Summary and Keywords
Computational and digital technologies have fundamentally transformed archaeological practice.
Archaeologists routinely use computers and the internet for digitally recording, archiving, displaying, and communicating archaeological knowledge and ideas. Many governmental and funding agencies even stipulate that primary data acquired through grant funding now must be made publicly accessible through digital data archives.
Archaeoinformatics is the study of computational and digital technologies to analyze, archive, and disseminate archaeological records and the locations, contexts, and characteristics of the materials that embody those records. The strength of archaeoinformatics, though, is not in the ubiquitous use of computers or other digital technologies; it is the integrative framework that these technologies provide to create intrinsically interdisciplinary studies of complex archaeological problems. This integrative framework is sustained by adapting knowledge and methods from other disciplines. As a result, archaeoinformatics specialists are often skilled at traversing disciplinary boundaries, and archaeoinformatics, therefore, can be considered a unifying science that bridges disciplines via a digital platform allowing researchers to tackle complex research questions using multipronged research strategies.
Computing in Archaeology
This article is about archaeological computing and the effects that computers have had on knowledge production within the discipline of archaeology. More specifically, the article focuses on archaeoinformatics and, particularly, how this term defines modern approaches to computing within the discipline. It is a nuanced perspective and likely not shared by all practitioners. Yet the way that computers so often are used in the early 21st century—as an integrative framework to synergize interdisciplinary studies of complex archaeological issues—is fundamentally different from how computers have been used in the past. Understanding this difference and why it is important to modern archaeology requires a review of the historical and theoretical development of archaeological computing. Therefore, the article first summarizes the historical and theoretical development of archaeological computing to contextualize the defining characteristics of archaeoinformatics. For more detailed descriptions about the history and theoretical development of archaeological computing, the reader is encouraged to read Lock (2003, 2009), and Huggett (2015).
Social, political, and economic changes in the mid-20th century heavily influenced archaeological practice. Culture-historical approaches, which emphasized cultural relativism and historical particularism, were common in the early 20th century. By mid-century, culture-history had been abandoned largely for “processual” archaeology. Processual approaches emphasized positivism and the idea that hypothetico-deductive reasoning could discover many aspects about past cultures and cultural evolution that were not directly observable from archaeological records. Quantitative methods became especially common at this time. They were seen as a way to standardize data collection, but also to reduce empirical cultural variations to a series of objective truths and mechanistic processes that could be used to establish cross-cultural generalizations (Trigger 1989). Rapid technological and computing advancements in the 1950s further fueled widespread optimism in technology as a source of social progress.
The adoption of computers into archaeology from the early 1960s onward reflected broader postmodern shifts in industrialization and automation as well as a practical need to power the quantitative methodologies that were being used to discover nomothetic generalizations about cultural process. This ideology was in full force by the late 1960s when Cowgill (1967) and Chenhall (1968) identified multivariate statistics and data storage and retrieval as key applications of early archaeological computing (see also Chenhall 1967). Both of these applications were designed to build large data sets of objective facts that they believed could reveal universal similarities between cultures and cultural processes.
Cowgill (1967) was particularly concerned about the technology-first attitude of many early adopters of computers. In his opinion, many people were using computers only because it was the latest and greatest innovation, but he also criticized the naïve expectations of many early computer-driven statistical studies. Specifically, Cowgill was concerned that archaeologists lacked the computer science and statistical training to develop new computer applications. He also linked archaeologists’ lack of training in statistics and computer science to their inability to understand the results that were being generated by preexisting computer-aided statistical methodologies. He argued for greater training in computer science and statistics as well as the development of statistical methods specific to archaeological problems. In other words, from very early on in archaeological computing, archaeologists were worried about the “black-box” effect and the adoption versus adaptation of computer methods into the discipline of archaeology.
The black-box effect is a metaphor to describe when input data and results are known but the methodology to create the results is unknown (Ashby 1965; Doran 1970). In computing, the black-box effect is often associated with the use of commercial or proprietary software where the underlying algorithms used to generate results are unknown (Kvamme 1999). The limitations of black-box approaches can include uncertainty about the validity of generated results and the inability to reproduce results (Morin et al. 2012). Trends toward open-source software seek to circumvent many black-box issues because it allows users access to the mechanics underlying processes (for reviews, see Ducke 2012, 2013). So, for example, instead of running an analysis and taking for granted the results that are returned—and their meaning—users of open-source software can directly observe how results are generated and make changes accordingly.
