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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
Cliodynamics, Volume 10, Issue 2 (2019)

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 behavior against three historical datasets: the relative size of simulated polities versus historical ones; the spatial correlation of simulated imperial density with historical population density; and the spatial correlation of simulated conflict versus historical conflict. At the global level, we show good agreement with population density (R2<0.75), and some agreement with historical conflict in Europe (R2<0.42). 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 neighbors. 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.

 

Page numbers for this article were updated on 01/05/2021.

  • 1 supplemental PDF
Cover page: Assessing Simulations of Imperial Dynamics and Conflict in the Ancient WorldCreative Commons 'BY' version 4.0 license
  • Article
  • Peer Reviewed
Cliodynamics, Volume 6, Issue 1 (2015)

Understanding patterns of conflict and pathways in which political history became established is critical to understanding how large states and empires ultimately develop and come to rule given regions and influence subsequent events. We employ a spatiotemporal Cox regression model to investigate possible causes as to why regions were attacked by the Neo-Assyrian (912-608 BCE) state. The model helps to explain how strategic benefits and costs lead to likely pathways of conflict and imperialism based on elite strategic decision-making. We apply this model to the early 9th century BCE, a time when historical texts allow us to trace yearly campaigns in specific regions, to understand how the Neo-Assyrian state began to re-emerge as a major political player, eventually going on to dominate much of the Near East and starting a process of imperialism that shaped the wider region for many centuries even after the fall of this state. The model demonstrates why specific locations become regions of conflict in given campaigns, emphasizing a degree of consistency with which choices were made by invading forces with respect to a number of factors. We find that elevation and population density deter Assyrian invasions. Moreover, costs were found to be more of a clear motivator for Assyrian invasions, with distance constraints being a significant driver in determining where to campaign. These outputs suggest that Assyria was mainly interested in attacking its weakest, based on population and/or organization, and nearest rivals as it began to expand. Results not only help to address the emergence of this empire, but enable a generalized understanding of how benefits and costs to conflict can lead to imperialism and pathways to political outcomes that can have major social relevance.

  • 1 supplemental ZIP
Cover page: Modeling Strategic Decisions in the Formation of the Early Neo-Assyrian Empire
  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2017)

Background

Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging).

Results

Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome.

Conclusions

Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes.
    Cover page: Suite of simple metrics reveals common movement syndromes across vertebrate taxa.
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2020)

    Objective

    To evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.

    Design and outcome measures

    We evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.

    Setting

    Four UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.

    Results

    1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.

    Conclusion

    The Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.
      Cover page: A national initiative in data science for health: an evaluation of the UK Farr Institute
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