## Metadata - Author:: [[gerd-gigerenzer|Gerd Gigerenzer]] - Full Title:: One Data Point Can Beat Big Data - Category:: #🗞️Articles - URL:: https://behavioralscientist.org/gigerenzer-one-data-point-can-beat-big-data/ - Finished date:: [[2023-01-07]] ## Highlights - the engineers embarked on improving the algorithm. To do so, there are two possible approaches. One is to fight complexity with complexity. The idea is: Complex problems need complex solutions, and if a complex algorithm fails, it needs to be made more complex. The second approach follows the stable-world principle ([View Highlight](https://read.readwise.io/read/01gp3ckkhfpxpj9pw5sjgpxncn)) - The idea behind it is that a complex algorithm using big data from the past may not predict the future well in uncertain conditions and it therefore should be simplified ([View Highlight](https://read.readwise.io/read/01gp3chje8xd1afe6g6w8czxqa)) - Blind and rapid search through terabytes of data would be sufficient to predict epidemics. ([View Highlight](https://read.readwise.io/read/01gp3cz2tgn0hda8hffzsc8gsm)) - just increase volume, velocity, and variety and measure what correlates with what. ([View Highlight](https://read.readwise.io/read/01gp3czh0419n4pwmfgyx4qpv7)) - **Under uncertainty, keep it simple and don’t bet on the past** ([View Highlight](https://read.readwise.io/read/01gp3ck0x9ncy3mh4y37sbnpfx)) - people do not automatically rely on what they recently experienced, but only do so in unstable situations where the distant past is not a reliable guide for the future ([View Highlight](https://read.readwise.io/read/01gp3d0pazbcbaym5ehrnf7ss5)) - in an unstable world, reducing the amount of data and complexity can lead to more accurate predictions. ([View Highlight](https://read.readwise.io/read/01gp3d5p12wnhr58azc0sqsybn))