Utilizing Complex Systems Statistics for Historical and Archaeological Data


  • Justin E. Lane University of Oxford, Masaryk University, Boston University
  • Michael J. Gantley University of Oxford




Archaeology, networks, generalized linear modeling, complex systems, data analysis


This article examines two statistical tools useful for historians and archaeologists that are common in other fields, but rare in cognitive approaches to historical materials. These tools, network statistics and general linear modelling, have been utilized for decades in other disciplines in the cognitive sciences to test for complex and dynamic relationships between different variables. One of the strengths of these two approaches is that they can be used to draw statistical inference from complex, multivariate data; even when data is incomplete. This article outlines how these analyses work and when these approaches can be appropriately used to analyse historical and archaeological data.

Author Biographies

  • Justin E. Lane, University of Oxford, Masaryk University, Boston University

    Justin E. Lane is a DPhil Researcher at the Institute for Cognitive and Evolutionary Anthropology, University of Oxford; Research associate at LEVYNA, Masaryk University; and the Post-doctoral Researcher in Modeling and Simulation at the Center for Mind and Culture, Boston University, USA.

  • Michael J. Gantley, University of Oxford

    Michael J. Gantley was a DPhil Researcher at the School of Archaeology and Institute for Cognitive and Evolutionary Anthropology, University of Oxford. He is currently a Postdoctoral Researcher at the College of Life and Environmental Sciences, University of Exeter, UK.


Agresti, A. 2007. An Introduction to Categorical Data Analysis. Hoboken, NJ: Wiley-Interscience. https://doi.org/10.1002/0470114754

Aiden, E., and J.-B. Michel. 2014. Uncharted: Big-Data as a Lens on Human Culture. New York: Riverhead Books.

Ammerman, A. I., and L. L. Cavalli-Sforza. 1984. The Neolithic Transition and the Genetics of Populations in Europe. Princeton, NJ: Princeton University Press. https://doi.org/10.1515/9781400853113

Axtell, R. L., J. M. Epstein, J. S. Dean, G. J. Gumerman, A. C. Swedlund, J. Harburger, M. Parker. 2006. “Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long House Valley”. In Generative Social Science: Studies in Agent-Based Computational Modeling, ed. J. M. Epstein, 117–29. Princeton, NJ: Princeton University Press.

Bastian, M., S. Heymann and M. Jacomy. 2009. “Gephi: An Open Source Software for Exploring and Manipulating Networks”. In International AAAI Conference on Weblogs and Social Media. gephi.org. Retrieved from http://gephi.github.io/

Baxter, M. J. 1994. Exploratory Multivariate Analysis in Archaeology. Edinburgh: Edinburgh University Press.

Beerenwinkel, N., T. Sing, T. Lengauer, J. Rahnenführer, K. Roomp, I. Savenkov, M. Däumer. 2005. “Computational Methods for the Design of Effective Therapies against Drug Resistant HIV Strains”. Bioinformatics 21(21): 3943–950. Retrieved from http://bioinformatics.oxfordjournals.org/content/21/21/3943.short

Bellwood, P. 2004. First Farmers: The Origins of Agricultural Societies. Malden, MA: Wiley-Blackwell.

Bewick, V., L. Cheek and J. Ball. 2004. “Statistics Review 13: Receiver Operating Characteristic Curves”. Critical Care 8: 508–12. https://doi.org/10.1186/cc3000

Blondel, V. D., J.-L. Guillaume, R.Lambiotte and E. Lefebvre. 2008. “Fast Unfolding of Communities in Large Networks”. Journal of Statistical Mechanics: Theory and Experiment 10008(10): 6. https://doi.org/10.1088/1742-5468/2008/10/P10008

Bourne, C. M., P. M. Regular, B. Sun, S. P. Thompson, A. J.Trant and J. A. Wheeler 2007. Generalized Linear Model Analysis in Ecology. St. Johns, Newfoundland. Retrieved from http://www.faculty.mun.ca/dschneider/b7932/B7932Final15Dec2007.pdf

