach deeper questions, such as: will computer simulation have an impact on our discipline or it will remain a fringe activity? If the former, then will increased use of simulation change mainstream archaeology or it will lead to the emergence of a “new” sub-discipline?

We are particularly interested in the following topics:

  • Abstract vs. Realistic Models: An adversative or complemental epistemology? 
  • Mathematical Models vs. Agent Based Models: two faces of the same coin or alternative pathways?
  • Validation and Verification of Computer Models. 
  • Communicating Models: looking for a common protocol or a language? 
  • Potential pitfalls and common problems of modelling in archaeology.
  • Why computer models are still an outsider?

Tuesday, November 8, 2011

CAA 2012 [Call for Papers]: Embracing Uncertainty in Archaeology

So, it's time for submitting your paper at CAA2012!!! This year (academic year I mean) the University of Southampton will host the event which will back in Europe after being hosted in China. I'm quite excited about this, since the last time I went it was Budapest 2008... And this time we (Andrew BevanEugenio BortoliniEleonora Gandolfi, Mark Lake and myself) have also proposed the following session:

Embracing Uncertainty in Archaeology
(Session Code: Theory1)

This session aims to develop greater awareness of approaches to uncertainty in archaeology by bringing together both established experts and young researchers from a range of different fields. Its ultimate goal is to generate broader discussion about how we confront uncertainty in the recovery of archaeological datasets, how we treat it analytically and computationally, and how we incorporate it into our inference building, interpretations and narratives.

Uncertainty is at the core of long-standing debate in a wide variety of modern scientific domains, as testified by the recent inclusion of the topic among the twelve most relevant scientific endeavours listed by the Royal Society . Critical debates concerning the assessment, representation and public understanding of uncertainty are also of widespread interest in the social and political sciences.

The increasing availability of tools capable to solve large computational problems has provided a suitable environment for tackling this issue. Examples of such approaches can be widely found in different realms of our discipline. These include the use of advanced techniques in chronometry (Buck et al 1996), predictive modelling (Ducke et al 2009), spatial analysis (Crema et al 2010) remote sensing (Menze and Ur 2011), phylogenetic analysis (Nicholls and Gray 2006), typology and classification (Hermon and Nicolucci 2002), stratigraphy (De Runz et al 2007) and data visualisation (Zuk et al. 2005).
Despite some explicit, epistemologically-oriented contributions (Wylie, 2008; Lake, 2011) a debate encompassing both practical and theoretical aspects has never emerged nor it has determined direction and priorities of mainstream archaeology.


Isolated discussions within single subfields (e.g. radiocarbon dating or spatial modelling) can certainly provide grounds for theoretical and methodological advancement, but we need to develop more integrated approaches to uncertainty across all the aspects of the discipline.
We invite original contributions to the following themes: a) the role of uncertainty in archaeological narratives; b) methodological debates about different probabilistic approaches; c) measurement and integration of uncertainty into archaeological analysis; d) appropriate sampling strategies and missing data problems; e) cultural resource management, risk assessment and decision making; f) public understanding and data visualisation.


Buck, C.E., Cavanagh, W.G. and Litton, C.D., 1996. Bayesian approach to interpreting archaeological data. Chichester:Wiley.

Crema, E. R., Bevan, A. and Lake, M., 2010, A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record, Journal of Archaeological Science, 37, 1118-1130.

De Runz, D., Desjardin, E., Piantoni, F., and Herbin, M. 2007. Using fuzzy logic to manage uncertain multi-modal data in an archaeological GIS. Proceedings of ISSDQ 2007, Enschede, Netherland.

Drennan, R.D. and Peterson, C.E. 2004. Comparing archaeological settlement systems with rank-size graphs: a measure of shape and statistical confidence, Journal of Archaeological Science 31:533-549.

Hermon, S., and Nicolucci, F. 2002. Estimating subjectivity of typologists and typological classification with fuzzy logic. Archeologia e Calcolatori, 12, 217-232.

Lake, M W, 2010. The Uncertain Future of Simulating the Past. In A Costopoulos and M W Lake (eds) Simulating Change: Archaeology Into the Twenty-First Century, Salt Lake City: University of Utah Press. 12-20.

Nicholls, G,K, and Gray, R.D. 2006. Quantifying Uncertainty in a Stochastic Model of Vocabulary Evolution. In: Forster P., and Renfrew, C. (eds.) Phylogenetic methods and the prehistory of languages. Cambridge : McDonald Institute for Archaeological Research. 161-172.

Wylie, A. 2008. Agnotology in/of Archaeology,” in R. N. Proctor and L. Schiebinger (eds.) Agnotology: The Making and Unmaking of Ignorance, edited by; Stanford University Press:183-205.

Zuk, T. Carpendale, S. and Glanzman, W.D., 2005. Visualizing Temporal Uncertainty in 3D Virtual Reconstructions, In Proceedings of the 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST 2005), 99-106.

