Agents, networks and models: formal approaches to systems, relationships and change in archaeology
Barcelona Supercomputing Center, Spain
University of Oxford, United Kingdom
University of Auckland, New Zealand
Even if much ink has already been spilled on the need to use formal, computational methods to represent theories, compare alternative hypotheses and develop more complex narratives, the idea is still far from being firmly established in archaeology.
Complexity Science, the study of systems consisting of a collection of interconnected relationships and parts, provides a useful framework for formalising social and socio-natural models and it is often under this umbrella term that formal models are presented in archaeology. It has a particular appeal for researchers concerned with humans, as it stresses the importance of individual actions and interactions, as well as relations between individuals and wider system elements. Archaeology is a discipline that studies long-term, large-scale shifts in social change, human evolution, and relationships with the environment; how these phenomena emerge through the actions and interactions of individuals are questions that lie at the heart of our interests. Complexity Science offers an arsenal of methods that were developed specifically to tackle these kind of mulitscalar, multifaceted research questions.
This session will provide a forum for archaeological case studies developed using Complexity Science toolkits as well as for more methodological papers. We invite submissions of models at any stage of development from the first formalisation of the conceptual model to presenting final results.
Possible topics include but are not limited to applications or discussions of the following approaches:
- Complexity science,
- Network science,
- Agent-based and equation-based modelling,
- System dynamics,
- Long-term change in social systems,
- Social simulation in geographical space,
- Complex urban systems, space syntax, gravity models.
Roman studies are all over network science! In particular the team behind the ‘Finding the limits of the Limes’ project at the VU Amsterdam. They’ve been doing some really cool network analyses of Roman socio-economic and transport networks. Next month they will be hosting a major conference. The program is available on the project website, and it includes a whole session on networks. A few seats are still available so don’t hesitate to sign up and attend.
Where? VU Amsterdam
When? 26-27 January 2017
Thursday 26 Jan 2017, 09:30 – 17:30
Welcome and opening lectures
Nico Roymans (Vrije Universiteit Amsterdam): Setting the scene: characterising Batavian society at the edge of empire in the Dutch river area
Philip Verhagen (Vrije Universiteit Amsterdam): Modelling the cultural landscape of the Dutch Roman Limes: approach, results and prospects
Session 1: Modelling subsistence economy
Session keynote: Wim Jongman (University of Groningen): What did the Romans ever do for us?
Jamie Joyce (Vrije Universiteit Amsterdam): Simulating the Roman farm
Tilman Baum (University of Basel): Models of Land-use in the Neolithic Pile-Dwellings of the Northwestern Pre-Alpine Forelands (4400-2400 BC)
Antoni Martín i OIiveras (University of Barcelona): The economy of Roman wine. Productive landscapes, archaeological data, quantification and modelling. Case Study Research: “Regio Laeetana-Hispania Citerior Tarraconensis” (1st century BC-3th century AD)
Tyler Franconi (University of Oxford): Cultivating change: Roman agricultural production and soil erosion in the Thames River basin
Maurice de Kleijn (Vrije Universiteit Amsterdam):Simulating land-use for the Lower Rhine-Meuse delta in the Roman period
Eli Weaverdyck (University of California, Berkeley): Farmers and Forts in Moesia Inferior: Modelling agricultural strategies on the Lower Danubian Frontier
Session 2: Modelling demography
Session keynote: Isabelle Séguy (Institut National des Études Démographiques, Paris)
Philip Verhagen (Vrije Universiteit Amsterdam): From population dynamics to settlement patterns. Linking archaeological data to demographic models of the Dutch limes.
Wim De Clercq (University of Ghent): The Disastrous Effects of the Roman Occupation!? Population dynamics and rural development on the fringes of the Roman Empire: theories and models.
Chris Green (University of Oxford): Modelling evidence densities: past population variation or modern structuring affordances? The case of England from the Iron Age to the early medieval period.
