Computational archeology

Computational archeology describes computer-based analytical methods for the study of long-term human behavior and behavioral evolution. As with other sub-disciplines that have prefixed ‘computational’ to their name (eg, computational biology , computational physics and computational sociology ), the term is reserved for (all mathematical) methods that could not be realistically performed without the aid of a computer.

Computational archeology May include the use of geographical information systems (GIS), Especially When applied to spatial analyzes Such As viewshed analysis and least-cost path analysis as thesis approaches are Sufficiently computationally complex That They Are extremely difficulty if not possible to Implement without the processing power of a computer. Likewise, some forms of statistical and mathematical modeling , [1] and the computer simulation of human behavior and behavioral change using software tools such as Swarm goldRepast would also be impossible to calculate without computational aid. The University of London’s Space Syntax Program, also falls under the term ‘computational archeology’. The application of a variety of other forms of complex and bespoke software to solve archeological problems, such as human perception and movement within built environments .

Computational archeology is also known as archaeological informatics (Burenhult 2002, Huggett and Ross 2004) or archaeoinformatics (sometimes abbreviated as “AI”, but not to be confused with artificial intelligence ).

Origins and objectives

In recent years, It has Become clear That archaeologists will only be reliable to harvest the full potential of quantitative methods and computer technology If They Become aware of the specific potentials and pitfalls inherent in the archaeological data and research process. A science is an emerging discipline that attempts to uncover, quantitatively represents and explores specific properties and patterns of archaeological information. Fundamental researches on data and methods for a self-sufficient archaeological approach to information processing and quantitative methods and computer software specifically geared to archaeological problem solving and understanding.

AI is capable of complementing and enhancing the area of scientific archaeological research. It incorporates a large part of the methods and theories developed in quantitative archeology since the 1960s but goes beyond forming forms of quantification archeology by exploring ways to represent general archaeological information and problem structures as computer algorithms and data structures. This opens up a wide range of computer-based information processing methods to solve problems of great complexity. It also promotes a formalized understanding of the discipline’s research objects and creates links between archeology and other quantitative disciplines, both in methods and software technology. Its agenda can be split into two major research themes that complement each other:

  1. Fundamental research on the structure, properties and possibilities of archaeological data, inference and knowledge building . This includes modeling and managing fuzziness and uncertainty in archaeological data, scale effects, optimal sampling strategies, and spatio-temporal effects.
  2. The development of computer algorithms and software (applied by AI) that makes this knowledge available to the user.

There is already a large body of literature on the use of quantitative methods and computer-based analysis in archeology. The development of methods and applications is best reflected in the annual publications of the CAA conference (see external links section at bottom). At least two journals, the Italian Archeology and Calcolatori and the British Archaeological Computing Newsletter , are dedicated to archaeological computing methods. AI Science contributes to many fundamental research topics, including but not limited to:

  • advanced statistics in archeology, spatial and temporal archaeological data analysis
  • bayesian analysis and advanced probability models, fuzziness and uncertainty in archaeological data
  • scale-related phenomena and scale transgressions
  • intrasite analysis (representations of stratigraphy , 3D analysis, artifact distributions)
  • landscape analysis (territorial modeling, visibility analysis )
  • optimal survey and sampling strategies
  • process-based modeling and simulation models
  • archaeological predictive modeling and heritage management applications
  • supervised and unsupervised classification and typology, artificial intelligence applications
  • digital excavations and virtual reality
  • archaeological software development, electronic data sharing and publishing

AI science advocates a formalized approach to archaeological inference and knowledge building. It is interdisciplinary in nature, borrowing, adapting and enhancing method and theory of numerous disciplines such as computer science (eg algorithm and software design, database design and theory), geoinformation science ( spatial statistics and modeling, geographic information systems ), artificial intelligence research (supervised classification, fuzzy logic ), ecology (dot pattern analysis), applied mathematics ( graph theory ,probability theory ) and statistics .

Training and research

Scientific progress in archeology, as in any other discipline, requires building abstract, generalized and transferable knowledge about the processes that underlie past human actions and their manifestations. Quantificationprovides the ultimate knowledge of abstracting and extending our scientific capabilities to the limits of intuitive cognition. Quantitative approaches to archaeological information handling and inference constitute a critical body of scientific methods in archaeological research. They provide the tools, algebra , statistics and computer algorithms , to process information too voluminous or complex for purely cognitive , informal inference. They also build a bridge between archeology and quantitative quantitative sciences such as geophysics , geoinformation sciences and applied statistics. And they allow archaeological scientists to design and carry out research in a formal, transparent and comprehensible way.

Being an emerging field of research, AI science is currently a rather dispersed discipline in need of strong, well-funded and institutionalized embedding, especially in academic teaching. Despite its obvious progress and usefulness, today’s quantitative archeology is often inadequately represented in archaeological training and education. Part of this problem may be misconceptions about the seeming conflict between mathematics and humanisticarcheology.

Nevertheless, digital excavation technology, modern heritage management and complex researches require skilled and able researchers to develop new, efficient and reliable means of processing an ever-growing mass of untackled archaeological data and research problems. Thus, providing students of archeology with a solid background in quantitative sciences such as mathematics, statistics and computer sciences can today more important than ever.

