D&D 5E Who tried to end the OGL?


The only thing I have to say in reply to this:

This is not true. The vast majority of people, regardless of their opinion, don't say anything at all. That's why they're the silent majority. Whether they're happy or unhappy, appreciative or annoyed, whatever--we don't know because they don't talk.

Unless and until we get actual data, we can say nothing meaningful at all about them. That's why I always criticize the poor survey and poll design WotC puts out. (It used to be absolute garbage poll and survey design, they've stepped up to merely low-quality in the years since "D&D Next.")
While we don't know it is true in this specific case, there is, IIRC, quite a bit phycological research that suggest that it is generally true for modern humans.

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Follower of the Way
While we don't know it is true in this specific case, there is, IIRC, quite a bit phycological research that suggest that it is generally true for modern humans.
It is not exclusively people who have a negative opinion who speak up. It is people who have strong opinions. In both directions.

The vast majority of people don't have strong opinions. That doesn't mean they necessarily think good of things. Their opinions just aren't strong enough to speak out. Consider, for example, the many, many, many people out there who stick with a job they hate, or a broken and unhealthy relationship, or various other things, not because they want to, but because it's contrasted against uncertainty and possible loss.

"Governments long established should not be changed for light and transient causes; and accordingly all experience hath shewn, that mankind are more disposed to suffer, while evils are sufferable, than to right themselves by abolishing the forms to which they are accustomed."

And, to be clear, this doesn't mean their opinions are bad either. They're just not strong enough to speak out either way. It is patently obvious that 5e was designed by only one slice of the existing community when it was called "D&D Next." That slice is now VASTLY overwhelmed by the new blood in the hobby. It is foolishness itself to presume that that new body is perfectly represented by the original sample set. New data should be collected--and not just the terrible surveys WotC has been running. Actually representative and effective survey design.


What's your favorite text/review paper on causal inference? (It's been on my list of things to read up on for a couple years now).

Finally got back to this tangent...

"Causal Inference" is currently one of the hot topics in statistics and related fields. The big focus is examining what can be done if you don't have an experiment, but can make some other assumptions. Like most things in statistics it will probably be horribly misused in some cases. Anyway, these are four things to look at that I've added to my stack to skim.

A 2022 interview/history overview with Judea Pearl is at https://ftp.cs.ucla.edu/pub/stat_ser/r523.pdf . Pearl is a computer scientist and phiolosopher at UCLA.

A chatty, recent, online, intro book on it is Causal Inference for the Brave and True (using Python and memes) at Causal Inference for The Brave and True — Causal Inference for the Brave and True . It still assumes you've had some intro stats with expected value notation, but has a chapter on the graphical models mentioned in the two above. It's intro notes 'Your parents have probably repeated to you that “association is not causation”, “association is not causation”. But actually, explaining why that is the case is a bit more involved. This is what this introduction to causal inference is all about. As for the rest of this book, it will be dedicated to figuring out how to make association be causation.' I can't vouch for it, but didn't find anything easily online torching it and saw several liking it. (I'm partially interested in it to see how they would teach it to students with the background it is aimed at).

A 2010 introduction to Causal Inference is at:
Scheines is a philosophy prof at Carnegie Mellon. His 2000 book with Spirtes and Glymour is widely cited.

This is a 2015 review of some basics of Causal Inference. It appeared in the Handbook of Big Data. https://arxiv.org/pdf/1506.07669
Maathuis is a stats prof at ETH Zurich who specializes in Causal Inference and Nandy is a data scientist at Google.
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