Antonin Nüsslein (École Pratique des Hautes Études, Paris): A different vision of ancient settlement dynamics: creation and application of a model of evolution of theAntique habitat of the Plateau Lorrain
Friday 27 Jan 2017 09:30 – 17:30
Session 3: Modelling transport
Session keynote: Dimitrij Mlekuž (University of Ljubljana): The archaeology of movement
Mark Groenhuijzen (Vrije Universiteit Amsterdam): Diverse movement in a dynamic environment: modelling local transport in the Dutch part of the Roman limes
Rowin van Lanen (University of Utrecht/Cultural Heritage Agency of the Netherlands):Shopping for wood during the first millennium AD: modelling Roman and early-medieval long-distance transport routes in the Netherlands using a multi-proxy approach
César Parcero-Oubiña (INCIPIT, Santiago de Compostela): Postdicting Roman Roads in the NW Iberian Peninsula
Katherine Crawford (University of Southampton): Walking Between Gods and Mortals: reconsidering the movement of Roman religious processions
Session 4: Modelling socio-economic networks
Session keynote: Tom Brughmans (University of Konstanz): Network science in Roman studies: the potential and challenges
Mark Groenhuijzen (Vrije Universiteit Amsterdam): Possibilities and challenges in the use of networks to study socio-economic relations in the Dutch part of the Roman limes
Pau de Soto (Universidade Nova de Lisboa): Network analysis to model and analyse Roman transport and mobility
Angelo Castrorao Barba (University of Palermo), Stefano Bertoldi (University of Pisa), Gabriele Castiglia (Pontifical Institute of Christian Archaeology): Multi-scalar approach to long-term dynamics, spatial relations and economic networks of the Roman secondary settlements in Italy: towards a model?
Iza Romanowska and I have spent the last few weeks at Carleton University in Ottawa, Canada, doing some awesome Roman networky boardgame “research” with Shawn Graham. You’ll hear more about this cool work soon. Tomorrow we will give a workshop on simulation and networks for the humanities. If you happen to be in the neighbourhood, swing by! If not, get in touch if you are interested and I will share the workshop tutorials with you.
Carleton University, Ottawa, Macodrum Library Discovery Centre RM 481, 11 – 2
Understanding the complexity of past and present societies is a challenge across the humanities. Simulation and network science provide computational tools for confronting these problems. This workshop will provide a hands-on introduction to two popular techniques, agent based modeling and social network analysis. The workshop has been designed with humanities students in mind, so no prior computer experience required.
The workshop is led by Tom Brughmans and Iza Romanowska of University of Konstanz and the University of Southampton, two of the leading digital archaeologists. Brughmans is co-editor of the recent volume, ‘The Connected Past: Challenges to Network Studies in Archaeology and History‘ published by Oxford University Press. Romanowska edits the scholarly blog ‘Simulating Complexity‘ and is a Fellow of the Software Sustainability Institute where she promotes the use of computational methods in the humanities.
The CAA call for papers and posters is now open until 28 October! The full list of sessions is published here. Among them you will notice a most awesomely appealing title: “Archaeological Networks: Uncertainty, Missing Data, and Statistical Inference”. Fancy nerding out on networks, stats and sampling? Then present a paper in the session Matt Peeples and myself will chair.
Archaeological Networks: Uncertainty, Missing Data, and Statistical Inference
If you want to learn how to use networks in an ABM environment then join this free 2-day workshop. A lot of ABM related topics will be taught, including networks. So sign up! More info below and in this leaflet.
Agent-based modelling (ABM) has taken by storm disciplines from all corners of the scientific spectrum, from ecology to medical research and social sciences and it is becoming increasingly popular in archaeology.Now it is your turn to give it go!Learn how to use the simulation software and explore how this popular complexity science technique can complement your research. This two-day workshop will provide an introduction to ABM using NetLogo – an open-source platform for building agent-based models, which combines user-friendly interface, simple coding language and a vast library of model examples, making it an ideal starting point for entry-level agent-based modellers, as well as a useful prototyping tool for more experienced programmers.For more details see the Workshop leaflet.To secure a place please send an email to i.romanowska at soton.ac.uk<http://soton.ac.uk> expressing your interest and briefly describing your background and the reasons why you want to attend. The event is free of charge, but you need to register to the CAA conference. Please note that places are limited and early applications will be given preference.If you are:
an undergraduate, master or PhD student in archaeology, anthropology, history or a similar subject, an early career researcher, a lecturer, a commercial archaeologists or a heritage specialistand if
● you are interested in computational modelling and simulations, or
● you work on a complex problem which can only be solved by modelling, or
● your supervisor told you to ‘go an learn how to do simulations’, or
● your students seem to be doing some magic with computers and you want to
help them but don’t know the tools, or
● you have once heard of agent-based modelling so you want to check what is
the whole fuss about, then this workshop is for you!What will you learn?
