Mathematics of Past and Present Social Systems: Workshop II: Data past and present
The semester is organized by the Cluster of Excellence Math+, supported by the Einstein Foundation Berlin and will take place at ZIB.
Organizers
Nataša Djurdjevac Conrad (ZIB)
Benjamin Ducke (DAI)
Friederike Fless (DAI)
Stefan Klus (U Surrey)
Jürgen Kurths (PIK/HU)
Christof Schütte (ZIB/FU)
Stefanie Winkelmann (ZIB)
Sarah Wolf (FU/GCF)
The semester is organized within the framework of the Berlin Mathematics Research Center Math+ and supported by the Einstein Foundation Berlin.
We are committed to fostering an atmosphere of respect, collegiality, and sensitivity. Please read our MATH+ Collegiality Statement.
Registration
To register for an event / events within the Thematic Einstein Semester please use the link:
Scope of the Semester
The Thematic Einstein Semester The Mathematics of Complex Social Systems: Past, Present, and Future aims at unlocking the potential for mathematical modeling and reasoning within the extremely large and diversified fields of study that constitute modern Social Sciences and the Humanities. It shall bring together young researchers and experienced scholars from mathematics and other disciplines to focus on complex social systems, with a two-fold focus on agent-based models (ABMs) and data-driven methods. The TES will consist of specific events, continuous activities over the semester, and research visits.
Workshop II: Data past and present (17-18 May 2022)
In this workshop research data becomes the proverbial rubber that meets the road. Models, data and interpretations from a range of research domains will be juxtaposed and illustrated by worked case studies. This will include data sampled from a present population in a controlled manner, as well as more extreme data about past populations that is fragmentary and results from only weakly controlled sampling. An overarching aim will be to identify the manifold sources of bias, uncertainty and error in real-world research data, and to scrutinize data-derived conclusions. Another focus will be on formal methods and frameworks for handling incomplete data and representing uncertainty.