Computational archaeology

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,[1] 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

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

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 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 ("Archäoinformatik") was established in 2005 at the University of Kiel (Benjamin Ducke, now at Oxford Archaeology), while currently there is only one regular junior professorship in Archaeoinformatics in the field of Classical Archaeology at Freie Universität Berlin. From April 2016 a new full professorship in Archaeoinformatics will be established at the University of Cologne (Institute of Archaeology).

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).

Further reading

References

  1. ↑ Sinclair, A. (2016) The Intellectual Base of Archaeological Research 2004-2013: a visualisation and analysis of its disciplinary links, networks of authors and conceptual language, Internet Archaeology 42. Retrieved 13 December 2016

Studying AI science

Research groups and institutions

Conferences

Archaeological IT service providers

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.