● the theory and practice of agent-based modelling;
● how to create an archaeological simulation;
● basic and intermediate programming skills in NetLogo;
● the modelling process, from finding the right research questions to publishing your groundbreaking results;
● how to make your code better, clearer and faster;
● NetLogo extensions incorporating GIS, network science, and stats.Coding experience is NOT required.
You need to bring your own laptop.
The following round table discussion event (see below) might be of interest to readers of this blog (it is definitely of interest to me!). I believe it will give us an insight into the direction the SNAP:DRGN project (which I blogged about earlier) is heading, and possibly an opportunity to contribute to their brainwave. Although the project focuses on linked open data, networks are definitely among their research interests, and the relation between network science and linked open data can always do with some more discussion. New technologies have a place in our workflows, we just need to find it! Linked open data and networks often accompany each other in project descriptions, but the usefulness of pairing them up beyond a metaphorical use of these new technologies needs more critical discussion. This round table might not necessarily be the place this needs to happen, but we will find a suitable venue for this discussion at some point 🙂
Linking Ancient People, Places, Objects and Texts
a round table discussion
Gabriel Bodard (KCL), Daniel Pett (British Museum), Humphrey Southall (Portsmouth), Charlotte Tupman (KCL); with response by Eleanor Robson (UCL)
18:00, Tuesday, December 2nd, 2014
Anatomy Museum, Strand Building 6th Floor
King’s College London, Strand London WC2R 2LS
As classicists and ancient historians have become increasingly reliant on large online research tools over recent years, it has become ever more imperative to find ways of integrating those tools. Linked Open Data (LOD) has the potential to leverage both the connectivity, accessibility and universal standards of the Web, and the power, structure and semantics of relational data. This potential is being used by several scholars and projects in the area of ancient world and historical studies. The SNAP:DRGN project (snapdrgn.net) is using LOD to bring together many technically varied databases and authorities lists of ancient persons into a single virtual authority file; the Pleiades gazetteer and service projects such as Pelagios and PastPlace are creating open vocabularies for historical places and networks of references to them. Museums and other heritage institutions are at the forefront of work to encode semantic archaeological and material culture data, and projects such as Sharing Ancient Wisdoms (ancientwisdoms.ac.uk) and the Homer Multitext (homermultitext.org) are developing citation protocols and an ontology for relating texts with variants, translations and influences.
The panel will introduce some of these key projects and concepts, and then the audience will be invited to participate in open discussion of the issues and potentials of Linked Ancient World Data.
Network science is becoming more commonly applied in both archaeology and history. But this is not happening without difficulties. Pioneers in both disciplines are now trying to overcome the numerous challenges that still surround their use of network techniques: how to deal with fragmentary data, performing analyses over extremely long time spans, using material data in network science to understand past human behaviour, …. I believe archaeologists and historians should face these challenges together! Through collaboration we might come to a better understanding of the use of network science in our disciplines much faster. In a recently published article in Nouvelles de l’Archéologie, Anna Collar, Fiona Coward, Claire Lemercier and myself show how many of the challenges that archaeologists and historians have identified are actually not discipline-specific: we CAN collaborate to tackle them together. Since this article is in French I wanted to provide an English summary of our argumentation here (written with my co-authors). The full article can be downloaded on Academia or through my bibliography page.
One of the key aspects of historical sources, compared to archaeological sources, is that the former often allow for the identification of past individuals, by name, and by role. This richness of data at the individual level means that network analytical methods can be very powerful in the illumination of past social networks and the details of particular places and times – offering, where the data are good enough, a window onto past social lives and interactions, and allowing the synchronic analysis of social networks at a particular moment in time.
