# On the value of anecdotes

What is better, if everyone is wrong about the same 2% of facts, or if everyone is wrong about a different 4% of facts? Depending on how you answer this question, you should act in very different ways. I’ll take vegan advocacy as an example, but the question can be applies more generally.

If you’re in the first group, you would prefer a scientific data-driven approach. You would experiment with many different approaches to advocacy, analyse the data to find the single best way of doing outreach, and make everyone in vegan activism aware that this is the best way to do it.

If you prefer the 2% case, a local algorithm is the way to go. Think about what drove you to become vegan, and continue this strategy. If you were shocked into becoming vegan by a cube of truth, you should be participating in cubes of truth. If you became vegan after your friendly vegan neighbour exemplified that veganism is a pretty normal lifestyle and they allowed you to pick their brain about why they became vegan themselves, then you should become the friendly vegan acquaintance of the people you know yourself.

One interesting question if you enact the local algorithm, is how to weigh anecdotes. The local algorithm described above only considers your data; one alternative algorithm is to use the approach that was effective on the majority of your direct friends that became vegan before you. Another algorithm looks at all your the friends of your friends, or everyone within distance 3 in the friendship-graph. If everyone is connected by everyone by a friendship-path of length 6, then the distance 6 algorithm is exactly the data-driven approach from the second paragraph.

Evolutionary theory suggests that the small-distance algorithms are effective, for the best outreach strategy will eventually out-compete all others. But for the distance 0 or 1 cases, you’re basically working on anecdotal evidence. I’m not sure anymore what the correct value is to place on anecdotes.