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Computational archaeology is a subfield ofdigital archeology that focuses on the analysis and interpretation of archaeological data using advanced computational techniques. There are differences between the terms "Computational Archaeology" and "Computer in Archaeology", though they are related to each other. This field employsdata modeling,statistical analysis, andcomputer simulations to understand and reconstruct past human behaviors and societal developments. By leveragingGeographic Information Systems (GIS),predictive modeling, and various simulation tools, computational archaeology enhances the ability to process complex archaeological datasets, providing deeper insights into historical contexts and cultural heritage.
Computational archaeology may include the use ofgeographical information systems (GIS), especially when applied tospatial analyses such asviewshed analysis andleast-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 ofstatistical andmathematicalmodelling,[1] and thecomputer simulation ofhuman behaviour andbehavioural evolution using software tools such asSwarm orRepast 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 asUniversity College London'sSpace Syntax program, also falls under the term 'computational archaeology'. Other examples of computational archaeology include semantic approach towards machine learning, such as data ontology or theCIDOC Conceptual Reference Model, used in the British Museum's ResearchSpace,[2] Arches,[3] and the Global Rock Art Database.[4]
The acquisition, documentation and analysis ofarchaeological finds at excavations and in museums is an important field havingpottery analysis as one of the major topics. In this area 3D-acquisition techniques likestructured light scanning (SLS),photogrammetric methods like "structure from motion" (SfM),computed tomography as well as their combinations[5][6] provide largedata-sets of numerous objects for digital pottery research. These techniques are increasingly integrated into the in-situ workflow of excavations.[7] TheAustrian subproject of theCorpus vasorum antiquorum (CVA) is seminal for digital research on finds within museums.[8]
Computational archaeology is also known as "archaeological informatics" (Burenhult 2002, Huggett and Ross 2004[9]) or "archaeoinformatics" (sometimes abbreviated as "AI", but not to be confused withartificial intelligence).
In recent years, it has become clear thatarchaeologists will only be able to harvest the full potential ofquantitative 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 toinformation processing produces quantitative methods and computersoftware specifically geared towards archaeological problem solving and understanding.
AI science is capable of complementing and enhancing almost any area ofscientific archaeological research. It incorporates a large part of the methods and theories developed inquantitative archaeology since the 1960s but goes beyond former attempts at quantifying archaeology by exploring ways to represent general archaeological information and problem structures as computeralgorithms anddata 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:
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 theCAA conference (see external links section at bottom). At least two journals, the ItalianArcheologia e Calcolatori and the BritishArchaeological 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 isinterdisciplinary in nature, borrowing, adapting and enhancing method and theory from numerous other disciplines such ascomputer 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) andstatistics.
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 ofintuitive 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 computeralgorithms, to process information too voluminous or complex for purelycognitive, informalinference. They also build a bridge between archaeology and numerous quantitative sciences such asgeophysics,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 andhumanistic archaeology.
Nevertheless, digitalexcavation technology, modernheritage 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. In April 2016 the first full professorship in Archaeoinformatics has been 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 onComputer 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).
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