The debate between adaptation versus adoption of new methods into archaeology concerns whether a method developed in another discipline for other purposes can, or should, be applied to archaeological problems wholly or adapted to suit unique archaeological contexts or issues (Huggett, Reilly, and Lock 2018). Cowgill’s concern about these issues, therefore, foreshadowed conversations about the application of computers in archaeology that is ongoing (e.g., see Siart, Forbriger, and Bubenzer 2018).
Many theoretical ideas that rose to prominence in the 1960s continued into the 1970s. Yet, repeated economic and societal crises led to decreased faith in technology as the source of social progress for both the public and academia (Trigger 1989, 319–320). Within archaeology, attitudes toward computing remained optimistic, albeit with a more nuanced understanding that computers were not panaceas of information but rather tools that can be used (and abused) (Chenhall 1971; Flude 1983; Scholtz and Chenhall 1976; Whallon 1972). The Computer Applications and Quantitative Methods in Archaeology (CAA) organization and annual conference was founded at this time by Susan Laflin. Subsequently, CAA has become a leading source for innovation, theory, and the methodological standards that have come to define computer applications in archaeology.
In the early 1970s, computing was limited to immobile and expensive mainframe computers (e.g., see Hall and Hewson 1977). Advances were being made in networking, however, which allowed computers to be accessed through remote terminals, including from field sites (Buckland 1973; Chenhall 1971; Wilcock 1973). Despite their initial limited availability, computer applications in archaeology were rapidly expanding. Shackley (1973), for example, explored how computers could be used for archaeological sedimentological analysis, while Whallon (1972 and references therein) provided a detailed account of applications, including three-dimensional (3D) and two-dimensional (2D) plotting of artifacts, contour, and density plots, as well as processing of geophysical data sets. A detailed list of 700 other examples can be found in Ryan (1988).
Another prominent development at this time was the archaeological data bank. Large data catalogs were already being collected, like those developed by the Teotihuacan Mapping Project (Cowgill 1967, 1968). However, starting in the mid-1960s (Chenhall 1965), centralized data banks were seen as a way to collate and organize large amounts of archaeological data (both spatially and temporally) for analytical and heritage management purposes. It was believed that the data could be quantitatively reduced down to objective facts and mechanistic processes and thereby sidestep subjective practices like typologies. One of the earliest data banks was the Arkansas Archaeological Survey, which began in 1967 (Chenhall 1971). Development of that system required consideration of many theoretical and practical issues (Scholtz and Chenhall 1976). For example, was the system going to be housed on a single terminal or distributed among terminals via a network? It therefore foreshadowed modern web-based computing. Who would finance and maintain the system? Also, who would have the authority to determine the classificatory scheme of the data and how would that classification be organized? Whallon (1972), for example, argued that it was impossible to accommodate both universal and unique classification systems, and recording everything without any plan would lead to duplicitous and unwieldy catalogs with internal inconsistencies. Thus, classification was also a key topic of interest at that time.
Classification relies on a set of underlying rules to delimit groups of artifacts, sites, or cultures. Ironically, this emphasis on classification and standardizing information collection subverted moves away from using the typologies of the early 20th-century culture historical approaches. Yet what differentiated the classifications of the 1960s and 1970s was that they could be quantifiable, and quantitative methods were often used to validate these systems. The Centre d’Analyse Documentaire pour l’Archéologie (CADA), under the direction of Jean-Claude Gardin, for example, was an early adopter of computers specifically to assist with defining and coding archaeological data (Borillo 1971; Gardin 1971), but the French school of data analysis itself has had a tremendous impact on archaeological computing (Djindjian 1989). The interest in classification raised epistemological and methodological questions, like what data should be coded and how to organize the classification systems (e.g., Gardin 1971), but it also forced archaeologists to assess the theoretical impact that computing had on the discipline at that time, including the logic underlying analytical processes and how that logic impacted data generation (Whallon 1972).
