The archaeological record comes in many forms. Some, such as buried sites from volcanic eruptions or other abrupt sedimentary phenomena are perhaps the only ones that leave relatively clean snapshots of moments in the past. And even in those cases time is compressed. Much, if not all other archaeological record is a messy affair. Things, whatever those things may be, artifacts or construction works (i.e., features), moved, modified, destroyed, warped and in a myriad of ways modified from their behavioral contexts. Do we at some point say the record is worthless? Not worth the effort or continuing investigation. Perhaps sometimes this may be justified, but as Daffara and colleagues show, heavily impacted archaeological remains can give us clues and important information about the past. Thoughtful and careful prehistorians can make significant contributions from what appear to be poor archaeological records. 

            In the case of Daffara and colleagues, a number of important theoretical cross-sections can be recognized. For a long time surface archaeology was thought of simply as a way of getting a preliminary peak at the subsurface. From some of the earliest professional archaeologists (e.g., Kidder 1924, 1931; Nelson 1916) to the New Archaeologists of the 1960s, the link between the surface and subsurface was only improved in precision and systematization (Binford et al. 1970). However, at Hatchery West Binford and colleagues not only showed that surface material can be used more reliably to get at the subsurface, but that substantive behavioral inferences can be made with the archaeological record visible on the surface.

            Much more important are the behavioral implications drawn from surface material. I am not sure we can cite the first attempts at interpreting prehistory from the surface manifestations of the archaeological record, but a flurry of such approaches proliferated in the 1970s and beyond (Dunnell and Dancey 1983; Ebert 1992; Foley 1981).  Off-site archaeology, non-site archaeology, later morphing into landscape archaeology all deal strictly with surface archaeological record to aid in understanding the past. With the current paper, Daffara and colleagues (2024) are clearly in this camp. Although still not widely accepted, it is clear that some behaviors (parts of systems) can only be approached from surface archaeological record. It is very unlikely that a future archaeologist will be able to excavate an entire human social/cultural system; people moving from season to season, creating multiple long and short term camps, travelling, procuring resources, etc. To excavate an entire system one would need to excavate 20,000 km2 or some similarly impossible task. Even if it was physically possible to excavate such an enormous area, it is very likely that some of contextual elements of any such system will be surface manifestations. 

Without belaboring the point, surface archaeological record yields data like any other archaeological record. We must contextual the archaeological artifacts or features weather they come from surface or below. Daffara and colleagues show us that we can learn about deep prehistory of northern Italy, with collections that were unsystematically collected, biased by agricultural as well as other land deformations agents. They carefully describe the regional prehistory as we know it, in particular specific well documented sites and assemblages as a means of applying such knowledge to less well controlled or uncontrolled collections.

References

Binford, L., Binford, R. S. R., Whallon, R. and Hardin, M. A. (1970). Archaeology of Hatchery West. Memoirs of the Society for American Archaeology, No. 24, Washington D.C.

Daffara, S., Giraudi, C., Berruti, G. L. F., Caracausi, S. and Garanzini, F. (2024). First evidence of a Palaeolithic frequentation of the Po plain in Piedmont: the case of Trino (north-western Italy), OSF Preprints, pz4uf, ver. 6 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.31219/osf.io/pz4uf

Dunnell, R. C. and Dancey, W. S. (1983). The siteless survey: a regional scale data collection strategy. In Advances in Archaeological Method and Theory, vol. 6, edited by Michael B. Schiffer, pp. 267-287. Academic Press, New York.

Ebert, J. I. (1992). Distributional Archaeology. University of New Mexico Press, Albuquerque.

Foley, R. A. (1981). Off site archaeology and human adaptation in eastern Africa: An analysis of regional artefact density in the Amboseli, Southern Kenya. British Archaeological Reports International Series 97. Cambridge Monographs in African Archaeology 3. Oxford England.

Kidder, A. V. (1924). An Introduction to the Study of Southwestern Archaeology, With a Preliminary Account of the Excavations at Pecos. Papers of the Southwestern Expedition, Phillips Academy, no. 1. New Haven, Connecticut.

Kidder, A. V. (1931). The Pottery of Pecos, vol. 1. Papers of the Southwestern Expedition, Phillips Academy. New Haven, Connecticut.

Nelson, N. (1916). Chronology of the Tano Ruins, New Mexico. American Anthropologist 18(2):159-180.

Various biological systems have been subjected to mathematical modelling to enhance our understanding of the intricate interactions among different species. Among these models, the predator-prey model holds a significant position. Its relevance stems not only from its application in biology, where it largely governs the coexistence of diverse species in open ecosystems, but also from its utility in other domains. 

