906,160 research outputs found
Eigenvalues of Euclidean Random Matrices
We study the spectral measure of large Euclidean random matrices. The entries
of these matrices are determined by the relative position of random points
in a compact set of . Under various assumptions we establish
the almost sure convergence of the limiting spectral measure as the number of
points goes to infinity. The moments of the limiting distribution are computed,
and we prove that the limit of this limiting distribution as the density of
points goes to infinity has a nice expression. We apply our results to the
adjacency matrix of the geometric graph.Comment: 16 pages, 1 figur
Assessing Simulations of Imperial Dynamics and Conflict in the Ancient World
The development of models to capture large-scale dynamics in human history is
one of the core contributions of cliodynamics. Most often, these models are
assessed by their predictive capability on some macro-scale and aggregated
measure and compared to manually curated historical data. In this report, we
consider the model from Turchin et al. (2013), where the evaluation is done on
the prediction of "imperial density": the relative frequency with which a
geographical area belonged to large-scale polities over a certain time window.
We implement the model and release both code and data for reproducibility. We
then assess its behaviour against three historical data sets: the relative size
of simulated polities vs historical ones; the spatial correlation of simulated
imperial density with historical population density; the spatial correlation of
simulated conflict vs historical conflict. At the global level, we show good
agreement with population density (), and some agreement with
historical conflict in Europe (). The model instead fails to
reproduce the historical shape of individual polities. Finally, we tweak the
model to behave greedily by having polities preferentially attacking weaker
neighbours. Results significantly degrade, suggesting that random attacks are a
key trait of the original model. We conclude by proposing a way forward by
matching the probabilistic imperial strength from simulations to inferred
networked communities from real settlement data
Binary indices at various densities
Binary similarity indices are numerical analysis methods used to compare data involving two binary vectors (lists). The scope of this project involved comparing 54 binary similarity indices methods in relationship to binary vector density using the R programming language. Matrices were created of various vector data. The matrices were then scrambled to represent random data. Finally, the data was analyzed and plotted. Vector density variation can result in large differences - in both rate of change relative to density and magnitude. Awareness of these differences is important when selecting an analysis method and understanding the effects of changing vector density on analysis of results
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