Modeling in archaeological computing also began to be more common. Its prominence can be tied to the increasing interests in general systems theory, epitomized by Clarke’s (1968) book, Analytical Archaeology, which showed how models can be used as heuristic devices to organize, visualize, and compare complex systems of relationships and derive hypotheses about cultural processes. General systems theory sought to characterize behavior (i.e., social, technological, ecological, etc.) within a system of interconnected parts (Trigger 1989). Cybernetic principles were often used to describe how the system responded to external stimuli (Flannery 1968; Watson, LeBlanc, and Redman 1971). Systems theory was ultimately criticized for being unable to explain causality (Salmon 1978), but the concepts, lexicon, and cybernetic process used to explain systemic processes articulated well with computer applications and the logic needed to model real-world phenomena within digital environments (Doran 1970).
The development of microcomputers in the late 1970s opened up the computing floodgates to broad archaeological audiences. Though lacking the power of larger and largely immobile mainframes, personal computers (PCs) were cheaper and more portable and so economics and flexibility drove the applications of PCs into all aspects of archaeological practice. Still, some saw PCs best utilized as terminals to access mainframes for analytical work (Voorrips 1984).
Lock (2003) characterizes this era as largely optimistic about new computer advances and applications within archaeology. Archaeological computing expanded beyond the previous confines of largely US and UK research programs (e.g., Andresen and Rahtz 1988; Arroyo-Bishop and Zarzosa 1989). The presence of PCs at sites enabled computers to be more fully integrated into archaeological data collection strategies from the ground up (Booth, Brough, and Pryor 1984; Lock 1985). Word processing applications also had a tremendous effect on scholarship (O’Flaherty 1988).
Another key development at this time was in digital visualization of archaeological sites and objects (Spicer 1988). Three-dimensional visualization of terrain and piece plots gave archaeologists newfound freedom to view and study issues like site formation processes and the effects of human behaviors across space and time (Boismier and Reilly 1987; Reilly 1989; Reilly and Richards 1988). In their study about terrain models, for example, Harris (1987) highlight the integrative role that computer applications were beginning to play within the discipline to bring together different specialists and aid the interpretation of complex archaeological phenomena.
However, the rapid proliferation of personal computers among archaeologists also created an apprehension about computing applications in the discipline. Concerns were raised about the level of familiarity people needed to use PCs; the role of experts in computer applications (Flude 1983; Lock 1985); the use of adopting conventional versus customized software (Lock and Spicer 1985; Voorrips 1984); and the lack of computing education and training that was available to archaeologists (Martlew 1988; Reilly 1985). Other concerns included “computer addiction” as a potential pitfall of research productivity decline (Lock 1985) and the prestige given to computers that could possibly elevate poor research (Reilly 1985). But the larger issues that these authorities and others were raising was no longer so much about if computers should still be used rather than about understanding how to anticipate the role of the archaeological computing specialist within the discipline. More importantly, the shift ensured that archaeologists lost neither touch with the discipline nor its underlying study of people in the past, and it paralleled contemporary theoretical trends to recognize human agency within archaeological records and subjectivity within archaeological practice.
The 1990s were a dynamic period for archaeological computing. Theoretical shifts away from the objective, positivist, and deterministic ideas of prior decades gave way to newfound recognition of individuals in the archaeological record and the subjectivity inherent within the interpretation of the archaeological record itself. These new approaches emphasized undercurrents of social, political, and economic experiences, actions, and thoughts that would have influenced different cultures and cultural evolution, leading to the formation of the material culture record.
Contextual archaeology (sensu Hodder 1986) had a particularly large effect on archaeological computing. Contextualism emphasized studying archaeological cultures from within and as products of the knowledge, beliefs, and values that structured cultural traditions (Trigger 1989). The approach required data on as many aspects of the archaeological record as possible, or, as Lock (1995) suggested, a “data-rich contextuality.” But contextualism also required an understanding of the links between data and how those relationships structured past ideologies and behaviors. Computers thus were a natural tool to study these complex webs of past people and their worldview.