Predator-prey dynamics have long been a focal point in population ecology, yet access to real-world data is confined to relatively brief periods, typically less than a century. Studying predator-prey dynamics over extended periods presents challenges due to the limited availability of population data spanning more than a century. The most extensive dataset is the hare-lynx records from the Hudson Bay Company, documenting a century of fur trade [1]. However, other records are considerably shorter, usually spanning decades [2,3]. This constraint hampers our capacity to investigate predator-prey interactions over centennial or millennial scales. 

Marom and Wolkowski [4] propose here that leveraging regional radiocarbon databases offers a solution to this challenge, enabling the reconstruction of predator-prey population dynamics over extensive timeframes. To substantiate this proposition, they draw upon examples from Pleistocene Beringia and the Holocene Judean Desert. This approach is highly relevant and might provide insight into ecological processes occurring at a time scale beyond the limits of current ecological datasets. 

The methodological approach employed in this article proposes that the summed probability distribution (SPD) of predator radiocarbon dates, which reflects changes in population size, will demonstrate either more or less variation than anticipated from random sampling in a homogeneous distribution spanning the same timeframe. A deviation from randomness would imply a covariation between predator and prey populations. This basic hypothesis makes no assumptions about the frequency, mechanism, or cause of predator-prey interactions, as it is assumed that such aspects cannot be adequately tested with the available data. If validated, this hypothesis would offer initial support for the idea that long-term regional radiocarbon data contain signals of predator-prey interactions. This approach could justify the construction of larger datasets to facilitate a more comprehensive exploration of these signal structures.

References

[1] Elton, C. and Nicholson, M., 1942. The Ten-Year Cycle in Numbers of the Lynx in Canada. J. Anim. Ecol. 11, 215–244.

[2] Gilg, O., Sittler, B. and Hanski, I., 2009. Climate change and cyclic predator-prey population dynamics in the high Arctic. Glob. Chang. Biol. 15, 2634–2652. https://doi.org/10.1111/j.1365-2486.2009.01927.x

[3] Vucetich, J.A., Hebblewhite, M., Smith, D.W. and Peterson, R.O., 2011. Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems. J. Anim. Ecol. 80, 1236–1245. https://doi.org/10.1111/j.1365-2656.2011.01855.x

[4] Marom, N. and Wolkowski, U. (2024). A note on predator-prey dynamics in radiocarbon datasets, BioRxiv, 566733, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.1101/2023.11.12.566733

This paper (Stephan 2023) is about the use of video games as a pedagogical tool in class. Instead of taking the perspective of a lecturer, the author seeks the student’s perspectives to evaluate the success of an interactive teaching method at the crossroads of history, archaeology, and classics. The paper starts with a literature review, that highlights the intensive use of video games among college students and high schoolers as well as the impact video games can have on learning about the past. The case study this paper is based on is made with the game Assassin’s Creed: Odyssey, which is introduced in the next part of the paper as well as previous works on the same game. The author then explains his method, which entailed the tasks students had to complete for a class in classics. They could either choose to play a video game or more classically read some texts. After the tasks were done, students filled out a 14-question-survey to collect data about prior gaming experience, assignment enjoyment, and other questions specific to the assignments.

The results were based on only a fraction of the course participants (n=266) that completed the survey (n=26), which is a low number for doing statistical analysis. Besides some quantitative questions, students had also the possibility to freely give feedback on the assignments. Both survey types (quantitative answers and qualitative feedback) solely relied on the self-assessment of the students and one might wonder how representative a self-assessment is for evaluating learning outcomes. Both problems (size of the survey and actual achievements of learning outcomes) are getting discussed at the end of the paper, that rightly refers to its results as preliminary. I nevertheless think that this survey can help to better understand the role that video games can play in class. As the author rightly claims, this survey needs to be enhanced with a higher number of participants and a better way of determining the learning outcomes objectively. This paper can serve as a start into how we can determine the senseful use of video games in classrooms and what students think about doing so.

