As Joe Biden cleared 270 last week, some people remarked on how different the narrative would’ve been had the votes been counted in a different order: It's staggering to think about how differently PA would be viewed/covered right now if the EDay/mail ballots were being counted in the opposite order. — Dave Wasserman (@Redistrict) November 5, 2020 The idea that order shouldn’t affect your final take is a classic criterion of rationality.... Read more

This is post 3 of 3 on simulated epistemic networks (code here): The Zollman Effect How Robust is the Zollman Effect? Mistrust & Polarization The first post introduced a simple model of collective inquiry. Agents experiment with a new treatment and share their data, then update on all data as if it were their own. But what if they mistrust one another? It’s natural to have less than full faith in those whose opinions differ from your own.... Read more

This is the second in a trio of posts on simulated epistemic networks: The Zollman Effect How Robust is the Zollman Effect? Mistrust & Polarization This post summarizes some key ideas from Rosenstock, Bruner, and O’Connor’s paper on the Zollman effect, and reproduces some of their results in Python. As always you can grab the code from GitHub. Last time we met the Zollman effect: sharing experimental results in a scientific community can actually hurt its chances of arriving at the truth.... Read more

I’m drafting a new social epistemology section for the SEP entry on formal epistemology. It’ll focus on a series of three papers that study epistemic networks using computer simulations. This post is the first in a series of three explainers, one on each paper. The Zollman Effect How Robust is the Zollman Effect? Mistrust & Polarization In each post I’ll summarize the main ideas and replicate some key results in Python.... Read more