Probability theory, understood meta-rationally
Ask: how and why to use it; and whether some other approach to uncertainty would be better.
Leveling up technical work with context and purpose
Ask: how and why to use it; and whether some other approach to uncertainty would be better.
A meta-rational organization may appear chaotic (although productive and innovative), until you notice how smoothly routine rational work gets done.
It's natural to react to meta-rationalism with skepticism or hostility initially. You may come to delight in it.
“The map is not the territory”—what is it then? How do rational models actually work?
The epistemological categories—truth, belief, inference—are richer, more complex, diverse, and nebulous than rationalism supposes.
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We accomplish reference by any means necessary: observable, improvised work that makes it clear what we are talking about in context.
Peculiar features of language make sense as tools to enable collaboration, rather than to express objective truths.
We actively work to perceive aspects of the world as meaningful, in terms of our purposes, in context.
Routine activity usually goes smoothly overall, despite frequent minor glitches, because we have methods for repairing trouble.
Accountability is the key concept in understanding mere reasonableness, as contrasted with systematic rationality.
Understanding concrete, purposeful activity is a prerequisite to understanding the formal rationality that depends on it.
A summary explanation of everyday reasonable activity, with a tabular guide and a concrete example.
A dramatic perspective shift: understanding rationality as dependent on mere reasonableness to connect it with reality.
The Eggplant is neither cognitive nor science, although it seeks a better understanding of some phenomena cognitive science has studied.
Everyday reasonableness is the foundation of technical, formal, and systematic rationality.
Rationalist theories of action try to deduce optimal choices from true beliefs. This is rarely possible in practice.
The mistaken belief that statistical methods can tell you what to believe drove the science replication crisis.
A thought experiment shows why probability theory and statistics cannot address uncertainty in general.
If probability theory were an epistemology, we’d want it to tell us how confident to be in our beliefs. Unfortunately, it can’t do that.