While archaeologists have been incorporating agent-based modeling in select research approaches, there have been no tutorials on how to use the tools specifically for modeling the past. This series brings together three case studies, followed by three step-by-step tutorials (see Supplemental Info for each article). The first paper presents the idea of why someone studying the past would want to use agent-based modeling. The second paper demonstrates how to incorporate realistic spatial data into a model. The third paper explores how agent-based modeling can be helpful for outreach, policy making, and teaching. This series aims to help those interested in incorporating computational modeling approaches into their research. And while the tutorials are archaeology-focused, the papers link to cross-disciplinary research and literature. Finally, by using open source packages the authors aim to link to open science and open education, promoting the core scientific principles at CRI.
What are we reading at CRI Research? Currently, two books that just arrived onto our bookshelf:
“Invisible Women: Exposing Data Bias in a World Designed for Men” by Caroline Criado Perez
“The Tyranny of Metrics” by Jerry Muller
A new review paper by Stefani Crabtree, a Short-term Fellow at CRI Research, and collaborators just appeared in the Springer Encyclopedia of Global Archaeology. It discusses the use of network science by archeologists. You can find the full text here.
Archaeologists reconstruct the activities and interactions of individuals using the accumulated material culture of the past, yet detecting these interactions can be difficult using traditional archaeological analytical tools. The development of a methodological framework emerging from graph theory, coupled with the growth of computational power and a growing multidisciplinary theoretical framework aimed at interpreting these analyses, have eased the difficulties of uncover ing, analyzing, and interpreting networks in the past. From examining physical locations of sites and how they interact together to examining trade routes and migration pathways, and the exchange of ideas across time and space, network approaches have infiltrated archaeology and grown exponentially in published studies. As computational power increases, and the use of large datasets that have unequal structure or resolution become easier and more common, network studies will undoubtedly shed further light on the archaeological past. These approaches may well herald new interdisciplinary collaborations and allow archaeologists to use these models of past social networks to understand our present and future.
A new paper on ageing in bacteria by former (Ulrich K. Steiner, Ming Ni, Peipei Chen, Xiaohu Song) and current (François Taddei, Ariel B. Lindner) researchers from CRI. It used experimental data and mathematical approaches to show that the pattern of ageing in bacteria (specifically E. coli) can be explained by two stochastic processes.
The full text of the study is available here
Despite advances in ageing research, a multitude of ageing models, and empirical evidence for diverse senescence patterns, understanding of the biological processes that shape senescence is lacking. This work shows that senescence of an isogenic Escherichia coli bacterial population results from two stochastic processes. The first process is a random deterioration process within the cell, such as generated by random accumulation of damage. The second process relates to the stochastic asymmetric transmission at cell fission of an unknown factor that influences mortality. The work calls for exploration of similar stochastic influences that shape ageing patterns beyond simple organisms.
A new study by Ignacio Atal, a Long-term Fellow at CRI Research, and collaborators from Hôpital Hôtel-Dieu, just appeared in the Journal of Clinical Epidemiology. It shows how the statistical significance of meta-analyses can be fragile if you modify single events in the studies included in the meta-analysis.
The full text of the study is available here
Meta-analyses inform clinical practice by summarising treatment effect using results from multiple trials. However, the statistical significance of such meta-analysis, may rely on the outcome of only a few patients from specific trials included. The study evaluates this effect by defining the Fragility Index of meta-analyses, simply a minimal number of changes in the patient outcome (e.g. cured vs. not cured), that would change the study conclusions. Among 906 meta-analyses considered, the median Fragility Index was 12, but for about 30% of studies it was 5 or less. This indicates overall fragility of the outcomes of meta-analysis to small changes in the specific studies considered.
The authors also created a website that allows other scientists to evaluate the fragility of their meta-analyses.