Computational archaeology

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Computational archaeology describes computer-based analytical methods for the study of long-term human behaviour and behavioural evolution. As with other sub-disciplines that have prefixed 'computational' to their name (e.g. computational biology, computational physics and computational sociology), the term is reserved for (generally mathematical) methods that could not realistically be performed without the aid of a computer.

Computational archaeology may include the use of geographical information systems (GIS), especially when applied to spatial analyses such as viewshed analysis and least-cost path analysis as these approaches are sufficiently computationally complex that they are extremely difficult if not impossible to implement without the processing power of a computer. Likewise, some forms of statistical and mathematical modelling, and the computer simulation of human behaviour and behavioural evolution using software tools such as Swarm or Repast would also be impossible to calculate without computational aid. The application of a variety of other forms of complex and bespoke software to solve archaeological problems, such as human perception and movement within built environments using software such as University College London's Space Syntax program, also falls under the term 'computational archaeology'.

Computational archaeology 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[edit]

In recent years, it has become clear that archaeologists will only be able to harvest the full potential of quantitative methods and computer technology if they become aware of the specific pitfalls and potentials inherent in the archaeological data and research process. AI science is an emerging discipline that attempts to uncover, quantitatively represent and explore specific properties and patterns of archaeological information. Fundamental research on data and methods for a self-sufficient archaeological approach to information processing produces quantitative methods and computer software specifically geared towards archaeological problem solving and understanding.

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

  1. Fundamental research (theoretical AI science) 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. Development of computer algorithms and software (applied AI science) that make this theoretical knowledge available to the user.

There is already a large body of literature on the use of quantitative methods and computer-based analysis in archaeology. 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 Archeologia e 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:

AI science advocates a formalized approach to archaeological inference and knowledge building. It is interdisciplinary in nature, borrowing, adapting and enhancing method and theory from numerous other disciplines such as computer science (e.g. 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 (point pattern analysis), applied mathematics (graph theory, probability theory) and statistics.

Training and research[edit]

Scientific progress in archaeology, as in any other discipline, requires building abstract, generalized and transferable knowledge about the processes that underlie past human actions and their manifestations. Quantification provides the ultimate known way of abstracting and extending our scientific abilities past 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 archaeology and numerous 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 stronger, well-funded and institutionalized embedding, especially in academic teaching. Despite its evident progress and usefulness, today's quantitative archaeology is often inadequately represented in archaeological training and education. Part of this problem may be misconceptions about the seeming conflict between mathematics and humanistic archaeology.

Nevertheless, digital excavation technology, modern heritage management and complex research issues require skilled students and 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 archaeology with a solid background in quantitative sciences such as mathematics, statistics and computer sciences seems today more important than ever.

Currently, universities based in the UK provide the largest share of study programmes for prospective quantitative archaeologists, with many institutes in Italy developing a strong profile quickly (see links at the bottom). In Germany, the country's first lecturer's position in AI science ("Archäoinformatik") was established in 2005 at the University of Kiel (Benjamin Ducke, now at Oxford Archaeology). This was in 2005. Actually the first and only position of a regular junior professorship for "Archäoinformatik" is established in the field of Classical Archaeology at Freie Universität Berlin. There is now the center for studying "Archäoinformatik" in Germany.

The most important platform for students and researchers in quantitative archaeology and AI science is the international conference on Computer Applications and Quantitative Methods in Archaeology (CAA) which has been in existence for more than 30 years now and is held in a different city of Europe each year. Vienna's city archaeology unit also hosts an annual event that is quickly growing in international importance (see links at bottom).

Employment opportunities[edit]

As a general rule, the archaeological job market has insufficient capacities to offer employment for all of the subject's graduates. Training in AI science will provide students with knowledge and skills related to a number of key qualifications and technologies that are sought for in many sectors of today's job market. In archaeology itself, prospective fields of work include heritage management, archaeological IT consulting and software development, digital excavation management, digital archives and museums, digital publishing (e.g. Internet Archaeology), and teaching and training quantitative archaeologists.

See also[edit]

  • Burenhult 2002: Burenhult, G. (ed.): Archaeological Informatics: Pushing The Envelope. CAA2001. Computer Applications and Quantitative Methods in Archaeology. 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. Hugget, S. Ross (eds.): Archaeological Informatics. Beyond Technology. Internet Archaeology 15. http://intarch.ac.uk/journal/issue15/
  • 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 archaeology. In: Ausserer, K.F., Börner, w., Goriany, M. & Karlhuber-Vöckl, L. (eds) 2004. Enter the Past. The E-way into the four Dimensions of Cultural Heritage. CAA 2003, Computer Applications and Quantitative Methods in Archaeology. BAR International Series 1227, Archaeopress, Oxford, 16-26.
  • Archeologia e Calcolatori journal homepage
  • Archaeological Computing Newsletter homepage, now a supplement to Archeologia e Calcolatori
  • Computational archaeology
  • Computational Archaeology Blog

External links[edit]

Studying AI science[edit]

Research groups and institutions[edit]

Important conferences[edit]

Archaeological IT service providers[edit]