Brughmans, T. 2013. “Thinking through Networks: A Review of Formal Network Methods in Archaeology”. Journal of Archaeological Method and Theory 20(4): 623–62. https://doi.org/10.1007/s10816-012-9133-8

Carley, K. M. 2013. ORA NetScenes. Pittsburgh, PA: Center for Computational Analysis of Social and Organizational Systems (CASOS). Retrieved from http://www.casos.cs.cmu.edu/projects/ora/software.php

Carley, K. M., and D. S. Kaufer. 1993. “Semantic Connectivity : An Approach for Analyzing Symbols in Semantic Networks”. Communication Theory 3(3): 183–213. https://doi.org/10.1111/j.1468-2885.1993.tb00070.x

Carley, K. M., J. Pfeffer, J. Reminga, J.Storrick and D. Columbus. 2012. ORA User’s Guide 2012. Pittsburgh, PA. Retrieved from http://www.casos.cs.cmu.edu/publications/papers/CMU-ISR-13-108.pdf

Collar, A. 2013a. “Re-thinking Jewish Ethnicity through Social Network Analysis”. In Network Analysis in Archaeology: New Approaches to Regional Integration, ed. C. Knappett, 223–46. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199697090.003.0010

—2013b. Religious Networks in the Roman Empire the Spread of New Ideas. Cambridge: Cambridge University Press.

Collar, A., F. Coward, T. Brughmans and B. J. Mills. 2015. “Networks in Archaeology: Phenomena, Abstraction, Representation”. Journal of Archaeological Method and Theory 22: 1–32. https://doi.org/10.1007/s10816-014-9235-6

Coward, F. 2010. “Small Worlds, Material Culture and Near Eastern Social Networks”. Proceedings of the British Academy 158: 449–79. Retrieved from http://www.fcoward.co.uk/Cowardsmallworlds.pdf

Dean, J. S., G. J. Gumerman, J. M. Epstein, R. L. Axtell, A. C. Swedlund, M. T.Parker and S. McCarroll. 2006. “Understanding Anasazi Culture Change Through Agent-Based Modeling”. In Generative Social Science: Studies in Agent-Based Computational Modeling, ed. J. M. Epstein, 90–116. Princeton, NJ: Princeton University Press.

Diallo, S. Y., C. J. Lynch, R. Gore and J. J. Padilla. 2016. “Identifying Key Papers within a Journal via Network Centrality Measures”. Scientometrics 107(3): 1005–20. https://doi.org/10.1007/s11192-016-1891-8

Diesner, J., and K. M. Carley. 2004. Using Network Text Analysis to Detect the Organizational Structure of Covert Networks. In Proceedings of the North American Association for Computational Social and Organizational Science (NAACSOS). Pittsburgh, PA.

Dobson, A. J. 2002. An Introduction to Generalized Linear Models. London: Chapman and Hall/CRC.

Dunbar, R. I. M., C. Gamble and J. A. Gowlett, eds. 2014. Lucy to Language: The Benchmark Papers. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:osobl/9780199652594.001.0001

Epstein, J. M., ed. 2006. Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity). Princeton, NJ: Princeton University Press.

Fawcett, T. 2006. “An Introduction to ROC Analysis”. Pattern Recognition Letters 27: 861–74. https://doi.org/doi:10.1016/j.patrec.2005.10.010

Firth, D. 1991. “Generalized Linear Models”. In Statistical Theory and Modelling, ed. D. V. Hinkley, N. Reid and E. J. Snell. London: Chapman and Hall.

Fox, J. 2008. Applied Regression Analysis and Generalized Linear Models. London: Sage Publications.

Freedman, D., R.Pisani and R. Purves. 2007. Statistics (4th edn). New York: W.W. Norton.

Gantley, M. 2015. The Rites of Spring: A Cognitive Analysis of Ritual Activity in the Agricultural Transition in South-west Asia and North-western Europe. Oxford: University of Oxford.

