Automated feature tracking and vehicle navigation have the potential to facilitate autonomous surveys of ocean eddies by increasing sampling quality and/or decreasing operator workload. During an observational campaign in late 2013 and early 2014, methods for automated tracking were used to direct multiple ocean gliders during persistent surveys of a California Undercurrent eddy in Washington and British Columbia, Canada, coastal waters over a 3-month period. Glider observations of depth-averaged currents in the ocean’s upper kilometer and vertical separation of selected isopycnals were assimilated into a simple model describing eddy position, size, strength, and background flows using an extended Kalman filter. Though differing in detail from observations, results show the assumed eddy structure was sufficient to describe its essential characteristics and stably estimate eddy position through time. Forecast eddy positions and currents were used to select targets automatically to guide multiple gliders along transects through the eddy center as it translated. Transects performed under automated navigation had comparable or better straightness and distance from the eddy center when compared to navigation based on manual interpretation of the eddy scale and position. The tracking results were relatively insensitive to model choices at times when the eddy was well sampled, but they were more sensitive during sampling gaps and redundancies or rapid eddy translation. Overall, the results provide evidence that automated tracking and navigation are feasible with potential for widespread application in autonomous eddy surveys.
During the Holocene, the scale and complexity of human societies increased markedly. Generations of scholars have proposed different theories explaining this expansion, which range from broadly functionalist explanations, focusing on the provision of public goods, to conflict theories, emphasizing the role of class struggle or warfare. To quantitatively test these theories, we develop a general dynamical model based on the theoretical framework of cultural macroevolution. Using this model and Seshat: Global History Databank, we test 17 potential predictor variables proxying mechanisms suggested by major theories of sociopolitical complexity (and >100,000 combinations of these predictors). The best-supported model indicates a strong causal role played by a combination of increasing agricultural productivity and invention/adoption of military technologies (most notably, iron weapons and cavalry in the first millennium BCE).
I investigate the predictive ability of a simple demographic model for agrarian empires in several Old World geographies between 1500 BCE and 1500 CE. I estimate and bound key model parameters from two historical datasets. I find that quasi-uniform carrying capacities and two net birth rates suffice to predict most Old World agrarian empire demographics in this period. This analysis suggests that a doubling of agricultural intensification occurred throughout most of the Old World circa 1000 CE.
What have been the causes and consequences of technological evolution in world history? In particular, what propels innovation and diffusion of military technologies, details of which are comparatively well preserved and which are often seen as drivers of broad socio-cultural processes? Here we analyze the evolution of key military technologies in a sample of pre-industrial societies world-wide covering almost 10,000 years of history using Seshat: Global History Databank. We empirically test previously speculative theories that proposed world population size, connectivity between geographical areas of innovation and adoption, and critical enabling technological advances, such as iron metallurgy and horse riding, as central drivers of military technological evolution. We find that all of these factors are strong predictors of change in military technology, whereas state-level factors such as polity population, territorial size, or governance sophistication play no major role. We discuss how our approach can be extended to explore technological change more generally, and how our results carry important ramifications for understanding major drivers of evolution of social complexity.
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