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Modelling Temporal Uncertainty in Archaeological Analysis

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Abstract

This paper discusses the problem of temporal uncertainty in archaeological analysis and how it affects archaeological interpretation. A probabilistic method is proposed as a potential solution for modelling and quantifying time when high levels of uncertainty restricts temporal knowledge and scientific datings are unavailable, while Monte Carlo simulation is suggested as a means to formally integrate such knowledge into actual analysis. A case study focusing on counts of prehistoric hunter–gatherer pithouses in Mid-Holocene Japan provides an example of how uncertainty can be problematic and bias the results of the most straightforward archaeological analysis and how the coupling of a probabilistic and simulation-based approach nonetheless offers a useful solution. The discussion that follows also addresses the need for more robust and quantifiable ways to illustrate the chronological flow of our archaeological narratives.

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Notes

  1. In case a single pithouse is known to have been re-occupied multiple times, each episode of occupation should be treated as a single independent event. One potential problem of this approach is when the lack of data cannot distinguish episodes of multiple occupations from a simple case of large time span (higher temporal uncertainty). This suggests that, when available, multiple sources of evidence (other than the recovered pottery sherd) should be included in the analysis.

  2. Notice that this will also determine the probability of counts for t 2. Since mutual exclusiveness is given as a starting assumption, the non-existence of the event b at t 2 will also indicate its existence at t 3.

  3. Incremental runs of 500, 750 and 1,000 has shown no substantial difference in the outputs, indicating how 1,000 runs are sufficient for the present dataset.

  4. The rate of change is given as the number of pithouses at t i+1 minus the number of pithouses t i , divided by the temporal resolution φ. An alternative approach is to divide the difference with the number of pithouses at t i , this will be a slightly different measure corresponding to the per capita rate of growth. Discussions on the differences between the two methods are out of the scope of the paper, but it could be stated that the former approach provides a more generic method, while the latter is more suited for evaluating population dynamics. In the present case study, the pithouse count does not necessarily correlates to the population dynamics in an equal manner through different time-blocks, as variability in the residential mobility could be expected. Hence, the latter method has been regarded as less suitable in this case.

  5. Notice how the labelling of the transition refers to the initial dates of each time-block. Thus a transition between 3000 and 2900 cal BP actually refers to the rate of change between 3000–2900 and 2900–2800 cal BP.

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Acknowledgments

I am grateful to Andrew Bevan and Mark Lake for constantly supporting my work, with useful suggestions and comments on different versions of the manuscript. Eva Jobbova also commented an early version of the manuscript pointing out what was necessary to enhance its clarity. Fujio Kumon has kindly provided the raw data for Fig. 11b. Special thanks go to the three anonymous reviewers for their insightful and supporting comments and critiques on many aspects of the paper. The Graduate School Research Scholarship of UCL has supported the project financially. Any errors and inconsistencies remain my own.

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    Enrico R. Crema

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Crema, E.R. Modelling Temporal Uncertainty in Archaeological Analysis. J Archaeol Method Theory 19, 440–461 (2012). https://doi.org/10.1007/s10816-011-9122-3

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