VUE Notes PDF

Summary

These are notes covering topics in cognitive psychology related to system 1 and system 2 thinking, and their influence on prediction and forecasting processes. It includes examples to illustrate the discussed concepts. These documents cover different types of biases, which affect our predictions, and are useful to gain a better insight into how human beings think and make predictions.

Full Transcript

SYSTEM 1 (fast): - Thinking fast allows to move quickly without really without thinking about it (+) - Taking a long time to analyze can allow you to be wrong (-) Example: WISELY; reacting to fire and putting water on it to put it out UNWISELY; being influenced by peer pressure because...

SYSTEM 1 (fast): - Thinking fast allows to move quickly without really without thinking about it (+) - Taking a long time to analyze can allow you to be wrong (-) Example: WISELY; reacting to fire and putting water on it to put it out UNWISELY; being influenced by peer pressure because everyone in the moment is telling you to do something where you know is bad but you want to be exempted SYSTEM 2 (slow): What we take the time to think about, actively really thinking about it - It will take more time (-) Example: WISELY; studying and taking time to slow down and really thinking about what your studying UNWISELY; hearing tornado sirens and believing that there's never actually a tornado so you ponder if you should go in the basement With emotion; you can feel it and you are acting and reacting - This can be a situation within a forecast such as telling people to evacuate in a stressful situation and making a “wrong” prediction COVID The predictions were good, but there was a failure to act on guidance 2016 ELECTION LESSONS 538: - Trump was not predicted to win but we still beat Hilliary - People were misinterpreting when trump actually ended up winning; and people were shocked WISDOM OF CROWDS What's good? - Having a sense of belonging - People can think independently but to work together to balance off ideas What’s bad? - No independent thinking; losing value in sharing ideas HOW CAN MAKE PREDICTIONS BETTER - Thinking probabilistic SIGNAL VS NOISE What is the signal: - Rolling dice, What is the noise: The gamblers; you are stuck on trying to get a certain number on the dice; a signal day Sports, Concerts, and Final Exams 80% Signal & 20% Noise Signal; Preparing: - Practicing hard, studying hard Noise; Hiccups; - So nervous for exam you forget information, having an off day during game day HOW CAN THESE CONCEPTS INFLUENCE THE FORECAST - Baseline odds What they start off with; their first instinct what the forecast will be How much snow will La Porte get from lake effect due to past evidence of lake effect event; what is their average; gotta have a little wiggle room in what is gonna happen - Laplace and Lorenz Butterfly effect; saying it’s gonna rain people will expect to rain and it will just be a constant domino effect Days before a eventful event it was seemed to be a bad storm; a day before it still looks like bad storm - Hindsight bias Knew it all along from past events If you're only looking in the bad events; you're gonna be overly confident in a forecast - Entertainment Value Pleasure for people There could be an incentive to exaggerate a forecast; the height can give people a false expectation - 2016 election forecast Even though that there are certain predictions that were expected, the complete opposite of what the prediction had happened - Wisdom of crowds The idea that groups of people are smarter than individual experts The forecast from the meteorologist will collaborate with each other to make their own assignments off ideas - Fast and slow thinking fast :looking at maps very quickly and making mental note of past forecast and stopping the thought right there - Probabilistic thinking Trying to estimate using some tool of math and logic Are forecasters thinks about the most likely outcome; but then they will look at the disruptions to happen and how confident are you; what are the odds it goes west - Signal and Noise Signal- Noise- Within the forecast models use to look at all the models to be ran Keying in on one result might blow the forecast because they aren’t giving all the options to look at for probabilistic options

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