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SUMMARY:From code to consequences: Modelling for policy - Eloisa Perez Ben
 netts
DTSTART;TZID=Europe/Madrid:20261107T114000
DTEND;TZID=Europe/Madrid:20261107T122000
DTSTAMP:20260726T231357Z
UID:pretalx-pycones-2026-X3TWLZ@pretalx.com
DESCRIPTION:Computational models are increasingly used to inform real-worl
 d policy decisions — from public health and climate planning to economic
 s and infrastructure design. In practice\, these models are not used as 
 “prediction machines”\, but as structured ways of exploring uncertaint
 y\, testing assumptions\, and comparing scenarios when real-world experime
 ntation is impossible.\n\nIn this talk\, I’ll discuss what it actually m
 eans to build and use modelling systems in a policy context\, drawing on e
 xperience developing epidemiological and other applied simulation models i
 n Python. While epidemiology provides one concrete example\, the focus of 
 the talk is broader: how models are constructed\, how they are used by dec
 ision-makers\, and where misunderstandings often arise.\n\nI will highligh
 t common failure modes — such as over-interpreting model outputs\, hidde
 n assumptions in code\, and miscommunication between technical builders an
 d non-technical users — as well as what good practice looks like in term
 s of transparency\, uncertainty handling\, and reproducibility.\n\nThe goa
 l of this talk is to give Python developers a clearer understanding of how
  modelling systems are used beyond academia\, and why careful software des
 ign and communication matter when code has real-world consequences.
LOCATION:Track 03 - Manychat
URL:https://pretalx.com/pycones-2026/talk/X3TWLZ/
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