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A theoretically-sufficient and computationally-practical technique for deterministic frequency seriation.

Lipo CP, Madsen ME, Dunnell RC - PLoS ONE (2015)

Bottom Line: This compression removes the ability to use all of the features of our data to fit to the seriation model, obscuring violations of the model, and thus lessens our ability to understand the degree to which the resulting order is chronological, spatial, or a mixture.Our initial implementation of IDSS does not solve all the problems of seriation, but begins to moves towards a resolution of a long-standing problem in archaeology while opening up new avenues of research into the study of cultural relatedness.We demonstrate the utility of IDSS using late prehistoric decorated ceramics from the Mississippi River Valley.

View Article: PubMed Central - PubMed

Affiliation: Department of Anthropology and IIRMES, California State University Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, USA.

ABSTRACT
Frequency seriation played a key role in the formation of archaeology as a discipline due to its ability to generate chronologies. Interest in its utility for exploring issues of contemporary interest beyond chronology, however, has been limited. This limitation is partly due to a lack of quantitative algorithms that can be used to build deterministic seriation solutions. When the number of assemblages becomes greater than just a handful, the resources required for evaluation of possible permutations easily outstrips available computing capacity. On the other hand, probabilistic approaches to creating seriations offer a computationally manageable alternative but rely upon a compressed description of the data to order assemblages. This compression removes the ability to use all of the features of our data to fit to the seriation model, obscuring violations of the model, and thus lessens our ability to understand the degree to which the resulting order is chronological, spatial, or a mixture. Recently, frequency seriation has been reconceived as a general method for studying the structure of cultural transmission through time and across space. The use of an evolution-based framework renews the potential for seriation but also calls for a computationally feasible algorithm that is capable of producing solutions under varying configurations, without manual trial and error fitting. Here, we introduce the Iterative Deterministic Seriation Solution (IDSS) for constructing frequency seriations, an algorithm that dramatically constrains the search for potential valid orders of assemblages. Our initial implementation of IDSS does not solve all the problems of seriation, but begins to moves towards a resolution of a long-standing problem in archaeology while opening up new avenues of research into the study of cultural relatedness. We demonstrate the utility of IDSS using late prehistoric decorated ceramics from the Mississippi River Valley. The results compare favorably to previous analyses but add new details into the structure of cultural transmission of these late prehistoric populations.

No MeSH data available.


Related in: MedlinePlus

The spatial distribution of the edges of graph shown in Fig 12 and the spatial groups of assemblages. The groups outlines represent the branches of the “minmax” graph depicted in Fig 12.Note that the edges have a strong spatial pattern in that assemblages next to each other are more likely to be paired within seriation solutions than those assemblages that are farther away. A bootstrap assessment of the significance of this spatial pattern shows that p = 0.04. The color of each spatial group corresponds to the major branches in the “minmax” graph in Fig 12.
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pone.0124942.g013: The spatial distribution of the edges of graph shown in Fig 12 and the spatial groups of assemblages. The groups outlines represent the branches of the “minmax” graph depicted in Fig 12.Note that the edges have a strong spatial pattern in that assemblages next to each other are more likely to be paired within seriation solutions than those assemblages that are farther away. A bootstrap assessment of the significance of this spatial pattern shows that p = 0.04. The color of each spatial group corresponds to the major branches in the “minmax” graph in Fig 12.

Mentions: Using the IDSS analysis we can systematically examine the full set of possible frequency seriation solutions (Figs 11–13). Despite the large number of possible solutions (N = 2.56 × 1018), iteratively finding the set of 97 possible solutions required less than two seconds of processing due to the fact that the largest possible seriations were composed of only 4 assemblages. No larger sets can be built without introducing violations of unimodality, so the algorithm did not need to continue its search and terminated. Using a confidence interval of α = 0.05 allowed us to generate a solution that included all assemblages. Fig 11 clearly shows how the traditional linear ordering breaks down as a visualization mechanism, especially in the presence of many valid solutions. A number of assemblages participate in multiple solutions, and it is impossible to get a sense of the overall nature of the solutions when confronted with many separate orderings. Thus, as described above, we focus here on the graph representation of results (Fig 12).


