Agricultural productivity in past societies: toward an empirically informed model for testing cultural evolutionary hypotheses

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 scale large enough 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 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 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 information we need to collect about past agricultural techniques and practices, and introduce a new databank initiative that 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 estimation of 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, enabling us to test more rigorously key hypotheses about human socio-cultural evolution

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

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

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.This work was supported by a John Templeton Foundation Grant (to the Evolution Institute) entitled “Axial-Age Religions and the Z-Curve of Human Egalitarianism,” a Tricoastal Foundation Grant (to the Evolution Institute) entitled “The Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability,” Economic and Social Research Council Large Grant REF RES-060-25-0085 entitled “Ritual, Community, and Conflict,” an Advanced Grant from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme Grant 694986, and Grant 644055 from the European Union’s Horizon 2020 Research and Innovation Programme (ALIGNED; www.aligned-project.eu). T.E.C. is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement 716212).Peer Reviewe

Population-Area Relationship for Medieval European Cities

<div><p>Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems. Yet, many of the spatial, political and economic features of medieval European cities were particular to the Middle Ages, and subsequently changed over the Early Modern Period and Industrial Revolution. There is a long tradition of demographic studies estimating the population sizes of medieval European cities, and comparative analyses of these data have shed much light on the long-term evolution of urban systems. However, the next step—to systematically relate the population size of these cities to their spatial and socioeconomic characteristics—has seldom been taken. This raises a series of interesting questions, as both modern and ancient cities have been observed to obey area-population relationships predicted by settlement scaling theory. To address these questions, we analyze a new dataset for the settled area and population of 173 European cities from the early fourteenth century to determine the relationship between population and settled area. To interpret this data, we develop two related models that lead to differing predictions regarding the quantitative form of the population-area relationship, depending on the level of social mixing present in these cities. Our empirical estimates of model parameters show a strong densification of cities with city population size, consistent with patterns in contemporary cities. Although social life in medieval Europe was orchestrated by hierarchical institutions (e.g., guilds, church, municipal organizations), our results show no statistically significant influence of these institutions on agglomeration effects. The similarities between the empirical patterns of settlement relating area to population observed here support the hypothesis that cities throughout history share common principles of organization that self-consistently relate their socioeconomic networks to structured urban spaces.</p></div

Estimation of Area—Population Scaling Relation for All Settlements.

<p>The Area—Population scaling relation for the entire data set of all medieval cities (<i>n = 173</i>). The black line represents proportionate (linear) scaling; the yellow line the theoretical prediction where <i>α</i> = 5/6; and the red line the best-fit line from OLS regression of the log-transformed data.</p

Schematic Social Networks of Towns and Cities.

<p>(A) An unstructured network where anyone can in principle connect with anyone else, subject to limitations deriving from cost of movement. Such a network is characterized by increasing connectivity with city population size, with mean degree <i>k</i>(<i>N</i>) = <i>k</i>₀ <i>N</i>^<i>δ</i>, <i>δ</i> ∼ 1/6. (B) A structured socioeconomic network. In this case, interactions between individuals are regulated by social groups and institutions (black squares) and may be damped by a factor s<1, for each level of institutions involved. If the parameter s<1, the net effect of institutions is to weaken social possibilities and thus reduce agglomeration effects, taking the exponent of the scaling of area with population for settlements closer to unity.</p

Map of Western European Settlements <i>ca</i>. 1300 CE Examined in this Paper.

<p>Medieval towns and cities of Western Europe <i>ca</i>. AD 1300 examined in this paper (n = 173), in England (red; n = 40), France and Belgium (blue; n = 63), Northern Italy (green; n = 30) and Germany (yellow; n = 40). All settlements examined have populations of >1,000, and in most cases have populations >5,000. This map was created using ArcGIS^® software by Esri, © OpenStreetMap and contributors, Creative Commons-Share Alike License (CC-BY-SA).</p

Measuring the Settled Area of Medieval Settlements.

<p>Bristol’s built-up areas in the later middle ages (late 13th–early 14th centuries), including built-up suburban areas shaded in grey. The red line indicates the 130 ha settled area we measured for the city, whereas the inner area circumscribed by walls and rivers measures only 55 ha. Even our relatively conservative outline of the city’s built up area more than doubles Bristol’s settled area. This map is modified and redrawn by the authors from Derek Keene’s (1976) map of the suburban built up area of later medieval Bristol [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162678#pone.0162678.ref102" target="_blank">102</a>].</p

Estimation of Area—Population Scaling Relations for Regional Urban Systems.

<p>Estimation of Area—Population scaling relations for: (A) England (red); (B) France and Belgium (blue); (C) Northern and Central Italy (green); and (D) Germany (yellow). The black line represents proportionate (linear) scaling; the yellow line the theoretical prediction where <i>α</i> = 5/6; and the red line the best-fit line from OLS regression of the log-transformed data.</p