View source for Computational archaeology

{{short description|Archaeological sub-discipline}}
{{for|the science of recovering computer data from obsolete media or formats|Data archaeology}}
{{See also|Digital archeology|Virtual archaeology}}
{{more footnotes needed|date=January 2013}}

'''Computational archaeology''' is a subfield of [[digital 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 employs [[data modeling]], [[statistical analysis]], and [[computer simulations]] to understand and reconstruct past human behaviors and societal developments. By leveraging [[Geographic information system|Geographic 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.
[[File:Computational_archaeology_links_to_subfields_diagram_Samran_Asiabani.svg|thumb|599x599px|Diagram showing major methodological areas within computational archaeology and their links|alt=© Samran Asiabani]]
Computational archaeology may include the use of [[Geographic information system|geographical information system]]s (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]] [[mathematical model|modelling]],<ref>{{cite journal|doi=10.11141/ia.42.8 | issue=42 | title=The Intellectual Base of Archaeological Research 2004-2013: a visualisation and analysis of its disciplinary links, networks of authors and conceptual language | year=2016 | journal=Internet Archaeology | last1 = Sinclair | first1 = Anthony | doi-access=free }}</ref> and the [[computer simulation]] of [[human behaviour]] and [[behavioural evolution]] using software tools such as [[Swarm (simulation)|Swarm]] or [[Repast (Modelling toolkit)|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|University College London's]] [[Space 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 the [[CIDOC CRM|CIDOC Conceptual Reference Model]], used in the British Museum's ResearchSpace,<ref>{{Cite web |title=ResearchSpace.org |url=https://researchspace.org/ |access-date=2025-05-16 |website=researchspace.org |language=en}}</ref> Arches,<ref>{{Cite web |title=Arches |url=https://cidoc-crm.org/Resources/arches |access-date=2025-05-16 |website=cidoc-crm.org |language=en}}</ref> and the Global Rock Art Database.<ref>{{cite journal |last1=Haubt |first1=R. A. |title=The global rock art database: developing a rock art reference model for the RADB system using the CIDOC CRM and Australian heritage examples |journal=ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |date=11 August 2015 |volume=II-5/W3 |pages=89–96 |doi=10.5194/isprsannals-II-5-W3-89-2015|doi-access=free |bibcode=2015ISPAn.II5...89H |hdl=10072/123513 |hdl-access=free }}</ref>

The acquisition, documentation and analysis of [[archaeological find]]s at excavations and in museums is an important field having [[Ceramic|pottery]] analysis as one of the major topics. In this area 3D-acquisition techniques like [[3D scanning|structured light scanning]] (SLS), [[Photogrammetry|photogrammetric]] methods like "[[structure from motion]]" (SfM), [[computed tomography]] as well as their combinations<ref name="CHNT23_Karl" /><ref name="CHNT23_Karl_Video" /> provide large [[Data set|data-sets]] of numerous objects for digital pottery research. These techniques are increasingly integrated into the in-situ workflow of excavations.<ref name="JOGA20_Fecher" /> The [[Austria]]n subproject of the [[Corpus vasorum antiquorum#Scientific and Digital Methods|Corpus vasorum antiquorum]] (CVA) is seminal for digital research on finds within museums.<ref name="CVABeiheft13_Trinkl" />

Computational archaeology is also known as "archaeological informatics"  (Burenhult 2002, Huggett and Ross 2004<ref>{{Cite web |title=Internet Archaeol. 15. Archaeological Informatics. Beyond Technology |url=https://intarch.ac.uk/journal/issue15/inf_index.html |access-date=2022-04-27 |website=intarch.ac.uk |language=en}}</ref>) 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 research|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. [[Basic research|Fundamental research]] on data and methods for a self-sufficient archaeological approach to [[Data processing|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 [[Processual archaeology|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:

#Fundamental research (theoretical AI science) on the structure, properties and possibilities of archaeological data, [[inference]] and [[knowledge building]]. This includes modeling and managing [[Fuzzy measure theory|fuzziness]] and [[uncertainty]] in archaeological data, scale effects, optimal [[Sampling (statistics)|sampling]] strategies and spatio-temporal effects.
#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 [[Computer Applications and Quantitative Methods in Archaeology|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:

* advanced [[statistics]] in archaeology, spatial and temporal archaeological data analysis
* [[bayesian analysis]] and advanced [[probability]] models, [[Fuzzy measure theory|fuzziness]] and [[uncertainty]] in archaeological data
* scale-related phenomena and scale transgressions
* [[GIS in archaeology|intrasite analysis]] (representations of [[stratigraphy]], 3D analysis, [[Artifact (archaeology)|artefact]] distributions)
* landscape analysis (territorial modeling, [[visibility analysis]])
* optimal [[archaeological field survey|survey]] and sampling strategies
* [[Process (science)|process-based]] modeling and [[simulation]] models
* archaeological [[predictive modeling]] and [[heritage management]] applications
* supervised and unsupervised [[Scientific classification|classification]] and typology, [[artificial intelligence]] applications
* digital [[Excavation (archaeology)|excavations]] and [[virtual reality]]
* computational reproducibility of archaeological research<ref>{{Cite journal |last=Marwick |first=Ben |date=2017 |title=Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation |url=http://link.springer.com/10.1007/s10816-015-9272-9 |journal=[[Journal of Archaeological Method and Theory]] |language=en |volume=24 |issue=2 |pages=424–450 |doi=10.1007/s10816-015-9272-9 |issn=1072-5369|url-access=subscription }}</ref><ref>{{Cite journal |last=Marwick |first=Ben |date=2025 |title=Is archaeology a science? Insights and imperatives from 10,000 articles and a year of reproducibility reviews |url=https://linkinghub.elsevier.com/retrieve/pii/S030544032500130X |journal=[[Journal of Archaeological Science]] |language=en |volume=180 |article-number=106281 |doi=10.1016/j.jas.2025.106281|url-access=subscription }}</ref>
* 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 from numerous other disciplines such as [[computer science]] (e.g. algorithm and software design, [[database]] design and theory), [[geoinformation]] science ([[Geostatistics|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 (science)|Quantification]] provides the ultimate known way of abstracting and extending our scientific abilities past the limits of [[Intuition (knowledge)|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 (archaeology)|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. 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 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).