A theoretically-sufficient and computationally-practical technique for deterministic frequency seriation.

Lipo CP, Madsen ME, Dunnell RC - PLoS ONE (2015)

The spatial distribution of the edges of graph shown in Fig 12 and the spatial groups of assemblages. The groups outlines represent the branches of the “minmax” graph depicted in Fig 12.Note that the edges have a strong spatial pattern in that assemblages next to each other are more likely to be paired within seriation solutions than those assemblages that are farther away. A bootstrap assessment of the significance of this spatial pattern shows that p = 0.04. The color of each spatial group corresponds to the major branches in the “minmax” graph in Fig 12.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414518&req=5

pone.0124942.g013: The spatial distribution of the edges of graph shown in Fig 12 and the spatial groups of assemblages. The groups outlines represent the branches of the “minmax” graph depicted in Fig 12.Note that the edges have a strong spatial pattern in that assemblages next to each other are more likely to be paired within seriation solutions than those assemblages that are farther away. A bootstrap assessment of the significance of this spatial pattern shows that p = 0.04. The color of each spatial group corresponds to the major branches in the “minmax” graph in Fig 12.
Mentions: Using the IDSS analysis we can systematically examine the full set of possible frequency seriation solutions (Figs 11–13). Despite the large number of possible solutions (N = 2.56 × 1018), iteratively finding the set of 97 possible solutions required less than two seconds of processing due to the fact that the largest possible seriations were composed of only 4 assemblages. No larger sets can be built without introducing violations of unimodality, so the algorithm did not need to continue its search and terminated. Using a confidence interval of α = 0.05 allowed us to generate a solution that included all assemblages. Fig 11 clearly shows how the traditional linear ordering breaks down as a visualization mechanism, especially in the presence of many valid solutions. A number of assemblages participate in multiple solutions, and it is impossible to get a sense of the overall nature of the solutions when confronted with many separate orderings. Thus, as described above, we focus here on the graph representation of results (Fig 12).

Bottom Line: This compression removes the ability to use all of the features of our data to fit to the seriation model, obscuring violations of the model, and thus lessens our ability to understand the degree to which the resulting order is chronological, spatial, or a mixture.Our initial implementation of IDSS does not solve all the problems of seriation, but begins to moves towards a resolution of a long-standing problem in archaeology while opening up new avenues of research into the study of cultural relatedness.We demonstrate the utility of IDSS using late prehistoric decorated ceramics from the Mississippi River Valley.

View Article: PubMed Central - PubMed

Affiliation: Department of Anthropology and IIRMES, California State University Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, USA.

ABSTRACT
Frequency seriation played a key role in the formation of archaeology as a discipline due to its ability to generate chronologies. Interest in its utility for exploring issues of contemporary interest beyond chronology, however, has been limited. This limitation is partly due to a lack of quantitative algorithms that can be used to build deterministic seriation solutions. When the number of assemblages becomes greater than just a handful, the resources required for evaluation of possible permutations easily outstrips available computing capacity. On the other hand, probabilistic approaches to creating seriations offer a computationally manageable alternative but rely upon a compressed description of the data to order assemblages. This compression removes the ability to use all of the features of our data to fit to the seriation model, obscuring violations of the model, and thus lessens our ability to understand the degree to which the resulting order is chronological, spatial, or a mixture. Recently, frequency seriation has been reconceived as a general method for studying the structure of cultural transmission through time and across space. The use of an evolution-based framework renews the potential for seriation but also calls for a computationally feasible algorithm that is capable of producing solutions under varying configurations, without manual trial and error fitting. Here, we introduce the Iterative Deterministic Seriation Solution (IDSS) for constructing frequency seriations, an algorithm that dramatically constrains the search for potential valid orders of assemblages. Our initial implementation of IDSS does not solve all the problems of seriation, but begins to moves towards a resolution of a long-standing problem in archaeology while opening up new avenues of research into the study of cultural relatedness. We demonstrate the utility of IDSS using late prehistoric decorated ceramics from the Mississippi River Valley. The results compare favorably to previous analyses but add new details into the structure of cultural transmission of these late prehistoric populations.

No MeSH data available.


Related in: MedlinePlus