However, the issues most commonly mentioned by historical network analysts also concern problematic and incomplete data. These issues are undeniably more significant for archaeology and history than for contemporary social sciences such as sociology. But we should not overestimate their potential impact. Even sociological research in contemporary populations face similar issues where full data may not be available for a variety of reasons, and although the problems are clearly more fundamental in history and archaeology, this also means that researchers in both disciplines have long been accustomed to dealing with, and developing methods at least partially compensating for, partial and biased datasets. As a result, this may be one important area where archaeology and history can contribute its expertise to other disciplines working with imperfect network data.
In contrast to history, archaeology is much less frequently furnished with such focused evidence. In archaeology, individuals are typically identified indirectly through the material remains they leave behind, and even where they can be identified, they often remain without names or specified roles. Not only is archaeological data typically not ‘individualized’, but it can also rarely be attributed an exact date. Most archaeological data typically has date ranges with differing probabilities attached to them, making the establishment of contemporaneity between entities/potential nodes in networks (e.g. individuals; events; settlements) highly problematic. Because of this, archaeologists have tended to focus on the synchronic study of human behavioural change over the long-term, rather than on the diachronic examination of behaviour and interaction. A further characteristic of archaeological data is that it is also likely to be more strongly geographically grounded. Indeed, the geographical location of archaeological data is often among the few pieces of information archaeologists possess. Finally, network analytical methods in archaeology tend to focus most closely on long-term changes in the everyday lives of past peoples.
Common challenges in archaeology and history
Alongside these differences, there are also a number of common challenges facing archaeology and history, as ultimately both disciplines aim to achieve similar goals relating to understanding past interactions and processes.
The most significant of these common challenges are the fragmentary datasets that often characterize both disciplines; we typically deal with bad samples drawn from populations of unknown size and/or with unknown boundaries, snapshots of the past that are heavily biased by differential preservation and/or observation effects. However we argue that this does not exclude the use network techniques in our disciplines, nor does it limit us to only those research contexts for which high quality datasets are available.
A second issue facing our disciplines is that many methodological and theoretical network approaches have been developed in other disciplines to address particular research themes. As a result, they therefore function according to certain rules and/or have certain specific data requirements that might prevent straightforward applications in our disciplines.
Furthermore, using a network approach to study a past phenomenon necessarily requires a researcher to make a series of decisions about how the parameters of that phenomenon should be represented – for example, what entities to use as nodes and what forms of relationship to model as vertices. Archaeologists and historians familiar with the analytical and visualization techniques used by researchers studying modern phenomena may find many analytical approaches and visualization techniques that are not appropriate or achievable. The past phenomena we are interested in, the kinds of questions our data allows us to ask, and the often very specific parameters of human behaviour assumed by archaeologists and historians for investigating the past, are likely to mean we will ultimately need to develop purpose-made visualization and analysis techniques. At the least we will need to acquire a critical understanding of the various methods available if we are to represent archaeological and historical network data in appropriate ways – and indeed, to ‘read’ such visualizations and analysis results correctly.
Finally, the poor chronological control characteristic to a certain extent of historical and to a much greater extent of archaeological datasets, limits our knowledge regarding the order in which nodes and links in networks became salient and also the degree of contemporaneity between nodes. This is likely to have significant ramifications for the ways in which archaeologists and historians visualize and analyse networks, driving a need to consider ‘fuzzy’ networks, margins of error and probabilistic models, as well as the consideration of complex processes of network change and evolution over time.
Unite! Meeting the challenges together
In the recent surge of network applications in archaeology and history, it would seem that the two disciplines have thus far focused their efforts on the more obvious potential applications which mirror those most common in other disciplines, such as the identification and interpretation of ‘small-world’ network structure or the choice of datasets that are readily envisaged as or translated into network data (e.g. road and river networks). Such analyses have demonstrated the potential of the methods for archaeological and historical datasets; however, we believe that potential applications go far beyond this, and that network approaches hold a wealth of untapped potential for the study of the past. To achieve this potential, we will need to become more critical and more creative in our applications, and explore not simply what network science can offer the study of the past, but also what our disciplines offer in terms of developing that science – firstly to tackle specifically archaeological and historical questions, but ultimately to broaden the scope of the science itself as methodologies specifically developed for use in archaeological and historical contexts are taken up for use in tackling similar questions in other disciplines.
