Applying event history analysis to explain the diffusion of innovations in archaeological networks

Viviana Amati,Jessica Munson,Jonathan Scholnick,Habiba Habiba

doi:10.1016/j.jas.2019.01.006

Viviana Amati, Jessica Munson + Show 2 more

Open Access

https://doi.org/10.1016/j.jas.2019.01.006

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Abstract

The simple dyadic structure of a network is the basis for studying a wide variety of entities and their relationships, as well as the outcomes of processes such as the diffusion of innovations. Here, we apply models from event history analysis and cultural evolutionary theory to investigate whether and by what means network ties facilitated the transmission of certain cultural traits in past complex societies. To illustrate the application of these models to archaeological data, we examine the spread of dynastic rituals by analyzing data collected from Classic Maya hieroglyphic inscriptions. In addition to providing a cautionary tale for the construction of archaeological networks, the results of this study highlight the compatibility of cultural evolutionary and social network approaches to investigate the spread of novel traits.

Highlights

The phrase “diffusion of innovation” refers to the process describing how an innovation spreads over time among the entities in a population (Rogers, 1995)
We introduce event history analysis as a method to investigate the ways different factors facilitate the flow of cultural information in past societies when detailed historical records
The full model indicates that the likelihood of adopting an accession ritual depends on the network variables

Summary

Introduction

The phrase “diffusion of innovation” refers to the process describing how an innovation (e.g., a new idea, information, disease, or technology) spreads over time among the entities in a population (Rogers, 1995). Some classic examples of diffusion of innovation in archaeology include studies on the spread of farming (Ammerman and CavalliSforza, 1971), hunting technologies (Bettinger and Eerkens, 1999; Jordan, 2014; Fort, 2015; Buchanan et al, 2017), pottery technology (Eerkens and Lipo, 2014), architectural technology (Östborn and Gerding, 2015, 2016), and the adoption of artifact styles (Dethlefsen and Deetz, 1966; Davis, 1983; Scholnick, 2012) These studies describe diffusion by analyzing the distributions of one or more traits in space and time using a variety of methods, such as seriation, typology, and agent-based and spatial modeling. It is surprising there has not been more crossover between these research domains, this paper demonstrates that there are significant parallels and important contributions to be made

Diffusion of innovation: concepts and factors

Event history analysis

Application

Model specification and estimation

Results

Discussion