Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization

Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as "Seshat: Global History Databank." We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history

An Introduction to Seshat: Global History Databank

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. Thee sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is part of a growing trend to use comparative historical data on a large scale and contributes as such to a growing consilience between the humanities and social sciences. Seshat is underpinned by a robust and transparent workflow to ensure the ever growing dataset is of high quality

Geographical Information and historical research: Current progress and future directions.

To a greater or lesser extent, all historians make use of geographical information. This means that there is clearly a large potential for the use of Geographical Information Systems (GIS) in the discipline. GIS has its origins in the earth sciences and its approach and the way that it models the world is based on the traditions and requirements of these subjects. This means that while there are many advantages to using GIS in historical research, its use must be implemented with caution, based on the limitations of the data and the traditions of historical scholarship. In this paper we define GIS and explain why it is relevant to historical research. We then use a wide variety of examples to illustrate the ways in which historians have used GIS. Finally, we discuss how GIS needs to be improved to make it more applicable to historical research. Our aim is to demonstrate that GIS, if properly used, is not only applicable to the more quantitative, scientific historical paradigms, but is equally appropriate in the more humanities-driven, qualitative areas of the discipline

Bottom-Up Gazetteers: Learning from the Implicit Semantics of Geotags

As directories of named places, gazetteers link the names to geographic footprints and place types. Most existing gazetteers are managed strictly top-down: entries can only be added or changed by the responsible toponymic authority. The covered vocabulary is therefore often limited to an administrative view on places, using only official place names. In this paper, we propose a bottom-up approach for gazetteer building based on geotagged photos harvested from the web. We discuss the building blocks of a geotag and how they relate to each other to formally define the notion of a geotag. Based on this formalization, we introduce an extraction process for gazetteer entries that captures the emergent semantics of collections of geotagged photos and provides a group-cognitive perspective on named places. Using an experimental setup based on clustering and filtering algorithms, we demonstrate how to identify place names and assign adequate geographic footprints. The results for three different place names (Soho, Camino de Santiago and Kilimanjaro), representing different geographic feature types, are evaluated and compared to the results obtained from traditional gazetteers. Finally, we sketch how our approach can be combined with other (for example, linguistic) approaches and discuss how such a bottom-up gazetteer can complement existing gazetteers.</p