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.

The origin of human ultrasociality—the ability to cooperate in huge groups of genetically unrelated individuals—has long interested evolutionary and social theorists, but there has been little systematic empirical research on the topic. The Historical Database of Sociocultural Evolution, which we introduce in this article, brings the available historical and archaeological data together in a way that will allow hypotheses concerning the origin of ultrasociality to be tested rigorously. In addition to describing the methodology informing the set-up of the database, our article introduces four hypotheses that we intend to test using the database. These hypotheses focus on the resource base, warfare, ritual, and religion, respectively. Ultimately the aim of our database is to offer a ‘rapid discovery science’ route to the study of the past. We believe our approach is not only highly complementary with existing traditions of enquiry in history and archaeology but will extend their intellectual scope and explanatory power.

The vast amount of knowledge about past human societies has not been systematically organized and, therefore, remains inaccessible for empirically testing theories about cultural evolution and historical dynamics. For example, what evolutionary mechanisms were involved in the transition from the small-scale, uncentralized societies, in which humans lived 10,000 years ago, to the large-scale societies with an extensive division of labor, great differentials in wealth and power, and elaborate governance structures of today? Why do modern states sometimes fail to meet the basic needs of their populations? Why do economies decline, or fail to grow? In this article, we describe the structure and uses of a massive databank of historical and archaeological information, Seshat: The Global History Databank. The data that we are currently entering in Seshat will allow us and others to test theories explaining how modern societies evolved from ancestral ones, and why modern societies vary so much in their capacity to satisfy their members’ basic human needs.

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.

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a large enough scale in order to be able to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates productivity, or potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying capacity of a given region, independently of estimates of population size. We outline how explicit crop yield models can be combined with high quality historical and archaeological information about past societies, in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss the kinds of information we need to collect about agricultural techniques and practices in the past, and introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the process of estimating past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, and will enable us to test more rigorously key hypotheses about human socio-cultural evolution.