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SUMMARY:Model selection\, model frames\, and scientific interpretation - J
 ulia Brettschneider (University of Warwick)
DTSTART:20180308T094500Z
DTEND:20180308T103000Z
UID:TALK102037@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Modelling complex systems in engineering\, science or social s
 cience involves selection of measurements on many levels including observa
 bility (determined e.g. by technical equipment\, cost\, confidentiality\, 
 existing records) and need for  interpretability. Among the initially sele
 cted variables\, the frequency and quality of observation may be altered b
 y censoring and sampling biases.   A model is\, by definition\, a simplifi
 cation\, and the question one asks is often not whether a certain effect e
 xists\, but whether it matters. This crucially depends on the research obj
 ective or perspective. Biased conclusions occur when the research question
  is interwoven with the mechanisms in which the variables for the analysis
  are selected or weighted.   Such effects can occur in any applications th
 at involve observational data. I will give some examples from a few of my 
 own research projects involving quality assessment\, decision making\, fin
 ancial trading\, genomics and microscopy.
LOCATION:Seminar Room 1\, Newton Institute
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