“Archaeology, Typology and Machine Epistemology” submitted by G. Lucas (1) offers a refreshing and welcome reflection on the role of computer-based practice, type-thinking and approaches to typology in the age of big data and the widely proclaimed ‘Third Science Revolution’ (2–4). At the annual meeting of the EAA in Maastricht in 2017, a special thematic block was dedicated to issues and opportunities linked to the Third Science Revolution in archaeology “because of [its] profound and wide ranging impact on practice and theory in archaeology for the years to come” (5). Even though the Third Science Revolution, as influentially outlined by Kristiansen in 2014 (2), has occasionally also been met with skepticism and critique as to its often implicit scientism and epistemological naivety (6–8), archaeology as a whole seems largely euphoric as to the promises of the advancing ‘revolution’. As Lucas perceptively points out, some even regard it as the long-awaited opportunity to finally fulfil the ambitions and goals of Anglophone processualism. The irony here, as Lucas rightly notes, is that early processualists initially foregrounded issues of theory and scientific epistemology, while much work conducted under the banner of the Third Science Revolution, especially within its computational branches, does not. Big data advocates have echoed Anderson’s much-cited “end of theory” (9) or at least emphatically called for an ‘empirization’ and ‘computationalization’ of theory, often under the banner of ‘data-driven archaeology’ (10), yet typically without much specification of what this is supposed to mean for archaeological theory and reflexivity. The latter is indeed often openly opposed by archaeological Third Science Revolution enthusiasts, arguably because it is viewed as part of the supposedly misguided ‘post-modernist’ project.

Lucas makes an original meta-archaeological contribution here and attempts to center the epistemological, ontological and praxeological dimensions of what is actually – in situated archaeological praxis and knowledge-production – put at stake by the mobilization of computers, algorithms and artificial intelligence (AI), including its many but presently under-reflected implications for ordering practices such as typologization. Importantly, his perspective thereby explicitly and deliberately breaks with the ‘normative project’ in traditional philosophy of science, which sought to nail down a universal, prescriptive way of doing science and securing scientific knowledge. He instead focuses on the practical dimensions and consequences of computer-reliant archaeologies, what actually happens on the ground as researchers try to grapple with the digital and the artefactual and try to negotiate new insights and knowledge, including all of the involved messiness – thereby taking up the powerful impetus of the broader practice turn in interdisciplinary science studies and STS (Science and Technology Studies (11)) (12–14), which have recently also re-oriented archaeological self-observation, metatheory and epistemology (15). This perspective on the dawning big data age in archaeology and incurred changes in the status, nature and aims of type-thinking produces a number of important insights, which Lucas fruitfully discusses in relation to promises of ‘automation’ and ‘novelty’ as these feature centrally in the rhetorics and politics of the Third Science Revolution. 

With regard to automation, Lucas makes the important point that machine or computer work as championed by big data proponents cannot adequately be qualified or understood if we approach the issue from a purely time-saving perspective. The question we have to ask instead is what work do machines actually do and how do they change the dynamics of archaeological knowledge production in the process? In this optic, automation and acceleration achieved through computation appear to make most sense in the realm of the uncontroversial, in terms of “reproducing an accepted way of doing things” as Lucas says, and this is precisely what can be observed in archaeological practice as well. The ramifications of this at first sight innocent realization are far-reaching, however. If we accept the noncontroversial claim that automation partially bypasses the need for specialists through the reproduction of already “pre-determined outputs”, automated typologization would primarily be useful in dealing with and synthesizing larger amounts of information by sorting artefacts into already accepted types rather than create novel types or typologies. If we identity the big data promise at least in part with automation, even the detection of novel patterns in any archaeological dataset used to construct new types cannot escape the fact that this novelty is always already prefigured in the data structure devised. The success of ‘supervised learning’ in AI-based approaches illustrates this. Automation thus simply shifts the epistemological burden back to data selection and preparation but this is rarely realized, precisely because of the tacit requirement of broad non-contentiousness. 

Minimally, therefore, big data approaches ironically curtail their potential for novelty by adhering to conventional data treatment and input formats, rarely problematizing the issue of data construction and the contested status of (observational) data themselves. By contrast, they seek to shield themselves against such attempts and tend to retain a tacit universalism as to the nature of archaeological data. Only in this way is it possible to claim that such data have the capacity to “speak for themselves”. To use a concept borrowed from complexity theory, archaeological automation-based type-construction that relies on supposedly basal, incontrovertible data inputs can only ever hope to achieve ‘weak emergence’ (16) – ‘strong emergence’ and therefore true, radical novelty require substantial re-thinking of archaeological data and how to construct them. This is not merely a technical question as sometimes argued by computational archaeologies – for example with reference to specifically developed, automated object tracing procedures – as even such procedures cannot escape the fundamental question of typology: which kind of observations to draw on in order to explore what aspects of artefactual variability (and why). The focus on readily measurable features – classically dimensions of artefactual form – principally evades the key problem of typology and ironically also reduces the complexity of artefactual realities these approaches assert to take seriously. The rise of computational approaches to typology therefore reintroduces the problem of universalism and, as it currently stands, reduces the complexity of observational data potentially relevant for type-construction in order to enable to exploration of the complexity of pattern. It has often been noted that this larger configuration promotes ‘data fetishism’ and because of this alienates practitioners from the archaeological record itself – to speak with Marxist theory that Lucas briefly touches upon. We will briefly return to the notion of ‘distance’ below because it can be described as a symptomatic research-logical trope (and even a goal) in this context of inquiry. 