Granovetter, M. S. 1983. “The Strength of Weak Ties: A Network Theory Revisited”. Sociological Theory 1: 201–33. https://doi.org/10.2307/202051

Gray, R. D., Q. D. Atkinson and S. J. Greenhill. 2011. “Language Evolution and Human History: What a Difference a Date Makes”. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 366(1567): 1090–100. https://doi.org/10.1098/rstb.2010.0378

Guisan, A., T. C. Edwards and T. Hastie. 2002. “Generalized Linear and Generalized Additive Models in Studies of Species Distributions: Setting the Scene”. Ecological Modelling 157: 89–100. https://doi.org/10.1016/S0304-3800(02)00204-1

Gumerman, G. J., A. C. Swedlund, J. S. Dean and J. M. Epstein. 2006. “The Evolution of Social Behavior in the Prehistoric American Southwest”. In Generative Social Science: Studies in Agent-Based Computational Modeling, ed. J. M. Epstein, 130–43. Princeton, NJ: Princeton University Press.

Hanley, J. A., and B. J. McNeil. 1982. “The Meaning and Use of the Area Under the Receiver Operating Characteristic (ROC) Curve”. Radiology 143: 29–36. https://doi.org/10.1148/radiology.143.1.7063747

Hanneke, S., and E. P. Xing, 2009. “Network Completion and Survey Sampling”. Aistats 5: 209–215. Retrieved from http://jmlr.org/proceedings/papers/v5/hanneke09a/hanneke09a.pdf

Human Relations Area Files. 2015. Retrieved from http://hraf.yale.edu/

Jackson, M. O. 2008. Social and Economic Networks. Princeton, NJ: Princeton University Press.

Johnson, B. D. 2010. “Multilevel Analysis in the Study of Crime and Justice”. In Handbook of Quantitative Criminology, ed. A. R. Piquero and D. Weisburd, 615–48. New York and London: Springer. https://doi.org/10.1007/978-0-387-77650-7_30

Johnson, D. D. P. 2005. “God’s Punishment and Public Goods: A Test of the Supernatural Punishment Hypothesis in 186 World Cultures”. Human Nature 16(4): 410–46. https://doi.org/10.1007/s12110-005-1017-0

Johnson, N. P. 2004. “Advantages to Transforming the Receiver Operating Characteristic (ROC) Curve into Likelihood Ratio Co-ordinates”. Statistics in Medicine 23: 2257–266. https://doi.org/10.1002/sim.1835

Jong-Hwan, Y. 2007. Introducing the Generalized Linear Models. Retrieved from https://www.scribd.com/document/312469651/Introducing-the-Generalized-Linear-Models-24-408-424-Jonghwan-yoo

Kim, M., and J. Leskovec, 2011. “The Network Completion Problem: Inferring Missing Nodes and Edges in Networks”. SIAM International Conference on Data Mining, 47–58. https://doi.org/10.1137/1.9781611972818.5

Lane, J. E. 2015a. Github: Create Network Platform. Retrieved from https://github.com/cogijl/CreateNetworkPlatform

—2015b. “Semantic Network Mapping of Religious Material”. Journal for Cognitive Processing 16(4): 333–41. https://doi.org/10.1007/s10339-015-0649-1

Leskovec, J., K. J. Lang and M. Mahoney. 2010. “Empirical Comparison of Algorithms for Network Community Detection”. In Proceedings of the 19th International World Wide Web, 631–40. Raleigh, NC: International World Wide Web Conference Comittee. https://doi.org/10.1145/1772690.1772755

Martin, L. H. 2013. “Cognitive Science of Religion and the History of Religions (in Graeco-Roman Antiquity)”. Journal of Cognitive Historiography 1(1): 10–13.

