eupolicy.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
This Mastodon server is a friendly and respectful discussion space for people working in areas related to EU policy. When you request to create an account, please tell us something about you.

Server stats:

214
active users

#onedimensionalmetrics

0 posts0 participants0 posts today
Daniele de Rigo<p>8/</p><p>A clear introduction on intrinsic <a href="https://hostux.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>uncertainty</span></a> in <a href="https://hostux.social/tags/ComputationalScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalScience</span></a> modelling (and epistemic humility) by Richard de Neufville (MIT <a href="https://hostux.social/tags/OpenCourseWare" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenCourseWare</span></a>, Risk and Decision Analysis):</p><p>- Flaw of Averages: why central <a href="https://hostux.social/tags/OneDimensionalMetrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OneDimensionalMetrics</span></a> in non-trivial systems may mislead<br><a href="https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/resources/unit-2-flaw-o-f-averages-video-5/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ocw.mit.edu/courses/ids-333-ri</span><span class="invisible">sk-and-decision-analysis-fall-2021/resources/unit-2-flaw-o-f-averages-video-5/</span></a></p><p>- unexpected surprises in the real world<br><a href="https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/resources/unit-2-forecast-always-wrong-video-1/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ocw.mit.edu/courses/ids-333-ri</span><span class="invisible">sk-and-decision-analysis-fall-2021/resources/unit-2-forecast-always-wrong-video-1/</span></a></p><p>- Porcupine Graphs: how "often experts don't learn", think "they know better" and miss evident mental biases<br><a href="https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/resources/unit-2-porcupine-graphic-video-3/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ocw.mit.edu/courses/ids-333-ri</span><span class="invisible">sk-and-decision-analysis-fall-2021/resources/unit-2-porcupine-graphic-video-3/</span></a></p>
Daniele de Rigo<p>3/</p><p>A partial illustration on <a href="https://hostux.social/tags/HiddenUncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HiddenUncertainty</span></a> influencing attempts to compress <a href="https://hostux.social/tags/complexity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>complexity</span></a> into <a href="https://hostux.social/tags/OneDimensionalMetrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OneDimensionalMetrics</span></a> may be "a large-scale crowdsourced research effort involving 73 teams" which found "that analyzing the same hypothesis with the same data can lead to substantial differences in statistical estimates and substantive conclusions" [2]</p><p>"Instead of convergence, teams’ results varied greatly, ranging from large negative to large positive effects"</p><p>See Fig 1<br><a href="https://www.pnas.org/doi/10.1073/pnas.2203150119#fig01" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pnas.org/doi/10.1073/pnas.2203</span><span class="invisible">150119#fig01</span></a></p>