The Best of LessWrong

In early 2020, COVID-19 was spreading rapidly, but many people seem hesitant to take precautions or prepare. Jacob Falkovich explores why people often wait for social permission before reacting to potential threats, even when the evidence is clear. He argues we should be willing to act on our own judgment rather than waiting for others. 

Zvi explores the four "simulacra levels" of communication and action, using the COVID-19 pandemic as an example: 1) Literal truth. 2) Trying to influence behavior 3) Signaling group membership, and 4) Pure power games. He examines how these levels interact and different strategies people use across them.

Most Prisoner's Dilemmas are actually Stag Hunts in the iterated game, and most Stag Hunts are actually "Schelling games." You have to coordinate on a good equilibrium, but there are many good equilibria to choose from, which benefit different people to different degrees. This complicates the problem of cooperating.

Dogmatic probabilism is the theory that all rational belief updates should be Bayesian updates. Radical probabilism is a more flexible theory which allows agents to radically change their beliefs, while still obeying some constraints. Abram examines how radical probabilism differs from dogmatic probabilism, and what implications the theory has for rational agents.

The path to explicit reason is fraught with challenges. People often don't want to use explicit reason, and when they try to use it, they fail. Even if they succeed, they're punished socially. The post explores various obstacles on this path, including social pressure, strange memeplexes, and the "valley of bad rationality".

The felt sense is a concept coined by psychologist Eugene Gendlin to describe a kind of a kind of pre-linguistic, physical sensation that represents some mental content. Kaj gives examples of felt senses, explains why they're useful to pay attention to, and gives tips on how to notice and work with them.

If you know nothing about a thing, the first example or sample gives you a disproportionate amount of information, often more than any subsequent sample. It lets you locate the idea in conceptspace, get a sense of what domain/scale/magnitude you're dealing with, and provides an anchor for further thinking.