Computers also became mainstream and more affordable for students and researchers alike in the 1990s. The first generations of archaeologists who had grown up with computing devices were also either in the midst of higher educational training or moving into the professional workforce. Their inherent familiarity with computers and their operation in many aspects of daily life had a profound effect on computer use in archaeology that fostered an uninhibited exploration of what computers could do. Software developments further shifted the creation of computing applications to mainstream archaeology like never before, empowering them to create and execute their own visions and ideas, yet fueling the persistent and uncritical borrowing of software and ideas from other disciplines with little or no formal training. The 1990s thus was a time of unbridled exploration into how far archaeological computing could go.
The edited volume by Scollar et al. (1990) and other academic papers and volumes that are too numerous to cite here illustrate the huge range of computing applications in archaeology to study archaeological sites from space, from the air (Bewley and Rączkowski 2002, and references therein), in the ground (Conyers and Goodman 1997; Johnson 2006), and even using different photographic techniques and equipment (Dorrell 1994; Howell 1992). Developments in graphics capabilities also drove the computerized exploration and visualization of archaeological data at this time (Miller and Richards 1995). Advances in virtual reality technology, for example, allowed a hitherto unprecedented ability to study and experience firsthand archaeological phenomena (Frischer and Dakouri-Hild 2008; Frischer et al. 2000; Kirchner and Jablonka 2001; Reilly 1990). Experiential studies, like those studying the effects of movement and perception, from computerized data sets also became more common, tracking contemporary theoretical trends (see, e.g., Bell and Lock 2000; Harris 2000; Llobera 2000; Tschan, Raczkowski, and Latalowa 2000; Wheatley and Gillings 2000). Multimedia also began to be incorporated more frequently into research papers and databases, greatly enriching the context of archaeological knowledge (Goodson 1989; Rahtz and Reilly 1992). Additionally, the increasing influence of the World Wide Web cannot be overstated, opening up many new avenues for research, collaboration, and information dissemination, and with it an entirely new generation of archaeological computing (Stewart 1996).
The most prolific development during the 1990s, however, was the rapid expansion of Geographic Information Systems (GIS) (Richards 1998) as evidenced by multiple books and edited volumes that were published in the 1990s or in the early 2000s on the subject (Aldenderfer and Maschner 1996; Allen, Green, and Zubrow 1990; Conolly and Lake 2006; Gillings, Mattingly, and Van Dalen 1999; Lock 2000; Lock and Stančič 1995; Maschner 1996; Westcott and Brandon 2000; Wheatley and Gillings 2002), besides dozens of papers and conference proceedings (particularly CAA) (Petrie et al. 1995). GIS are software platforms that allow users to study spatial and non-spatial data together within a spatial framework. Because archaeological data have inherent spatial and temporal qualities, GIS emerged early on as a useful tool to study spatial patterns across and through time. Scollar et al. (1999) link the interest in GIS at this time to cheap high-level graphic displays and hard drives, which facilitated the storage of maps and map data, besides the development of many different digitization devices and commercial software.
Trends toward increasing digitization continued through the new millennium, and it was seen positively as a way to rapidly distribute large amounts of information cheaply and with dynamic content (Schloen 2001). Print, in particular, made large strides online in the early 2000s when new tools like Elsevier’s “ScienceDirect” and Clarivate Analytics’ “Web of Knowledge” (now known as “Web of Science”) debuted, fundamentally transforming how scientific knowledge was disseminated.1 Museums also began to digitize their collections to make these data available online (Maschner, Schou, and Holmes 2013).
Advances in the internet also influenced the long-sought goals of centralized digital archaeological repositories. Stemming from the relatively limited early archaeological data banks, like the United States Arkansas Archaeological Survey (Chenhall 1971) or the United Kingdom’s Archaeology Data Service (Richards 1997), evolved massive, international online repositories of archaeological data. The ARIADNE network, for example, includes twenty-four databases spread across thirteen European countries (Niccolucci and Richards 2013).2 Other significant repositories include the Digital Archaeological Record (tDAR) (Kintigh 2006), Open context, and the Open Science Framework.3 Many private and governmental funding agencies now require data management plans to store and make data available online after research has completed.