Initiatives like The Connected Past and Historical Network Research offer a platform that would allow for exactly this kind of interaction between network scientists and those applying network science to the study of the past. The challenges individual members were encountering in our own research across archaeology and history encouraged us to consider developing a mutually supportive space in which to share concerns and problems, and to discuss ideas and approaches for moving beyond these.
We suggest that simply bringing people together through conferences, workshops, conference sessions and more informal groupings is key to fostering the dialogue between the disciplines that is so important to move forward applications of network analysis to the study of the past. Talking to each other across traditional disciplinary boundaries is vital in the ongoing development of network perspectives on the past. However, as noted above, at the same time we also need to be more sensitive to the specific demands of our disciplinary goals and our datasets and develop new network methods that suit our disciplines better. The sociological roots of most social network analysis software packages means that these are often designed and engineered to address discipline-specific research concepts that may not be appropriate for archaeology and history. SNA software has generally been created to deal with interactions between people in a modern setting – where the individual answers to questions about interactions can be documented with a degree of accuracy. As such, this software and network methodologies in general will need to be applied with care and ideally even developed from scratch for use with networks comprised of nodes which are words, texts, places or artefacts, for the characteristically fragmentary and poorly chronologically controlled datasets of archaeology and history, and for research that aims to go beyond the structuring of individual networks of contemporary nodes to investigate questions of network evolution and change. While interdisciplinary dialogue is crucial, we will need to be sensitive to the discipline-specific idiosyncracies of our data and to critique rather than adopt wholesale practices used in other fields. In this way, rather than apologizing for the ‘deficiencies’ of our datasets in comparison with those characteristic of other disciplines, we will also be able to make novel contributions to the wider field based on the new questions and challenges the study of the past offers network science.
This fourth blog post in the series discusses process-related issues in archaeological network studies. As I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space, and process. The full paper can be found on Academia.
Many archaeological network studies treat networks as static snapshots. This is at least in part a result of the nature of archaeological data and our inability to observe past processes directly. Graph visualisations and many network analysis techniques further enforce this idea of a static network by exploring structural features of particular networks in isolation. However, the past systems we study were dynamic phenomena and the network approach used to understand these phenomena should reflect their changeable nature. In fact, one could argue that no network is truly static since our assumptions underlying the creation of ties imply flows of resources, which are dynamic processes taking place in a changing network.
Archaeological data often does not have the chronological accuracy to reconstruct an exact sequence of events: which ties and nodes appeared and disappeared in what order? A number of network modelling approaches exist that can help one deal with this issue, including agent based modelling (e.g. Graham 2006), algebraic modelling (e.g. Menze and Ur 2012), and statistical modelling (e.g. Lusher et al. 2013). Underlying all of these modelling approaches are clearly formulated assumptions of what a relationship means and what types of flows it allows for. They therefore require one to explicitly acknowledge the dynamic nature of past processes and the dynamic assumptions underlying the definition of ties.
But which model is best? Many models, representing different hypothetical processes, can be created that could all give rise to the same observed network. Since archaeologists cannot directly observe past processes, and given that our data are incomplete and are merely indirect proxies, how then can we ever claim that one model is more probable than any other? The problem of equifinality (the idea that multiple processes can have the same end result) is equally critical for network analysis as for any other technique in the archaeologist’s toolbox. There are a few ways in which formal network methods can help us address this issue. Firstly, archaeological data (however flawed) used in statistical models can help us to identify very general processes that are more probable than others. Secondly, these models can help us to formally express otherwise ill-defined hypotheses and evaluate their likeliness given certain archaeological assumptions. Thirdly, they might not be able to prove certain processes, but models can definitely be used to negatively test or falsify certain hypotheses, or at least identify which processes are less likely than others (given our current knowledge). In this way, such models serve as experimental laboratories (Premo 2006). One has to acknowledge, however, that some past processes are unknowable given our current techniques and datasets. All archaeological approaches suffer from this disadvantage and network analysis is no exception.
Graham, S. (2006). Networks, agent-based models and the Antonine Itineraries: implications for Roman archaeology. Journal of Mediterranean Archaeology, 19(1), 45–64.
