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

“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 “A return to function as the basis of lithic classification” by Radu Iovita (2024) is a contribution to an upcoming volume on the role of typology and type-thinking in current archaeological theory and praxis edited by the PCI recommenders. In this context, the paper offers an in-depth discussion of several crucial dimensions of typological thinking in past and current lithic studies, namely:

• “common sense” in archaeology, discussed based on earlier proposals by influential anthropologist Clifford Geertz (1975),
• “function”, argued by the author to be the “fundamental property of tools”,
• “cognitive” aspects, said to be reflected in the “property we naturally use to classify” stone tools and then argued to be grounded in function,
• “traceology” as an archaeological bundle of methods and practices to determine (tool) function, discussing the current status of this research perspective in archaeology and its future.

Discussing and importantly re-articulating these concepts, Iovita ultimately aims at “establishing unified guiding principles for studying a technology that spans several million years and several different species whose brain capacities range from ca. 300–1400 cm³”.

The notion that tool function should dictate classification is not new (e.g. Gebauer 1987). It is particularly noteworthy, however, that the paper engages carefully with various relevant contributions on the topic from non-Anglophone research traditions. First, its considering works on lithic typologies published in other languages, such as Russian (Sergei Semenov), French (Georges Laplace), and German (Joachim Hahn). Second, it takes up the ideas of two French techno-anthropologists, in particular: 

• Anthropologist of technics François Sigaut's (1940-2012) distinction of form, function, and “fonctionnement” (Sigaut 1991). Iovita proposes to draw and recast this tripartition, splitting the notion of function into “structural function” (a concept encompassing biological function as well as the “interface between the tool and its environment”), “operation” (which “relates to learning the function of artifacts from others and representing them through their motor associations”), and "designer-intended function” (DIF). Iovita shows how these distinctions can be used to clarify the ways and the grounds on which we build lithic typologies.
• Structural anthropologist Claude Lévi-Strauss' (1908-2009) concept of “bricolage”, influentially proposed and developed in his La pensée sauvage (Lévi-Strauss 1962); this concept was also much discussed by North American anthropologist Clifford Geertz and more recently critically re-considered in the English-speaking literature thanks to a new translation of Lévi-Strauss' original text (Lévi-Strauss 2021).

Interestingly, Iovita grounds his argumentation on insights from primatology, psychology and the cognitive sciences, to the extent that they fuel discussion on archaeological concepts and methods. Results regarding the so-called “design stance” for example play a crucial role: coined by philosopher and cognitive scientist and philosopher Daniel Dennett (1942-2024), this notion encompasses the possible discrepancies between the designer’s intended purpose and the object's current functions. DIF, as discussed by Iovita, directly relates to this idea, illustrating how concepts from other sciences can fruitfully be injected into archaeological thinking.

Lastly, readers should note the intellectual contents generated on PCI as part of the reviewing process of the paper itself: both the reviewers and the author have engaged in in-depth discussions on the idea of (tool) “function” and its contested relationship with form or typology, delineating and mapping different views on these key issues in lithic study which are worth reading on their own.

References

Gebauer, A. B. (1987). Stylistic Analysis. A Critical Review of Concepts, Models, and Interpretations, Journal of Danish Archaeology, 6, p. 223–229.

Geertz, C. (1975). Common Sense as a Cultural System, The Antioch Review, 33 (1), p. 5–26.

Iovita, R. (2024). A return to function as the basis of lithic classification. Zenodo, 7734147, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7734147

Lévi-Strauss, C. (1962). La pensée sauvage. Paris: Plon.

Lévi-Strauss, C. (2021). Wild Thought: A New Translation of “La Pensée sauvage”. Translated by Jeffrey Mehlman & John Leavitt. Chicago, Illinois: University of Chicago Press.

Sigaut, F. (1991). “Un couteau ne sert pas à couper, mais en coupant. Structure, fonctionnement et fonction dans l'analyse des objets” in 25 Ans d'études technologiques en préhistoire : Bilan et perspectives, Juan-les-Pins: Éditions de l'association pour la promotion et la diffusion des connaissances archéologiques, p. 21-34.

This paper (Panagiotidis et al. 2024) discusses the digital approach to the ‘digital storyline’ of the Monastery of Brontochion of Mystras, Peloponnese, Greece. Using drone and terrestrial photogrammetric recording techniques, their goal is to produce maps and 3D models that will be integrated in the overall study of the medieval city, as well as function as a comprehensive visualisation of the archaeological site throughout its history. This will take shape as a web application using ArcGIS Online that will be a valuable platform presenting interactive visual information to accompany written publications. In addition, the assets in the database will be available for further advanced purposes, such as suggested by the authors; xR applications, educational games or digital smart guides. The authors of the paper do a good job in describing the historical background of the site and the purpose of the digital documentation techniques, and the applied methods and the technical details of the produced models are dealt with in detail.

The paper presents a compelling case for an integrative approach to using digital recording techniques at an architectonically complex site for Cultural Heritage Management. It can be placed in a series of studies discussing how monumental sites can benefit from advanced digital recording techniques such as those presented in this paper (see for example Waagen and Wijngaarden 2024). The paper is recommended as an interesting read for all who are involved in this field.

References

Panagiotidis V. Vayia, Valantou Vasiliki, Kazolias Anastasios, and Zacharias Nikolaos. (2024). At the Edge of a City: The Digital Storyline of the Brontochion Monastery of Mystras. Zenodo, 8126952, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8126952

Waagen, J., and van Wijngaarden, G. J. (2024). Understanding Archaeological Site Topography: 3D Archaeology of Archaeology. Journal of Computer Applications in Archaeology, 7(1), 237–243. https://doi.org/10.5334/jcaa.157 

​

This paper presents the use of a participatory arts-based methodology to understand how digital and analogue tools affect individuals' participation in the process of archaeological recording and interpretation. The preliminary results of this work highlight the importance of rethinking archaeologists' relationship with different recording methods, emphasising the need to recognise the value of both approaches and to adopt a documentation strategy that exploits the strengths of both analogue and digital methods.

Although a larger group of participants with broader and more varied experience would have provided a clearer picture of the impact of technology on current archaeological practice, the article makes an important contribution in highlighting the complex and not always easy transition that archaeologists trained in analogue methods are currently experiencing when using digital technology.

 This is assessed by using arts-based methodologies to enable archaeologists to consider how digital technologies are changing the relationship between mind, body and practice.

I found the range of experiences described in the papers by the archaeologists involved in the experiment particularly interesting and very representative of the change in practice that we are all experiencing.  As the article notes, the two approaches cannot be directly compared because they offer different possibilities: if analogue methods foster a deeper connection with the archaeological material, digital documentation seems to be perceived as more effective in terms of data capture, information exchange and data sharing (Araar et al., 2023).

It seems to me that an important element to consider in such a study is the generational shift and the incredible divide between native and non-native digital.

 The critical issues highlighted in the paper are central and provide important directions for navigating this ongoing (digital) transition.

References

Araar, L., Morgan, C. and Fowler, L. (2023) Body Mapping the Digital: Visually representing the impact of technology on archaeological practice., Zenodo, 7990581, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7990581