In total, then, the aspiration for novelty is ultimately difficult to uphold if computational archaeologies refuse to engage in fundamental epistemological and reflexive self-engagement. As Lucas poignantly observes, the most promising locus for novelty is currently probably not to be found in the capacity of the machines or algorithms themselves, but in the modes of collaboration that become possible with archaeological practitioners and specialists (and possibly diverse other groups of knowledge stakeholders). In other words, computers, supercomputers and AI technologies do not revolutionize our knowledge because of their superior computational and pattern-detection capacities – or because of some mysterious ‘superintelligence’ – but because of the specific ‘division of labour’ they afford and the cognitive challenge(s) they pose. Working with computers and AI also often requires to ask new questions or at least to adapt the questions we ask. This can already be seen on the ground, when we pay attention to how machine epistemologies are effectively harnessed in archaeological practice (and is somewhat ironic given that the promise of computational archaeology is often identified with its potential to finally resolve "long-standing (old) questions"). The Third Science Revolution likely prompts a consequential transformation in the structural and material conditions of the kinds of ‘distributed’ processes of knowledge production that STS have documented as characteristic for scientific discoveries and knowledge negotiations more generally (14, 17, 18). This ongoing transformation is thus expected not only to promote new specializations with regard to the utilization of the respective computing infrastructures emerging within big data ecologies but equally to provoke increasing demand for new ways of conceptualizing observations and to reformulate the theoretical needs and goals of typology in archaeology. The rediscovery of reflexivity as an epistemic virtue within big data debates would be an important step into this direction, as it would support the shared goal of achieving true epistemic novelty, which, as Lucas points out, is usually not more than an elusive self-declaration. Big data infrastructures require novel modes of human-machine synergy, which simply cannot be developed or cultivated in an atheoretical and/or epistemological disinterested space. 

Lucas’ exploration ultimately prompts us to ask big questions (again), and this is why this is an important contribution. The elephant in the room, of course, is the overly strong notion of objectivity on which much computational archaeology is arguably premised – linked to the vow to eventually construct ‘objective typologies’. This proclivity, however, re-tables all the problematic debates of the 1960s and – to speak with the powerful root metaphor of the machine fueling much of causal-mechanistic science (19, 20) – is bound to what A. Wylie (21) and others have called the ‘view from nowhere’. Objectivity, in this latter view, is defined by the absence of positionality and subjectivity – chiefly human subjectivity – and the promise of the machine, and by extension of computational archaeology, is to purify and thus to enhance processes of knowledge production by minimizing human interference as much as possible. The distancing of the human from actual processes of data processing and inference is viewed as positive and sometimes even as an explicit goal of scientific development. Interestingly, alienation from the archaeological record is framed as an epistemic virtue here, not as a burden, because close connection with (or even worse, immersion in) the intricacies of artefacts and archaeological contexts supposedly aggravates the problem of bias. The machine, in this optic, is framed as the gatekeeper to an observer-independent reality – which to the backdoor often not only re-introduces Platonian/Aristotelian pledges to a quasi-eternal fabric of reality that only needs to be “discovered” by applying the right (broadly nonhuman) means, it is also largely inconsistent with defendable and currently debated conceptions of scientific objectivity that do not fall prey to dogma.  

Furthermore, current discussions on the open AI ChatGPT have exposed the enormous and still under-reflected dangers of leaning into radical renderings of machine epistemology: precisely because of the principles of automation and the irreducible theory-ladenness of all data, ecologies such as ChatGPT tend to reinforce the tacit epistemological background structures on which they operate and in this way can become collaborators in the legitimization and justification of the status quo (which again counteracts the potential for novelty) – they reproduce supposedly established patterns of thought. This is why, among other things, machines and AI can quickly become perpetuators of parochial and neocolonial projects – their supposed authority creates a sense of impartiality that shields against any possible critique. With Lucas, we can thus perhaps cautiously say that what is required in computational archaeology is to defuse the authority of the machine in favour of a new community archaeology that includes machines as (fallible) co-workers. Radically put, computers and AI should be recognized as subjects themselves, and treated as such, with interesting perspectives on team science and collaborative practice.

Bibliography

1. Lucas, G. (2022). Archaeology, Typology and Machine Epistemology. https:/doi.org/10.5281/zenodo.7620824.

2. Kristiansen, K. (2014). Towards a New Paradigm? The Third Science Revolution and its Possible Consequences in Archaeology. Current Swedish Archaeology 22, 11–34. https://doi.org/10.37718/CSA.2014.01.

