Seriation is a set of methods which
Aper: MIG GP CC.
Seriation is a set of methods which use historical classes to chronologically order otherwise unordered archaeological assemblages and/or objects [1]. Frequency seriation is a technique that produces chronological sequences by arranging descriptions of assemblages so that the frequencies of artifact classes jointly form unimodal distributions. Developed in the early 20th century, frequency seriation played an integral role in the emergence of archaeology as a coherent discipline [2] and enabled culture historians to construct regional chronologies of prehistory throughout the New World [3?2]. Yet, for the last 50 years, frequency seriation has been WP1066 site largely ignored due to its association with relative chronology and the mistaken belief that radiometric dating techniques have replaced it. Saddled with a prevalent misunderstanding that seriation is simply a “dating method” [13] that is useful only when radiocarbon dating is impossible [14], seriation has never been fully developed as a computational algorithm. While there has been some interest in seriation for disciplines outside of archaeology [15?8], to the extent that methodological development has occurred in archaeology over the last 50 years, the focus has been largely on reducing the method to probabilistic similarity-ordering problems that can be attacked via multivariate statistical methods [19?3]. The roots of frequency seriation, however, stem from a deterministic algorithm that identifies orders on the basis of occurrence and frequency criteria. [1]. Recently, deterministic frequency seriation (hereafter, DFS) received some attention due to the demonstration that the method can be theoretically rationalized using an evolutionary framework. While the potential of this idea has been long recognized [24?6], the work of Neiman [27] firmly established an explanatory basis within cultural transmission models for the unimodal distributions that form the core of the frequency seriation algorithm. Neiman’s achievement has led to the re-imagining of DFS as a general tool for studying patterns of cultural inheritance within populations through time and across space [28?0]. With these advances, there remains substantial promise for DFS to again become a primary tool for archaeological analyses as it enables researchers to quantitatively track patterns of interaction, define social communities, and trace lineages among past populations, in addition to informing upon chronology. In this way, frequency seriation could serve as a key method in the establishment of a fully evolution-based discipline. Despite its potential, the use of DFS as a productive tool for archaeological research remains difficult, and methods for constructing and evaluating solutions are incomplete. While a handful of assemblages can be seriated using hand manipulation, sorting through all possible orderings for a set of assemblages is neither feasible nor systematic. When the numbers of assemblages grows, a ZM241385 molecular weight combinatorial explosion sets in, first visible once 10 or more assemblages are analyzed. The order of magnitude of numbers involved makes brute force approaches impossible even using modern computing power. This limitation was recognized early in the discipline. When archaeologists became concerned with the quantitative basis of their methods, probabilistic approaches were developed that could construct orders on the basis of similarity scores [41?9]. With probability-based seriation techniques one i.Seriation is a set of methods which
Aper: MIG GP CC.
Seriation is a set of methods which use historical classes to chronologically order otherwise unordered archaeological assemblages and/or objects [1]. Frequency seriation is a technique that produces chronological sequences by arranging descriptions of assemblages so that the frequencies of artifact classes jointly form unimodal distributions. Developed in the early 20th century, frequency seriation played an integral role in the emergence of archaeology as a coherent discipline [2] and enabled culture historians to construct regional chronologies of prehistory throughout the New World [3?2]. Yet, for the last 50 years, frequency seriation has been largely ignored due to its association with relative chronology and the mistaken belief that radiometric dating techniques have replaced it. Saddled with a prevalent misunderstanding that seriation is simply a “dating method” [13] that is useful only when radiocarbon dating is impossible [14], seriation has never been fully developed as a computational algorithm. While there has been some interest in seriation for disciplines outside of archaeology [15?8], to the extent that methodological development has occurred in archaeology over the last 50 years, the focus has been largely on reducing the method to probabilistic similarity-ordering problems that can be attacked via multivariate statistical methods [19?3]. The roots of frequency seriation, however, stem from a deterministic algorithm that identifies orders on the basis of occurrence and frequency criteria. [1]. Recently, deterministic frequency seriation (hereafter, DFS) received some attention due to the demonstration that the method can be theoretically rationalized using an evolutionary framework. While the potential of this idea has been long recognized [24?6], the work of Neiman [27] firmly established an explanatory basis within cultural transmission models for the unimodal distributions that form the core of the frequency seriation algorithm. Neiman’s achievement has led to the re-imagining of DFS as a general tool for studying patterns of cultural inheritance within populations through time and across space [28?0]. With these advances, there remains substantial promise for DFS to again become a primary tool for archaeological analyses as it enables researchers to quantitatively track patterns of interaction, define social communities, and trace lineages among past populations, in addition to informing upon chronology. In this way, frequency seriation could serve as a key method in the establishment of a fully evolution-based discipline. Despite its potential, the use of DFS as a productive tool for archaeological research remains difficult, and methods for constructing and evaluating solutions are incomplete. While a handful of assemblages can be seriated using hand manipulation, sorting through all possible orderings for a set of assemblages is neither feasible nor systematic. When the numbers of assemblages grows, a combinatorial explosion sets in, first visible once 10 or more assemblages are analyzed. The order of magnitude of numbers involved makes brute force approaches impossible even using modern computing power. This limitation was recognized early in the discipline. When archaeologists became concerned with the quantitative basis of their methods, probabilistic approaches were developed that could construct orders on the basis of similarity scores [41?9]. With probability-based seriation techniques one i.