The cultural evolution of warfare practices: examining the roles of social structure, political complexity, and resource ecology with cross-cultural comparative analyses

This thesis investigates how forms of wartime violence changed with the scale and complexity of past human societies. Data on aspects of social and political structures, subsistence practices, and warfare were coded from ethnographic and secondary historical sources for a global sample of societies. Four studies are presented that examine variation in warfare cross-culturally and historically, specifically the prevalence of self-sacrificial actions for other group members, levels of indiscriminate killing of enemies, and the taking of enemy body parts as trophies. These behaviors were tested for relationships with social complexity and associated variables, including military formalization and reliance on agriculture. Overall, there was no evidence for any clear relationships. These efforts resulted in the creation of datasets representing archaeologically, historically, and ethnographically recorded societies and defined new variables for specific wartime behaviors which had not previously been the focus of quantitative comparative analyses. More broadly, it contributes to the growing area of cultural evolutionary research with comparative historical databases and to research on the evolution of warfare through human history

The Equinox2020 Seshat data release

This report describes the current canonical time-series dataset named “Equinox2020,” a subset of Seshat: Global History Databank data for a well-curated list of polities and variables available on the Seshat Data Browser. The report provides an introduction to the methods and procedures of the Seshat project relating to the curation and release of the Equinox2020 dataset

The Benefits and Challenges of Linked Datasets for Cliodynamics and Comparative Anthropology

The past few decades have witnessed a proliferation of large comparative cultural databases, primarily consisting of contemporary data (e.g., ethnographic writings), but increasingly historical data as well (including archaeological materials). Individually, these databases already serve as valuable resources as evidenced by the growing number of papers utilizing them. However, further benefits could result from merging or linking these data in ways that surpass their original intentions and ambitions. One avenue is the integration of ethnographic and historical data to help remedy the weaknesses of each (e.g., by addressing lacunae, imprecision, bias, subjectivity, and unreliability) and draw on their reciprocal strengths (e.g., by combining longitudinal depth and primary source material) of these different forms of evidence. The work presented here is a further step in that direction. This article shows how efforts to quantitatively examine historical variation in features of warfare benefit from combining ethnographic, historical, and archaeological data. It describes the general challenges faced by combining datasets (e.g. units of analyses, differing variables across datasets, sampling issues, etc.), how these challenges can be mitigated, and what further challenges remain to be addressed. The overall aim is to encourage further research into the benefits and challenges of  integrating such datasets

A novel comprehensive investigation for enhancing cluster analysis accuracy through ensemble learning methods

Ensemble learning stands out as a widely embraced technique in machine learning. This research explores the application of ensemble learning, including ensemble clustering, to enhance the precision of cluster analysis for datasets with multiple attributes and unclear correlations. Employing a majority voting-based ensemble clustering approach, specific techniques such as k-means clustering, affinity propagation, mean shift, BIRCH clustering, and others are applied to defined datasets, leading to improved clustering results. The study involves a comprehensive comparative analysis, contrasting ensemble clustering outcomes with those of individual techniques. The process of improving cluster identification accuracy encompasses data collection, pre-processing to exclude irrelevant elements, and the application of standard clustering algorithms. The task includes defining the optimal number of groups before comparing clustering models. Additionally, a combined model is constructed by merging BIRCH clustering and mean shift clustering, leveraging their advantages to enhance overall clustering strength and accuracy. This research contributes to advancing ensemble learning and ensemble clustering methodologies, offering improved accuracy, and uncovering hidden patterns in complex datasets