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Bringing archaeology into the information age: entropy, noise, channel capacity, and information potential in archaeological significance assessments


Most archaeology today is conducted by private cultural resources management (CRM) firms in compliance with preservation legislation. Industry archaeologists make decisions that affect the expenditure of public and private development funds, and determine what resources, data, and information are available for the future. Decisions about what is archaeologically significant impact public value purchased by governments and firms, and impact cumulative ability to know the archaeological record. While there are many concepts of archaeological significance, many justify significance in terms of the information yield. Few define information. Discussions are confused by vague or absent definitions of information, and the conflation of data volume with information resulting in bias in the types of resources preserved. Perpetuating modern theoretical interests into the preserved data hobbles future archaeology. In the interest of representative preservation of archaeological information, we need an operational definition of “information” and which resources have information potential. To unify and organize discussions of information and representative samples, I turn to information theory. Claude E. Shannon provides a formal definition of “information”. Applying Shannon’s concepts of entropy and equivocation provides formal tools to objectively assess relative and absolute information potential, and can force CRM practitioners to more explicitly justify their recommendations for expenditure of public and private development funds and preserve a more representative sample of the archaeological record for future inquiry. Bringing archaeology into the information age is a practical solution to many problems with CRM significance evaluations, and will better justify the value CRM provides in return for public investment in archaeology.

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Fig. 1

Data from Briuer and Mathers (1996: Appendix II)


  1. 1.

    There are exceptions, significance is messy and there is no one-to-one match with the NRHP criteria.

  2. 2.

    There is debate in the information, statistical mechanics, and physics literature as to whether Shannon’s entropy is thermodynamic entropy. Everyone acknowledges similarities in properties, but not everyone admits identity. Henceforth, my use of “entropy” refers to “Shannon entropy” or Shannon’s definition of “information”, unless otherwise specified.

  3. 3.

    Quantitative assessment of equivocation would require a defined symbol set for discrete observations and the occupations we are reconstructing. The symbol set of discrete observations would entail an explicit artifact classification and some type of classification of the spatial arrangement(s). Traditional units of artifact types, phases, site types, etc. are not sufficient to the task, though they can be used in an informal way in initial applications of the proposed model. Rafferty (2012) and Rafferty et al. (2011) provide an example of a way to create such an alphabet for occupations from observational units. Only after defining the number of possible classes in the channel and for the receiver can we begin to calculate conditional entropy. However, we can use the proposed model to frame the relative conditional entropies of different patterns of survey results.

  4. 4.

    An anonymous reviewer noted that there are many methods of investigation into formation processes that can account for and mitigate the increase in entropy and that my characterization of the increase in equivocation as “irretrievable” was perhaps too strong. I acknowledge the reviewer’s point. Indeed there are well tested methods and theories for assessing formation processes, in effect filtering the noise from the signal. However, any information lost is irretrievable, and there is always some that is irretrievable. The reviewer’s comment speaks to the discussion of error correction, noise reduction, and sampling decisions given a specified set of noise for a specific period (Nj). I did not mean to imply there is no way to filter noise and correct errors in the archaeological record. The ability of established methods to mitigate formation processes is factored into discussions of assessing channel capacity, and the methods referenced by the reviewer are what I had in mind in that discussion.

  5. 5.

    This would also include a version of intersymbol interference (Hartley 1928: 544). In archaeology, there is also intersource interference which is the Nj introduced by a different Xi occurring in the same place at a different time i. Being recipients, we cannot take Hartley’s design solution guidance; however, we can be aware of this as a practical limit in the approximation of HYs(Δj) to H(Δj).


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I am grateful to many people who have tolerated me talking about information, entropy, and equivocation over the past several years as this idea developed. I am especially grateful to Dr. Evan Peacock for always being willing to discuss CRM, significance, and classification issues. Evan and Dr. Janet Rafferty’s work inspired me to think about sites, surveys, and significance more critically and ultimately led me to this paper. The initial exchange of ideas with Janet and Evan came from a Wenner-Gren funded symposium held at Mississippi State in 2012. I am also grateful to Josh Donaldson whose paper for one of my classes reviewing significance and classification was a resource useful in building my bibliography. This idea began while listening to the audiobook version of James Gleick’s (2011) The Information: A History, a Theory, a Flood while commuter cycling to and from work in 2013. I started assembling the application of entropy to significance assessments in 2014 and 2015, and then reached the limits of my own understanding and thinking on entropy. Then, I encountered Dr. Benjamin Schumacher’s Great Courses lectures (also consumed while cycling) which provided a structure for my deeper engagement with information theory. Through brief correspondence, he also provided guidance to the literature. I’m grateful he bothered to return my emails and provide input as I started my deep(er) dive into foreign fields of study. Two anonymous reviewers provided welcome constructive criticism of the argument and the manner of presentation which have improved my ability to communicate my ideas. Finally, I must acknowledge the support of Ball State University and particularly the Department of Anthropology and the College of Sciences and Humanities for providing funding for my special assigned leave (a.k.a. sabbatical) to have the time to dig into the entropy literature and fully develop this idea that has be gestating for over 6 years. Any shortcomings and errors (interpretive or otherwise) are mine alone.

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Nolan, K.C. Bringing archaeology into the information age: entropy, noise, channel capacity, and information potential in archaeological significance assessments. Qual Quant 54, 1171–1196 (2020).

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  • Significance
  • Information potential
  • Entropy
  • Equivocation
  • Principle of representativeness
  • CRM
  • Value
  • Archaeology