Mason, S. J., and N. E. Graham. 2002. “Areas Beneath the Relative Operating Characteristics (ROC) and Relative Operating Levels (ROL) Curves: Statistical Significance and Interpretation”. Quarterly Journal of the Royal Meteorological Society 128(584): 2145–166. https://doi.org/10.1256/003590002320603584

Matthews, L. J., J. J. Tehrani, F. M. Jordan, M. Collard and C. L. Nunn. 2011. “Testing for Divergent Transmission Histories among Cultural Characters: a Study using Bayesian Phylogenetic Methods and Iranian Tribal Textile Data”. PloS One 6(4): e14810. https://doi.org/10.1371/journal.pone.0014810

McCullagh, P. 1984. “Generalized Linear Models”. European Journal of Operational Research 16: 285–92. https://doi.org/10.1016/0377-2217(84)90282-0

McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models. London: Chapman and Hall. https://doi.org/10.1007/978-1-4899-3242-6

McCulloch, C. E. 1997. “Maximum Likelihood Algorithms for Generalized Linear Mixed Models”. Journal of the American Statistical Association 92(437): 162–70. Retrieved from http://www.jstor.org/stable/2291460

McCulloh, I., and K. M. Carley. 2011. “Detecting Change in Longitudinal Social Networks”. Journal of Social Structure 12: 1–37. Retrieved from https://tinyurl.com/oai-ADA550790

McCulloh, I., B. Ring, T. L.Frantz and K. M. Carley. 2008. “Unobtrusive Social Network Data from Email”. In Proceedings of the 26th Army Science Conference. Orlando, FL. Retrieved from http://www.casos.cs.cmu.edu/publications/papers/Mcculloh-email.pdf

Metz, C. E. 1978. “Basic Principles of ROC Analysis”. Seminars in Nuclear Medicine 8(4): 283–98. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/112681

Nelder, J. A., and R. W. M. Wedderburn, 1972. “Generalized Linear Models”. Journal of the Royal Statistical Society. Series A 135: 370–84. https://doi.org/10.2307/2344614

Newman, M. E. J. 2006a. “Finding Community Structure in Networks Using the Eigenvectors of Matrices”. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 74(3): 1–19. https://doi.org/10.1103/PhysRevE.74.036104

Newman, M. E. J. 2006b. “Modularity and Community Structure in Networks”. Proceedings of the National Academy of Sciences of the United States of America 103(23): 8577–82. https://doi.org/10.1073/pnas.0601602103

Newman, M. E. J., and M. Girvan. 2004. “Finding and Evaluating Community Structure in Networks”. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 69: 1–15. https://doi.org/10.1103/PhysRevE.69.026113

Norenzayan, A. 2013. Big Gods: How Religion Transformed Cooperation and Conflict. Princeton, NJ: Princeton University Press.

Padgett, J. F., and C. K. Ansell. 1993. “Robust Action and the Rise of the Medici, 1400–1434”. American Journal of Sociology 98(6): 1259. https://doi.org/10.1086/230190

Papadimitriou, A., P. Symeonidis, and Y. Manolopoulos. 2012. “Fast and Accurate Link Prediction in Social Networking Systems”. Journal of Systems and Software 85(9): 2119–132. https://doi.org/10.1016/j.jss.2012.04.019

Peoples, H. C., and F. W. Marlowe. 2012. “Subsistence and the Evolution of Religion”. Human Nature 23(3): 253–69. https://doi.org/10.1007/s12110-012-9148-6

Python Software Foundation. 2015. Welcome to Python.org. Retrieved from https://www.python.org/

R Core Team. 2013. R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from http://www.r-project.org/

Roes, F. L., and M. Raymond. 2003. “Belief in Moralizing Gods”. Evolution and Human Behavior 24(2): 126–35. https://doi.org/10.1016/S1090-5138(02)00134-4

Seshat Databank. 2017. Seshat: Global History Databank. Retrieved from http://seshatdatabank.info/

Shariff, A. F., A. Norenzayan and J. Henrich. 2011. “The Birth of High Gods: How the Cultural Evolution of Supernatural Policing Influenced the Emergence of Complex, Cooperative Human Societies, Paving the Way for Civilization”. In Evolution, Culture, and the Human Mind , ed. M. Shaller, A. Norenzayan, S. J. Heine, T. Yamagishi and T. Kameda, 119–36. New York: Psychology Press. https://doi.org/10.4324/9780203848746

