niplav @niplav@schelling.pt

Things that are a fact in ℝ³ but not in ℝ²:

* You can embed any graph
* Random walk never returns to origin (in expectation)

Any others?

harberger taxes and land-value taxes are in P, Pigou taxes are in NP

what's the equivalent to the flicker fusion threshold for hearing/tasting/smelling?

“danger lives at my hairtips” she pouted and downed the day-after pill with two finger widths of tequila

In the context of making inconsistent preferences consistent, these are fairly strong results.

Not sure about their approximation behavior, but I think this makes becoming a coherent agent very difficult.

• PCS and all c∈C have minimum graph edit distance of i. (Proof sketch: There is a graph for which all acyclic tournaments with the same (minimal) graph-edit distance don't contain a specific subgraph). Graph in picture, the minimal edit distance is 3, the non-preserved consistent subgraph is a2→a4. This extends to arbitrarily big consistent subgraphs (replace all edges with acyclic tournaments with n nodes).

Then you have the following impossibilities:

• PCS and f has polynomial runtime. (Proof sketch: finding an s of size k is in NP. Finding all of them is in NP as well.)
• PCS and C has polynomial size. (Proof sketch: You can construct a graph with exponentially many acyclic tournaments as subgraphs).

Let f be a function that takes as input an inconsistent preference i and produces a set C of consistent preferences. Let S be the set of subgraphs of i for which it holds that for every s∈S: s is an acyclic subgraph, s is a subgraph of i, and s is maximal in i for those properties.

S shall contain all s that fulfill the above conditions.

Let PCS be the property that for every s∈S, s is a subgraph of at least one c∈C (every consistent subpreference appears at least once in a consistent version)

Pearl is of course the GOAT, but the book doesn't look like the thing I'm looking for (not quite practical enough)

I kind of want to learn Bayesian Statistics real bad

But like the practical kind you can actually use to calculate likelihood ratios of experiments

Any reccs for textbooks? Ideally with code[1] *and* exercises

[1]: Best of Julia, 2nd best if Python, I don't wanna learn R :-/

Queering Hume's Guillotine: Ontological Crises, von Neumann-Morgenstern-inconsistent Preferences and Two Impossibility Theorems

Functional Decision Theory has a WP page

I should do more forecasting again

the answer to the Needham question is that China glorified wordcels too much