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential Random Graph Models for Social Networks. Cambridge: Cambridge university press.
Menze, B. H., & Ur, J. a. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences of the United States of America, 109(14), E778–87. doi:10.1073/pnas.1115472109
Premo, L. S. (2006). Agent-based models as behavioral laboratories for evolutionary anthropological research. Arizona Anthropologist, 91–113.
This third blog post in the series discusses space-related issues in archaeological network studies. As I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space, and process. The full paper can be found on Academia.
The definition of nodes is not only dependent on data type categorisation but also necessarily reflects the research questions being asked, revealing an issue of spatial scales. Do the past processes we are interested in concern interactions between regions, sites or individuals? How will this be represented in node, tie and network definitions? The ability of network approaches to work on multiple scales is often mentioned as one of the advantages of using formal network methods (Knappett 2011). In practice, however, archaeological network analysts have traditionally focused on inter-regional or macro-scales of analysis. Knappett (2011) argues that it is on the macro-scale that network analysis comes into its own and a recently published edited volume reveals this regional emphasis (Knappett 2013). This insistence to work on large scales becomes quite unique in light of social network analysts’ traditional focus on individual social entities in interaction. SNA provides a multitude of good examples of how network methods could be usefully applied on a micro- or local scale of analysis (e.g. ego-networks). However, the nature of archaeological data, which rarely allows for individuals and their interactions to be identified with any certainty, should not be considered the only reason for this focus on the macro-scale. Arguably, networks lend themselves very well to exploring inter-regional interaction, and archaeologists have always had a particular interest in the movements and flows of people, resources and information across large areas. Moreover, many of the early applications of network methods in archaeology, which in some cases might have served as an example to more recent applications, concerned inter-regional interaction (e.g. Terrell 1976). One should acknowledge the importance of exploring how local actions give rise to larger-scale patterns if we are to benefit from the multi-scalar advantage of formal network methods (Knappett 2011).
It is not surprising that many archaeological network analysts are interested in exploring the dynamics between relational and geographical space (e.g. Bevan and Wilson 2013; Knappett et al. 2008; Menze and Ur 2012; Wernke 2012), given the importance of spatial factors in understanding archaeological data and archaeologists’ traditional interest in geographical methods (e.g. Hodder and Orton 1976). Despite early work by archaeologists on geographical networks (for an overview see chapter 2 in Knappett 2011), geographical space has been almost completely ignored by sociologists and physicists, resulting in a very limited geographical network analysis toolset for archaeologists to draw on (although see a recent special issue of the journal Social Networks [issue 34(1), 2012] and the review work by Barthélemy , as well as techniques used in Space Syntax [Hillier and Hanson, 1984]).
Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1-3), 1–101. doi:10.1016/j.physrep.2010.11.002
Bevan, A., & Wilson, A. (2013). Models of settlement hierarchy based on partial evidence. Journal of Archaeological Science, 40(5), 2415–2427.
Hillier, B., & Hanson, J. (1984). The social logic of space. Cambridge: Cambridge University Press.
Hodder, I., & Orton, C. (1976). Spatial analysis in archaeology. Cambridge: Cambridge University Press.
Knappett, C. (2011). An archaeology of interaction: network perspectives on material culture and society. Oxford: Oxford University Press.
Knappett, C. (2013). Introduction: why networks? In C. Knappett (Ed.), Network analysis in archaeology. New approaches to regional interaction (pp. 3–16). Oxford: Oxford University Press.
Knappett, C., Evans, T., & Rivers, R. (2008). Modelling maritime interaction in the Aegean Bronze Age. Antiquity, 82(318), 1009–1024.
Menze, B. H., & Ur, J. a. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences of the United States of America, 109(14), E778–87. doi:10.1073/pnas.1115472109
Terrell, J. E. (1976). Island biogeography and man in Melanesia. Archaeology and physical anthropology in Oceania, 11(1), 1–17.
Wernke, S. a. (2012). Spatial network analysis of a terminal prehispanic and early colonial settlement in highland Peru. Journal of Archaeological Science, 39(4), 1111–1122. doi:10.1016/j.jas.2011.12.014