Scott Alexander warns against forming strong heuristics based on limited data, using examples from AI research, elections, and campaign strategies to illustrate the pitfalls of this approach. Longer summary

Scott Alexander discusses the dangers of forming strong heuristics based on limited data points. He presents three examples: AI research progress, election predictions, and campaign strategies. In each case, he shows how people formed confident heuristics after observing patterns in just one or two instances, only to be surprised when these heuristics failed. The post argues against treating life events as moral parables and instead advocates for viewing them as individual data points that may not necessarily generalize. Scott uses a mix of statistical reasoning, historical examples, and cultural references to illustrate his points. Shorter summary

Scott Alexander evaluates his 2016 predictions, finding good overall calibration with slight underconfidence at 70% probability, consistent with previous years. Longer summary

Scott Alexander reviews his predictions for 2016, comparing them to actual outcomes. He lists predictions for world events and personal/community matters, marking false predictions with strikethrough and true ones intact. He then calculates his accuracy for different confidence levels, finding he was generally well-calibrated but slightly underconfident at 70% probability. He compares this year's results to previous years, noting a similar pattern of underconfidence in medium probabilities. Overall, he considers his 2016 predictions successful and promises predictions for 2017 soon. Shorter summary

Scott Alexander argues that the 2016 US election outcome shouldn't drastically change our understanding of politics, given how close the race is. Longer summary

Scott Alexander argues that the outcome of the 2016 US presidential election shouldn't dramatically change our understanding of politics and society. He criticizes both extreme predictions of a certain Hillary Clinton victory and a certain Donald Trump victory, pointing out that the race is close enough that the outcome could be determined by random factors like weather. Alexander suggests that people should precommit to their views on politics and society rather than drastically changing them based on the election result. He uses his own January 2016 prediction of Trump having a 20% chance of winning (conditional on winning the Republican primary) as an example of a reasonable prediction, given that prediction markets on election eve give Trump an 17.9% chance. Shorter summary