https://www.wiki.ed.ac.uk/display/CIDS/2021+26th+March++-+Modelling+for+public+policy%3A+lessons+fro...The evidence from computational models is gaining is use and
legitimacy in the field of policy and strategy making, as data
availability, computational power, and modelling techniques develop,
bring model based decision making into the public eye. For over 100
years mathematical and computational models have been used generate
evidence to inform policy and the public - weather forecasting, disease
outbreaks, natural disasters, economic forecasting, etc. They are not
only use to help prepare for natural events, but are used to inform
choices in policy and business, and increasingly as the basis for
realtime operations - "automated decision making".
While
mathematical models maybe used in deterministic prediction, many
contemporary computational models help us deal with the unknown and the
uncertain - they help estimate gaps in data, or provide probabilistic
scenarios of the future. However, the results from running models can
always be contested, and probabilistic evidence can be hard to
incorporate in to policy and public debate. It is also hard to interpret
and trust the expert's jargon and graphs when compared with political
certainties or established heuristics.
The climate change threat
saw the rise of modelling in political and public consciousness, and the
COVID crisis doubled this, with the whole public struggling to analyse
predictions, and life changing decisions being made by political leaders
on the basis of hastily repurposed flu models and debatable graphics.
Different choices of and in models, often understood by relatively few
people can have profound impact on decisions. In this session we welcome
two mathematicians to present and discuss modelling in COVID, and
invite you to think about the future role of models across public
policy.
Chris and Ben are collaborating on a report to be published by the end of 2021 by the Newton Insitute, on mathematical sciences in the COVID pandemic