Sing, T., O. Sander, N.Beerenwinkel and T. Lengauer. 2005. “ROCR: Visualizing Classifier Performance in R”. Bioinformatics 21(20): 3940–941. https://doi.org/10.1093/bioinformatics/bti623

Slingerland, E. 2014. “Toward a Second Wave of Consilience in the Cognitive Scientific Study of Religion”. Journal of Cognitive Historiography 1(1): 121–30. https://doi.org/10.1558/jch.v1i1.121

Slingerland, E., R. Nichols, K. L. Nielbo and C. Logan. 2017. “The Distant Reading of Religious Texts: A ‘Big Data’ Approach to Mind-Body Concepts in Early China”. Journal of the American Academy of Religion (lfw090): 1–31. https://doi.org/10.1093/jaarel/lfw090

Sørensen, L., and S. Karg. 2014. “The Expansion of Agrarian Societies towards the North – New Evidence for Agriculture during the Mesolithic/Neolithic Transition in Southern Scandinavia”. Journal of Archaeological Science 51: 98–114. https://doi.org/10.1016/j.jas.2012.08.042

Sujun, L., L. Boshu, Z. Rong, C. Yudong and L. Yixue. 2006. “Predicting O-glycosylation Sites in Mammalian Proteins by Using SVMs”. Computational Biology and Chemistry 30: 203–208. https://doi.org/10.1016/j.compbiolchem.2006.02.002

Symeonidis, P., N. Iakovidou, N. Mantas and Y. Manolopoulos. 2013. “From Biological to Social Networks: Link Prediction Based on Multi-way Spectral Clustering”. Data & Knowledge Engineering 87: 226–42. https://doi.org/http://dx.doi.org/10.1016/j.datak.2013.05.008

Tatem, A., and D. Rogers. 2006. “Global Transport Networks and Infectious Disease Spread”. Advances in Parasitology 62: 293–343. https://doi.org/10.1016/S0065-308X(05)62009-X.Global

The Database of Religious History. 2015. The Database of Religious History. Retrieved, from http://www.religiondatabase.arts.ubc.ca/

The R Foundation. 2015. The R Project for Statistical Computing. Retrieved from http://www.r-project.org/

Travers, J., and S. Milgram. 1969. “An Experimental Study of the Small World Problem”. Sociometry 32(4): 425–43. Retrieved from http://www.jstor.org/stable/2786545

Venables, W. N., and C. M. Dichmont. 2004. “GLMs, GAMs and GLMMs: An Overview of Theory for Applications in Fisheries Research”. Fisheries Research 70(2–3): 319–37. https://doi.org/10.1016/j.fishres.2004.08.011

Watts, J., S. J. Greenhill, Q. D. Atkinson, T. E. Currie, J. Bulbulia and R. D. Gray. 2015. “Broad Supernatural Punishment but Not Moralizing High Gods Precede the Evolution of Political Complexity in Austronesia”. Proceedings of the Royal Society B 282: 1–7. https://doi.org/10.1098/rspb.2014.2556

Whitehouse, H., K. Kahn, M. E. Hochberg and J. J. Bryson. 2012. “The Role for Simulations in Theory Construction for the Social Sciences: Case Studies Concerning Divergent Modes of Religiosity”. Religion, Brain & Behavior 2(3): 182–201. https://doi.org/10.1080/2153599X.2012.691033

Whittle, A. 1996. Europe in the Neolithic: The Creation of New World (2nd Edition). Cambridge: Cambridge University Press.

Wilensky, U. 1999. Netlogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling. Retrieved from http://ccl.northwestern.edu/netlogo/

Wolfram, S. 2010. A New Kind of Science. Champaign, IL: Wolfram Media.

World Values Survey. 2015. WVS Database. Retrieved from http://www.worldvaluessurvey.org/wvs.jsp





Digital Humanities, Cognitive Historiography and the Study of Religion

How to Cite

Lane, J. E., & Gantley, M. J. (2018). Utilizing Complex Systems Statistics for Historical and Archaeological Data. Journal of Cognitive Historiography, 3(1-2), 68-92. https://doi.org/10.1558/jch.31696