The shift to online data storage and access also raised many important questions about data standards and ethics. International and domestic laws regarding the protection of cultural heritage, digital surveillance, and personal identifiable information, for example, have become key issues as information becomes available online (Richardson 2018). Metadata standards that describe how data were collected and processed have also become important in creating flexible and interoperable online databases (Richards 2009).
Additionally, free and open-source software (FOSS) development and use became more popular to ensure scientific reproducibility (Wilson and Edwards 2015). FOSS means that the program and all of its source code are published and available for customization. Although there are a variety of licenses that restrict how the source code can be used and transferred (Ducke 2012), the benefit of FOSS means that anyone can access the software and the underlying processes to replicate analyses (Marwick 2017). Using FOSS also skirts black-box issues of commercial software when the underlying code is not published, hence a full understanding of results is unobtainable. The black-box issue has become so concerning that Kvamme (2018), for example, has argued for a fundamental shift in teaching students from relying on top-down approaches using commercial software to a bottom-up approach that first teaches students coding and an appreciation for the processes underlying different approaches.
Other significant developments in the 2000s include rapid developments in remote sensing applications using commercial unmanned aerial systems, LIDAR, multi- and hyper-spectral imaging, and structure from motion (for a review of these technologies, see Opitz and Herrmann 2018 and references therein). Similarly, there have been major improvements in visual analytics tools to create interfaces for visual exploration and discovery (Gupta and Devillers 2017) as well as the methods to create 3D data from scanning and photogrammetry, enabling rapid recording of sites and artifacts as well as the quantitative analysis of artifacts (Grosman 2016). Computational modeling and archaeological simulation also became mainstream due to more powerful computing resources and readily available desktop software packages to build agent-based models (Lake 2014; Whitley 2016; Wurzer, Kowarik, and Reschreiter 2015 and references therein).
The use of GIS also persisted and increased, and it is now seen as an indispensable tool within archaeological methodology (Verhagen 2018). Kvamme (1999), Verhagen (2012), McCoy and Ladefoged (2009), and Wagtendonk et al. (2009) have all published detailed reviews on the main trends in GIS research during this time, including the development of more humanized GIS that is multi-scalar and multi-sensory (e.g., see Landeschi 2018). The development of humanized GIS is related to shifts back to theory-driven archaeological computing from more method-heavy descriptive approaches. Recording archaeological data, for example, was now seen as less about preserving an exact digital copy of what was observed versus preserving an exact understanding of how excavators interacted with the observed phenomenon (Roosevelt et al. 2015). Spatial patterns and other archaeological phenomena therefore can be viewed as biased and incomplete products of past human agency (Whitley 2017) while computer models, simulations, and other digital techniques only provide one way of interpreting this incomplete perspective of the past (Barton 2013).
What Is Archaeoinformatics?
The historical review of archaeological computing demonstrates how the field has undergone tremendous change since its foundation in the late 1950s and early 1960s. These changes are a microcosm of the idiosyncratic adaptation of computers into nearly every aspect of people’s lives and the transformational impact that this has had on how people communicate and share knowledge, study the world, and structure human social, economic, and political systems. Occurring at the same time were theoretical shifts within the humanities, but specifically within the disciplines of archaeology and anthropology, that challenged the epistemology and ontology of archaeological practice. These shifts and people’s growing familiarity with computers and other digital technologies naturally drove the exploration of how these technologies could be applied to archaeological science, ultimately changing the way that archaeological knowledge is generated, understood, and disseminated. Archaeology is now firmly and irreversibly digitized (Burg 2017; Costopoulos 2016; Grosman 2016; Huggett et al. 2018; Morgan and Eve 2012)
Archaeoinformatics is indelibly linked to this historical development and particularly to people’s growing familiarity with computers. Naturally, as archaeological computing evolved, many different names have also been recommended to describe different computing applications, with archaeoinformatics being just one of them. The oldest term used to describe computer applications in archaeology is “archaeological computing” itself. In use since the 1960s, archaeological computing primarily describes quantitative archaeological analyses using computers, but the phrase has become one of a number of catch-all terms that are used generally to refer to computing within the discipline. By the 1990s, booming interest in computing had led to a proliferation of other names (Grosman 2016). For example, “computational modeling” referred to a set of tools to study spatiotemporal patterns in human societies (Barton 2013), whereas “virtual archaeology” described the use of modern technologies to aid data acquisition, processing, and dissemination (Kirchner and Jablonka 2001). “Digital archaeology” referred to the study of archaeology, information, and communication technologies (Daly and Evans 2004), while “cyber archaeology” was focused on the integration of applied computer science, engineering, and hard sciences to solve archaeological problems (Levy et al. 2012). There was also “archaeological information science,” which generates, manipulates, and represents archaeological data as an information system (Llobera 2011), and “archaeological informatics” (Burenhult and Arvidsson 2002).