How criminology and computational statistics can help archaeology...

I've been working for a while (Crema et al 2010, Crema In press) on the issue of temporal uncertainty in archaeological analysis. The reason for such interest emerged while I was trying to do the simplest spatial analysis of the distribution of pithouse dwellings. While making my database I found something similar to the following:

ID,  Name, Date
001,Pithouse A, Kasori E1 phase
002,Pithouse B, Middle Jomon
003,Pithouse C, transition between Kasori E2 and E3 phase.

Now... in case you're not an expert of pottery phases of Jomon period (you should, they look good), the Middle Jomon period lasted ca 1,000 years (ca. 5470-4420 cal BP) and the Kasori E1 phase ca 60 years (4900-4840 cal BP), with the latter being a sub-phase of the former.

You can easily imagine my problem here. In order to do any diachronic analysis I have two options. I could either lump a large portion of my data choosing the coarsest resolution I have (Middle Jomon in this case, and hence virtually dismissing the knowledge I have about pithouse A) and then carry on my analysis or I can use only those satisfying the temporal resolution I am interested in, and ignore the rest of the data (thus removing pithouse B in this case and using only A and B).
Both solution is highly unsatisfactory, and frankly the second one involves even omitting part of the available knowledge....

While looking around, I found some papers by Jerry Ratcliffe, a american criminologists who happened to have very similar problems...Imagine you've left your car at 9 AM in the morning, you've worked all day and when you've finished  at 5 PM you found your car to be stolen. You know that the crime happened sometimes between 9AM and 5PM. Now if you are criminologist and you are trying analyse the data of other thefts, you will quickly notice that the great majority of the temporal data involves intervals within which the crime have occurred rather than the precise time of the event. Ratcliffe calls these intervals time-spans and noticed that you might have shorter ones when you have more information (a friend came late to job and noticed the car was already missing at 11 AM) and longer ones where you have less information. The problem of spatio-temporal analysis of crime data is that you have consistently different time-spans in your dataset.... Exactly the same problem WE have...

The solution proposed by Ratcliffe is called aoristic analysis (Ratcliffe 1998) and essentially involves what is called "principle of insufficient reason" and basically assumes that, with other things being equal, if we divide our time-span in equally long time-blocks (e.g. decades or hours) the chance that the event have occurred in any of these will be homogeneously distributed within the time-span. In other words, if you don't have any information, the chance that your car might have been stolen between 9 and 10 AM is equal to the chance that the crime occurred between 3 and 4 PM. Based on this very simple premise, we can provide probabilistic measures to our "events".

Now the problem I was facing is that, probability weights cannot be used for standard analysis. You can enhance perhaps visualisation of the data, and maybe provide broad cumulative sum of the probability as time-series. But If you want to do something more sophisticated you need a non-probabilistic data, since the majority of available tools are not designed to deal with temporal uncertainty.

Then I cam across to Monte-Carlo simulation, The idea itself is very simple. Based on a probability distribution (in this case given by the aoristic analysis) one could simulate all the possible combinations of events, and hence all the possible spatio-temporal patterns that might have occurred. The number will be immensely huge, but if a sufficient degree of knowledge is available, some pattern will occur more frequently than others. Hence by simulating n scenarios, one could compute the proportions of these where a given pattern is observed. This will then provide a likelihood estimate of such pattern.

Adopting Monte-carlo simulation opens an entire array of possibilities. One could in fact use different sources of knowledge, from radiocarbon dates to stratigraphic relations and explore the range of possible spatio-temporal patterns. One should then simply assess each of the possible scenarios and compare the distribution of the outcomes to infer about the past in probabilistic terms...


Crema, E. R., Bevan, A. and Lake, M., 2010, A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record, Journal of Archaeological Science,  37, 1118-1130.

Crema, E. R., In press. Aoristic Approaches and Voxel Models for Spatial Analysis. In: Jerem, E., Redő, F. and Szeverényi, V. (ed.) On the Road to Reconstructing the Past. Proceedings of the 36th Annual Conference on Computer Applications and Quantitative Methods in Archaeology.  Budapest: Archeolingua.

Crema, E. R., In press, Modelling Temporal Uncertainty in Archaeological Analysis, Journal of Archaeological Method and Theory (online first). 

Johnson, I., 2004. Aoristic Analysis: seeds of a new approach to mapping archaeological distributions through time. In: Ausserer, K. F., ̈rner, W. B., Goriany, M. and ckl, L. K.-V. (ed.) [Enter the Past] the E-way into the Four Dimensions of Cultural Heritage: CAA2003. BAR International Series 1227.  Oxford: Archaeopress, 448–452.

Ratcliffe, J. H. and McCullagh, M. J., 1998, Aoristic crime analysis, Inernational Journal of Geographical Information Science,  12, 751-764.

Ratcliffe, J. H., 2000, Aoristic analysis: the spatial interpretation of unspecifed temporal events, Inernational Journal of Geographical Information Science,  14, 669-679.