Digital archaeology is often about deformance rather than justification
Deformation allows for the discovery of many possibilities, and when reconstructing ancient civilizations, allows for an exercise in which one could view multiple possibilities for how each settlement was built where it was, and possibly catch relevant information that would be lost to one simply trying to prove a point.
Rather we might concentrate more on discovery and generation, of ‘interesting way[s] of thinking about this’.
This is a more progressive approach to modern learning, with the focus less on proving why things are the way you believe they are, and instead discovering many ways in which things could have been. The previous method was flawed, forcing one to think with a closed mind, only searching for things that could justify their arguments and being blind to the other arguments that could also be justified.
By the 1980s desktop computing was becoming sufficiently widespread that the use of Geographic Information Systems (GIS) was feasible for greater numbers of archaeologists. The other ‘killer app’ of the time was computer-aided design, which allowed metric 3-dimensional reconstructions from the plans drawn on site by excavators.
The information that these reconstructions can provide enables questions which could only be answered with conjecture to be realistically solved. It allows for more accurate research, especially into past events or those to come, which helps to build a true understanding of history.
Geospatial, digital and Web-based tools are now central to carrying out archaeological research and to communicating archaeological information in a globalized world.
These tools, becoming vital in current archaeological research, promote the approach of encouraging the use of modern technology. The capabilities between a person using these tools and a person who doesn't are so vast that it makes no sense to prohibit use. People should instead be taught to understand how these tools function and use them to better understand the material they are learning. The idea that people are becoming too reliant on new technology only applies to those who use technology for things they should do themselves, not for things that they can't do on their own.
This puts our volume in dialogue with the work of archaeologists such as Ben Marwick, who makes available with his research, the code, the dependencies, and sometimes, an entire virtual machine, to enable other scholars to replicate, reuse, or dispute his conclusions. We want you to reuse our code, to study it, and to improve upon it. We want you to annotate our pages, point out our errors and make digital practice better. For us, digital archaeology is not the mere use of computational tools to answer archaeological questions more quickly. Rather, we want to enable the audience for archaeological thinking to enter into conversation with us, and to do archaeology for themselves. This is one way to practice inclusivity in archaeology.
This is a modern approach to learning that involves adapting to the current technological climate and encourages the use of new technology, rather than prohibiting it. It allows students to understand how the technology works, as well as learn the material.
Everything we do with our digital devices is underpinned by software driven by innumerable algorithms, which are frequently characterised as invisible, black boxes. Striphas (2015), for example, has argued that our reliance on algorithms constitutes what he calls an 'algorithmic culture', while Bogost (2015) goes further and suggests that we live not so much in an algorithmic culture as a 'computational theocracy' with the invisibility of algorithms giving them a transcendental, almost divine character. In the process, algorithms can become mythologised:
This is a genuine societal issue, putting too much trust in the information that computers give to you, or relying on them for tasks which you could easily complete without them. Almost like if it wasn't developed with the assistance of technology, it is deemed unworthy as if it needs a computers approval to be validated. This raises the importance of cross checking information given to you from the internet, and making sure to not blindly follow the word of the computer.
Information flow is reciprocal – we have to set the instrument up correctly over a fixed point and provide it with locational information and the height of the target in order to make the instrument operational. The instrument records horizontal distance and angle, but it is dependent on us to select the location of interest, aim appropriately at the target and trigger the reading. It is also dependent on the staff holder positioning the target correctly over the object of interest, and on both human team members correctly recording any changes in target height. The instrument reports the three-dimensional coordinates back to the user and the process repeats iteratively in the conduct of the survey.
This dependant relationship between machine and people is what makes it acceptable for intellect to be given to machines. They are capable of doing calculations at speeds that are impossible for any human, but they remain machines with a need for directives. This is what keeps them as tools, allowing them to act as an extension of a person and enables that person to develop their ideas into reality.
This introduces an essentially asymmetric relationship between human agent and thing rather than the broadly symmetric interaction implicit in the parity principle. In some respects, this might appear to be akin to the distinction between 'primary agency' and 'secondary agency' (for example, Gell 1998, 21) in which, unlike humans, things do not have agency in themselves but have agency given or ascribed to them. However, the increasing assignment of intelligence in digital devices that enables them to act independent of human agents could suggest that some digital cognitive artefacts possess primary agency as they autonomously act on others – both human and non-human/inanimate things. Arguably this agency is still in some senses secondary in that it is ultimately provided via the human programmer even if this is subsequently subsumed within a neural network generated by the thing itself, for example. This is not the place to develop the discussion of thing agency further (for example, see the debate between Lindstrøm (2015), Olsen and Witmore (2015), and Sørensen (2016)); however, the least controversial position to adopt here is to propose that for the most part the agency of digital cognitive artefacts employed by archaeologists complements rather than duplicates through extending and supporting archaeological cognition. They do this, for example, through providing the capability of seeing beneath the ground or characterising the chemical constituents of objects, neither of which are specifically human abilities. So there is considerable scope for considering the nature of the relationship between ourselves as archaeologists and our cognitive artefacts – how do we interact and in what ways is archaeological cognition extended or complemented by these artefacts?
While controversial, the addition of intelligence to digital cognitive artifacts making them operate independently from humans, remains a completely necessary step in advancement. There are some tasks which would simply require too much time, or are actually impossible for humans to complete if the process relied on their intelligence alone. The ability for an artifcat to work on its own allows for an incredible increase in effeciency, making things that were deemed impossible 20 years ago into a reality.
These cognitive artefacts support us in performing tasks that otherwise at best we would have to conduct using more laborious and time-consuming methods (film photography or measured survey using tapes, for instance) or that we would not be able to undertake (we cannot physically see beneath the ground, or determine the chemical constituents of an object, for example). Furthermore, a characteristic of archaeology is the way that we adopt and apply tools and techniques developed in other domains (Schollar 1999, 8; Lull 1999, 381). Consequently, most if not all of the cognitive artefacts used in archaeology are designed outside their discipline of application, meaning we have little or no control over their development and manufacture, and hence their internal modes of operation have to be taken at face value.
This is the line of thinking that has fast tracked technological evolution, for better or for worse. The idea that our intellect is humanities greatest advantage, and that if we desire something that we are incapable of accomplishing ourselves, we create a tool which makes it possible. The idea of recreating a simulation of an ancient civilization would have been seen as an impossible magic even a couple hundred years ago.
We have seen the cost of terrestrial laser scanners come down in recent years, and, perhaps more significantly, the development of the SIFT (Scale Invariant Feature Transforms) algorithm has seen an explosion in the use of structure from motion photogrammetry as a means of three-dimensional survey using consumer-grade cameras and drones. In the process, we have witnessed changes to the way in which we see the world and capture what we see.
This explosion in the use of photogrammetry as a means of three-dimensional survey was exactly what allowed my research proposal to become a possibility. With this technology it will be possible to reconstruct the settlement locations of ancient greece and allow for further research into proving my hypothesis. This is an example of the web of options opened up with a significant technological breakthrough.