3. Kristiansen, K. (2022). Archaeology and the Genetic Revolution in European Prehistory. Elements in the Archaeology of Europe. https://doi.org/10.1017/9781009228701

4. Eisenhower, M. S. (1964). The Third Scientific Revolution. Science News 85, 322/332. https://www.sciencenews.org/archive/third-scientific-revolution.

5. The ‘Third Science Revolution’ in Archaeology. http://www.eaa2017maastricht.nl/theme4 (March 16, 2023).

6. Ribeiro, A. (2019). Science, Data, and Case-Studies under the Third Science Revolution: Some Theoretical Considerations. Current Swedish Archaeology 27, 115–132. https://doi.org/10.37718/CSA.2019.06

7. Samida, S. (2019). “Archaeology in times of scientific omnipresence” in Archaeology, History and Biosciences: Interdisciplinary Perspectives, pp. 9–22. https://doi.org/10.1515/9783110616651

8. Sørensen, T. F.. (2017). The Two Cultures and a World Apart: Archaeology and Science at a New Crossroads. Norwegian Archaeological Review 50, 101–115. https://doi.org/10.1080/00293652.2017.1367031

9. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/.

10. Gattiglia, G. (2015). Think big about data: Archaeology and the Big Data challenge. Archäologische Informationen 38, 113–124. https://doi.org/10.11588/ai.2015.1.26155

11. Hackett, E. J. (2008). The handbook of science and technology studies, Third edition, MIT Press/Society for the Social Studies of Science.

12. Ankeny, R., Chang, H., Boumans, M. and Boon, M. (2011). Introduction: philosophy of science in practice. Euro Jnl Phil Sci 1, 303. https://doi.org/10.1007/s13194-011-0036-4

13. Soler, L., Zwart, S., Lynch, M., Israel-Jost, V. (2014). Science after the Practice Turn in the Philosophy, History, and Social Studies of Science, Routledge.

14. Latour, B. and Woolgar, S. (1986). Laboratory life: the construction of scientific facts, Princeton University Press.

15. Chapman, R. and Wylie, A. (2016) Evidential reasoning in archaeology, Bloomsbury Academic.

16. Greve, J. and Schnabel, A. (2011). Emergenz: zur Analyse und Erklärung komplexer Strukturen, Suhrkamp.

17. Shapin, S., Schaffer, S. and Hobbes, T. (1985). Leviathan and the air-pump: Hobbes, Boyle, and the experimental life, including a translation of Thomas Hobbes, Dialogus physicus de natura aeris by Simon Schaffer, Princeton University Press.

18. Galison, P. L. and Stump, D. J. (1996).The Disunity of Science: Boundaries, Contexts, and Power, Stanford University Press.

19. Pepper, S. C. (1972). World hypotheses: a study in evidence, 7. print, University of California Press.

20. Hussain, S. T. (2019). The French-Anglophone divide in lithic research: A plea for pluralism in Palaeolithic Archaeology, Open Access Leiden Dissertations. https://hdl.handle.net/1887/69812 

21. A. Wylie, A. (2015). “A plurality of pluralisms: Collaborative practice in archaeology” in Objectivity in Science, pp. 189-210, Springer. https://doi.org/10.1007/978-3-319-14349-1_10

The paper by Brown & Lewis [1] presents an approach to measure seasonal mobility and subsistence practices. In order to do so, the paper proposes a Bayesian mixture model to estimate the annual distribution of shellfish harvesting activity. Following the recommendations of the two reviewers, the paper presents a clear and innovative method to assess seasonal mobility for prehistoric groups, although it could benefit from additional references regarding isotopic literature.

While the adequacy of isotope analysis for estimating mobility patterns in Archaeology has been extensively proven by now, work on specific seasonal mobility is not that much abundant. However, this is a key issue, since seasonal mobility is one of the main social components defining the differences between groups both considering farming vs hunting and gathering or even among hunter-gatherer groups themselves. In this regard, the paper brings a valuable methodological resources that can be used for further research in this issue.

One of its greatest values is the fact that it can quantify the uncertainty present in previous isotope studies in seasonal mobility. As stated by the authors, the model can still undergo several optimisation aspects, but as it stands, it is already providing a valuable asset regarding the quantification of uncertainy in the isotopic studies of seasonal mobility.

Reference

[1] Brown, J. and Lewis, G. (2024). Inferring shellfishing seasonality from the isotopic composition of biogenic carbonate: A Bayesian approach. Zenodo, 7949547, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7949547