Currently, universities based in the UK provide the largest share of studies for prospective quantitative archaeologists, with more institutes in Italy, Germany and the Netherlands developing a strong profile quickly. In Germany, the country’s first lecturer’s position in AI science (“Archeoinformatik”) was established in 2005 at the University of Kiel, while currently there is only one regular junior professor in the field of archaeoinformatics. Classical Archeology at Freie Universität Berlin. From April 2016 a new full professorship in Archaeoinformatics will be established at the University of Cologne (Institute of Archeology).

The most important platform for students and researchers in Quantitative Archeology (CAA), which has been in existence for more than 30 years and is held in a different city of Europe each year. Vienna’s city archeology unit hosts an annual event that is rapidly growing in international importance (see links at bottom).

Further reading

  • Roosevelt, Cobb, Moss, Olson, and Ünlüsoy 2015: “Excavation is Destruction Digitization: Advances in Archaeological Practice,” Journal of Field Archeology , Volume 40, Issue 3 (June 2015), pp. 325-346.
  • Burenhult 2002: Burenhult, G. (ed.): Archaeological Informatics: Pushing The Envelope . CAA2001. Computer Applications and Quantitative Methods in Archeology. BAR International Series 1016, Archaeopress, Oxford.
  • Falser, Michael; Juneja, Monica (Eds.): ‘Archaeologizing’ Heritage? Transcultural Entanglements between Local Social Practices and Global Virtual Realities (Series: Transcultural Research – Heidelberg Studies on Asia and Europe in a Global Context). Springer: Heidelberg / New York, 2013, VIII, 287 p. 200 illus., 90 illus. in color.
  • Huggett and Ross 2004: J. Huggett, S. Ross (eds.): Archaeological Informatics. Beyond Technology . Internet Archeology 15.
  • Marwick, Ben (2016). “Computational Reproducibility in Archaeological Research: Basic Principles and Case Study of Their Implementation”. Journal of Archaeological Method and Theory . doi : 10.1007 / s10816-015-9272-9 .
  • Schlapke 2000: Schlapke, M. Die “Archäoinformatik” am Thüringischen Landesamt für Archäologische Denkmalpflege , Ausgrabungen und Funde im Freistaat Thüringen, 5, 2000, S. 1-5.
  • Zemanek 2004: Zemanek, H .: Archaeological Information – An information scientist looks on archeology. In: Ausserer, KF, Börner, W., Goriany, M. & Karlhuber-Vöckl, L. (eds) 2004. Enter the Past. The E-way into the oven Dimensions of Cultural Heritage. CAA 2003, Computer Applications and Quantitative Methods in Archeology. BAR International Series 1227, Archaeopress, Oxford, 16-26.
  • Archeologia e Calcolatori newspaper homepage
  • Archaeological Computing Newsletter homepage, now a supplement to Archeology and Calcolatori
  • Computational archeology
  • Computational Archeology Blog


  1. Jump up^ Sinclair, Anthony (2016). “The Intellectual Base of Archaeological Research 2004-2013: a visualization and analysis of its disciplinary links, networks of authors and conceptual language”. Internet Archeology (42). doi : 10.11141 / ia.42.8 .

External links

Studying Computational Archeology

  • University College London: M.Sc. GIS and Spatial Analysis in Archeology
  • University of York: MSc Archaeological Information Systems
  • University of Birmingham: MA / PG Dip Landscape Archeology, GIS and Virtual Environments
  • University of Southampton: MSc in Archaeological Computing (Spatial Technologies) and MSc in Archaeological Computing (Virtual Pasts)
  • Archaeoinformatics at Siena University (Italian page)
  • Archaeoinformation science at CAU Kiel
  • University of the Aegean M.Sc. in Cultural Informatics
  • University of Washington Digital Archeology Research Lab
  • The Computational Archeology Lab at San Diego State University focuses on Open-Science and Open-Source approaches to GIS, Agent Based Modeling, Imagery Analysis, and Computation in Archeology and Coupled Human-Natural Systems Science.

Research groups and institutions

  • University College London: Material Culture and Science Research Group
  • University of York: Archaeological Information Systems Research Group
  • University of Southampton: Archaeological Computing Research Group
  • University of Birmingham: HP Visual and Spatial Technology Center Archaeological Computing Division
  • Foundation for Research and Technology Hellas (FORTH), Center for Cultural Informatics
  • Alexandria Archive Institute (AAI)
  • Internet and Open Source for Archeology (2004-2013) was a portal dedicated to the collection and creation of resources to help archaeologists open source eveluate alternatives to proprietary software.
  • Cultural and Educational Technology Institute is a research institute which constitutes an integrated research environment with a continuous interaction with the academic community, in particular with the Democritus University of Thrace, the national and European educational and cultural technology industry, the international scientific community and the public sector .
  • Michigan State University Cultural Heritage Informatics Initiative is a platform for interdisciplinary scholarly collaboration and communication in the field of Cultural Heritage Informatics at Michigan State University. In addition, the initiative strives to equip students (both graduate and undergraduate) with the practical and analytical skills necessary to provide information, communication, and computing technologies to cultural heritage materials.
  • ISAAK (Initiative for Statistical Analysis in Archeology Kiel)


  • “Computer Applications and Quantitative Methods in Archeology (CAA”)
  • “International Conference on Cultural Heritage and New Technologies” (formerly: “Workshop Archeology und Computer” at Vienna)

Archaeological IT service providers

  • L – P: Archeology
  • Archaeovision
  • Oxford Archeology Digital
  • Archeology Data Service
  • Intrasis GIS
  • ArcTron (in German)
  • Open Context: Experimental System for Archaeological Data-Sharing