Book Review: Noise

When discussing human judgment and, by extension, algorithmic decisions, we are used to talking about ๐›๐ข๐š๐ฌ, but what about ๐ง๐จ๐ข๐ฌ๐ž?

Above picture by: Little, Brown Spark, Featured image by: Sophie Huiberts

๐ŸŽฏ Nobel Laureate แด…แด€ษดษชแด‡สŸ แด‹แด€สœษดแด‡แดแด€ษด and co-authors make a case for why we should pay close attention to it in their new book ๐‘๐‘œ๐‘–๐‘ ๐‘’: ๐ด ๐น๐‘™๐‘Ž๐‘ค ๐‘–๐‘› ๐ป๐‘ข๐‘š๐‘Ž๐‘› ๐ฝ๐‘ข๐‘‘๐‘”๐‘’๐‘š๐‘’๐‘›๐‘ก. It has some compelling stories to underpin how widespread the problem is in business and government with succinct illustrations. For instance, I love the target illustration and the error decompositions.

๐Ÿ“ข The book covers group dynamics such as information cascades, social pressure, group polarization as amplifiers of noise, and some cognitive #biases to boot. Lastly, it outlines noise mitigation strategies with decision hygiene, decision observers, and noise audits, which were BY FAR the biggest takeaways for me.

๐Ÿ˜’ However, if you are already familiar with the topic, the book will likely disappoint (at least a little). It can feel very repetitive and not getting into enough depth, and its entanglement with bias means it keeps referring to concepts covered in ๐‘‡โ„Ž๐‘–๐‘›๐‘˜๐‘–๐‘›๐‘” ๐น๐‘Ž๐‘ ๐‘ก ๐‘Ž๐‘›๐‘‘ ๐‘†๐‘™๐‘œ๐‘ค, as it was some long-lost final chapter. I still enjoyed it, regardless.

Have you read it? Do you want to?

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