==References==
<references>
<ref name="CVABeiheft13_Trinkl">{{citation|surname1=Trinkl|given1=Elisabeth|title=Interdisziplinäre Dokumentations- und Visualisierungsmethoden, CVA Österreich Beiheft 1|publisher=Verlag der Österreichischen Akademie der Wissenschaften (VÖAW)|language=de|url=http://hw.oeaw.ac.at/7145-4inhalt?frames=yes|access-date=2020-01-14|location=Vienna, Austria|date=2013|isbn=978-3-7001-7544-5}}
</ref>
<ref name="JOGA20_Fecher">{{citation|last1=Fecher|first1=Franziska|last2=Reindel|first2=Markus|last3=Fux|first3=Peter|last4=Gubler|first4=Brigitte|last5=Mara|first5=Hubert|author-link5=Hubert Mara|last6=Bayer|first6=Paul|last7=Lyons|first7=Mike|periodical=Journal of Global Archaeology (JOGA)|volume=1|title=The archaeological ceramic finds from Guadalupe, Honduras: optimizing documentation with a combination of digital and analog techniques|location=Bonn, Germany|date=January 2020|url=https://www.researchgate.net/publication/339848522|url-access=limited|via=[[ResearchGate]]
}}</ref>
<ref name="CHNT23_Karl">{{citation|last1=Karl|first1=Stephan|last2=Bayer|first2=Paul|last3=Mara|first3=Hubert|last4=Márton|first4=András |periodical=Proceedings of the 23rd International Conference on Cultural Heritage and New Technologies (CHNT23)|title=Advanced Documentation Methods in Studying Corinthian Black-figure Vase Painting|location=Vienna, Austria|date=2019|url=https://www.chnt.at/wp-content/uploads/eBook_CHNT23_Karl.pdf|access-date=2020-01-14|isbn=978-3-200-06576-5
}}</ref>
<ref name ="CHNT23_Karl_Video">{{YouTube|155zXG9eyg4|Advanced documentation methods in studying Corinthian black-figure vase painting}} showing a [[Computed Tomography]] scan and rollout of the aryballos No. G26, archaeological collection, [[Graz University]]. The video was rendered using the [[GigaMesh Software Framework]], cf. [[doi:10.11588/heidok.00025189]].</ref>
</references>

== Further reading ==
*[http://www.tandfonline.com/doi/full/10.1179/2042458215Y.0000000004 Roosevelt, Cobb, Moss, Olson, and Ünlüsoy 2015: "Excavation is {{strikethrough|Destruction}} Digitization: Advances in Archaeological Practice," ''Journal of Field Archaeology'', 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 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.&nbsp;200 illus., 90 illus. in color.
*Huggett and Ross 2004: J. Huggett, S. Ross (eds.): ''Archaeological Informatics. Beyond Technology''. ''[[Internet Archaeology]]'' 15. http://intarch.ac.uk/journal/issue15/
*{{cite journal | doi = 10.1007/s10816-015-9272-9 | title=Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation | year=2016 | journal=Journal of Archaeological Method and Theory | volume=24 | issue=2 | pages=424–450 | last1 = Marwick | first1 = Ben| s2cid=43958561 | url=http://ro.uow.edu.au/smhpapers/4034 }}
*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: [[Heinz Zemanek|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.
*[http://soi.cnr.it/archcalc/ Archeologia e Calcolatori journal homepage]
*[https://web.archive.org/web/20110717154320/http://soi.cnr.it/archcalc/acn/ ''Archaeological Computing Newsletter'' homepage, now a supplement to Archeologia e Calcolatori ]
*Computational archaeology
*[http://computationalarchaeology.wordpress.com/ ''Computational Archaeology Blog'']
* {{cite journal |last1=Fisher |first1=Erich |title=Archaeoinformatics |journal=Oxford Research Encyclopedia of Anthropology |date=30 July 2020 |doi=10.1093/acrefore/9780190854584.013.43 |isbn=978-0-19-085458-4 |url=https://oxfordre.com/anthropology/view/10.1093/acrefore/9780190854584.001.0001/acrefore-9780190854584-e-43 |language=en|url-access=subscription }}
* {{cite journal |last1=Jackson |first1=Sarah E  |date=2020 |title=Data-Informed Tools for Archaeological Reflexivity: Examining the substance of bone through a meta-analysis of academic texts |url=https://intarch.ac.uk/journal/issue55/12/index.html |journal=Internet Archaeology |issue=54 |doi=10.11141/ia.55.12 |doi-access=free}}

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[[Category:Computational archaeology| ]]
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