The term and focus of archaeoinformatics evolved out of the studies of informatics, information science, and information technology. Informatics focuses on the human impact of computing, particularly the science of how people interact with data and how data are turned into knowledge (Bawden and Robinson 2015). It is rooted in computer science and it is interdisciplinary by design. Early uses of the term, like “archéo-informatique” (Arroyo-Bishop and Zarzosa 1989), however, referred to “informatics” meaning “computers and computing.” More specifically, archaeoinformatics can be defined as an interdisciplinary bridge linking people to computer science and information technology to store, manage, retrieve, and analyze increasingly large archaeological data sets (Kriegel et al. 2010).
The concept of a disciplinary “bridge” seems to have coalesced in the mid-to-late 2010s. Huggett (2015), for example, has proposed a three-wave classification of archaeological computing applications since the 1960s. The first wave, called “archaeological computing,” was the time of curious, if not innocent intrigue with computers and how they could be applied to archaeological issues. By the late 1980s and through the 1990s, a second wave, which Hugget has termed “digital archaeology,” saw wide-scale experimentation and expansion in archaeological computing applications. Hugget’s third wave, which is the most recent, is focused on introspection about how digital technologies have changed archaeology, archaeological practice, knowledge generation, and dissemination. Importantly, it involves the recognition that digital techniques have crept into every aspect of archaeological knowledge production and that it is the archaeological computing specialist (i.e., the archaeoinformatics specialist) that is best positioned to study human and technology relationships.
Beale and Reilly (2017) have also commented on the shifting roles of archeological computing specialists. They envision a person who uses technology to negotiate between archaeologists and between archaeologists and people in other disciplines in order to tailor scientific approaches and methodologies to research questions. The role that Beale and Reilly (2017) suggest is like a mediator who understands and who can translate different approaches, methods, and data between subfields and disciplines. This is important because archaeology has become increasingly specialized and there are now dozens of subfields and focus interest groups. Additionally, research priorities within archaeology, and more broadly the social sciences, are shifting toward answering larger and more complex research issues that crosscut sites, regions, fields, and disciplines (Kintigh et al. 2014). Wuchty, Jones, and Uzzi (2007), for example, have noted a >190 percent increase in the numbers of peer-reviewed social science papers written by research teams versus single authors between 1955 and 2000. Similarly, many studies also recognize the need for interdisciplinary cooperation, for example, in GIS (Bevan and Lake 2013; Burg 2017; Verhagen 2018) and geoarchaeology (Siart et al. 2018).
The broader purview of these studies, their interdisciplinary ethos, and their more complex infrastructure therefore require larger and more diverse teams. For these kinds of projects, a mediator (i.e., a bridge) becomes paramount to ensure that the different and widely varying data sets will have comparable formats and scales, that each researcher understands how their data relates to other data sets, and that the project itself operates as a unified knowledge production body rather than as a disparate amalgamation of researchers and methodologies. Archaeoinformatics is precisely that mediator, whether it is formally recognized or not, and the field utilizes the integrative framework that computing provides via analytical, visualization, and communicative tools to build intellectual bridges between researchers across disciplinary lines to tackle complex research questions using multipronged research strategies.
