12-13 May 2022
Max Planck Institute for Evolutionary Biology
Europe/Berlin timezone

Developing a software platform for a systems’study of human history

13 May 2022, 12:15
Lecture Hall (Max Planck Institute for Evolutionary Biology)

Lecture Hall

Max Planck Institute for Evolutionary Biology

August Thienemann Strasse 2 24306 Plön Germany
remote Session 5


Ricardo Fernandes (Max Planck Institute for the Science of Human History, Jena, Germany)


Pandemics, war, inequality, environmental and climatic degradation, identitarian conflicts, and the rise of extreme political movements are not isolated phaenomena but rather intertwined in ways often difficult to detect. This reminds us that human societies are complex dynamical systems and themselves part of a broader human-environmental system or nested by social, economic, cultural, technological, political, and climatic systems. Gaining a better understanding of the intricate connections among such systems is a difficult but crucial task for policy makers and risk analysts facing modern-day societal problems. Yet, the useful contribution that could be made by the systematic study of past societies remains largely unexplored.To gain useful historical knowledge I am developing a systems’ approach for the comparative study of past societies. This is grounded on the Big Data initiatives Pandora & IsoMemo that form an interdisciplinary distributive network of open-access historical and archaeological databases plus on a new open-source software platform. Thisplatform consists of a combination of existing and novel self-developed R packages that are made available via user-friendly Shiny interfaces. Bayesian modelling of diverse types of proxy data is extensively employedfor spatiotemporal reconstruction of past human activities or eventsand paleo-environmental conditions. Modelling features include the ability to incorporate historical uncertainties and prior expert knowledge. In addition, a variety of formal hypothesis testing approaches are also being developed together with more data driven methods (e.g., Bayesian networks) for the discovery of historical causal mechanisms.In my presentation, I will givea brief overview of the Pandora & IsoMemo initiatives, describe theirsoftware platform, andillustrate research possibilities via a selection of case studies.

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