Cobb et al. (2019), for example, describe seven varied projects at the University of Pennsylvania that integrated archaeologists and engineers to tackle complex archaeological problems ranging from spatial accuracy and attribute recording to colorimetric analysis and bibliographic maintenance. Fisher et al. (2015) likewise show how multidisciplinary research teams in South Africa have approached complex spatiotemporal questions at archaeological sites using high-resolution, color-corrected photomosaics integrated into a 3D GIS. Berggren et al. (2015) describe how the implementation of an intra-site GIS at Çatalhöyük enables communication and reflexive interpretations among diverse international teams of researchers. In the field of nautical archaeology, the rapid proliferation of 3D techniques has led McCarthy et al. (2019), for example, to question who collects these kinds of data within projects—and their training—and how the data are standardized and described. The need for training is also being addressed. For example, Watrall (2019) and Visser, van Zijverden, and Alders (2015), respectively, describe how teaching digital methods in archaeology has been implemented.
Recognizing the archaeoinformatics specialist as a mediator who uses computing platforms to bring different research perspectives together also recognizes two more fundamental features about modern archaeoinformatics research. First, there is a focused shift away from the individual computer toward that of the digital infrastructure itself. This shift may be attributed to the progressive creep of computers into nearly every aspect of people’s personal and professional life, which has had one of the most profound, albeit inconspicuous, impacts on archaeological computing.
In the 1960s and 1970s, for example, computers were exotic, immobile, and expensive systems that required special training and infrastructure to use. It was therefore natural to define archaeological computing at that time based on the use of these alien and largely exclusive machines. Subsequently, computers have become commonplace in everyday personal devices, from watches and telephones to cars and toys. Likewise, tablets (Berggren et al. 2015; Fee, Pettegrew, and Caraher 2013; McKinny and Shai 2018), smartphones (Cascalheira, Goncalves, and Bicho 2014; Shaw and Challis 2013; Welsh and France 2012), and laptops have become intrinsic to archaeological field and lab work, with applications far too numerous to list here.
Another important aspect of modern computers is that they are millions of times more powerful in 2020 than the mainframe computers of the 1960s (Puiu 2017). For example, the 1960s-era Apollo guidance computer that got astronauts to the moon had only slightly greater processing speed and memory than the Nintendo Entertainment System (NES), which was released in 1983 (Experts-Exchange 2015). With a processor speed of just 1.79 MHz and only 2 KB memory, the NES is far removed from common modern smartphones, like the Apple iPhone X, which has a 2.39 GHz processor (i.e., 2,390 MHz) with 3 GB (i.e., 3,000,000 kb) memory. The point is that computers have become so powerful, mobile, and deeply integrated into people’s personal and professional lives that their exclusivity has eroded to the point of rendering them ubiquitous. What this means for archaeological computing is that computer use by itself really can no longer define archaeoinformatic science any more than the use of a trowel defines an archaeologist.
Lastly, it is also important to recognize that creativity and ingenuity drive archaeoinformatic science like it does any science. In lockstep with this ingenuity has been the persistent awareness and adaptation of computing tools and techniques developed in other disciplines toward the study of archaeological problems (Costopoulos 2016). Numerous authors have expressed concerns about borrowing from other disciplines (Huggett 2015; Huggett et al. 2018; Scollar et al. 1999; Siart et al. 2018), particularly because the practice may delegitimize the role of archaeology or relegate archaeologists to being technology users, not producers. Huggett (2015), for example, has suggested that borrowing from other disciplines undervalues the impact that novel developments within the discipline have on itself and on related disciplines. But archaeologists are not trained to develop computer systems or software in the same way that a computer scientist is not trained to do archaeology. Likewise, does driving a car delegitimize or devalue the role of archaeology? Archaeologists are not developing the vehicles that are so crucial to transporting people and equipment to and from field sites.
Just like the mainframe computers of the 1960s, which were not developed by archaeologists or for archaeologists, neither have been most statistical methods, field mapping techniques (including Global Navigation Satellite Systems), Geographic Information Systems, computer operating systems, or even photographic equipment, to name just a few examples. Yet archaeologists learned how to use all these things, and each has had significant impacts on modern archaeological methodology and knowledge generation. Instead of looking at cross-disciplinary borrowing as a problem, it should rather be viewed as a sign of healthy interdisciplinary relationships. Those relationships are not pitting archaeologists against other disciplines (e.g., technology users vs. producers), rather, they are collaborating with them via archaeoinformatic specialists to develop new approaches to study the archaeological record.
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