現代数学の系譜工学物理雑談古典ガロア理論も読む47

[過去ﾛｸﾞ] 現代数学の系譜工学物理雑談古典ガロア理論も読む47 (650ﾚｽ)
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52: 現代数学の系譜 雑談 古典ガロア理論も読む ◆e.a0E5TtKE  [sage] 2017/11/30(木) 22:28:15.89 ID:IqNIthYM

>>51 つづき

スレ45 https://rio2016.5ch.net/test/read.cgi/math/1508931882/472
472 自分返信：現代数学の系譜 工学物理雑談 古典ガロア理論も読む[sage] 投稿日：2017/11/06(月) 00:05:26.40 ID:1Au30FRy [6/13]

The strategy is as follows: Let ～ be the equivalence relation on functions from Ｒ to Ｒ defined by f ～ g iff for all but finitely many y, f(y) = g(y). Using the axiom of choice, pick a representative from each equivalence class.

In Step 2, choose x with uniform probability from [ 0,1 ].
When, in step 3, Bob reveals {(x0, f(x0)) ｜ x0 ≠ x }, you know what equivalence class f is in, because you know its values at all but one point. Let g be the representative of that equivalence class that you picked ahead of time. Now, in step 4, guess that f(x) is equal to g(x).

What is the probability of success of this strategy?
Well, whatever f that Bob picks, the representative g of its equivalence class will differ from it in only finitely many places.
You will win the game if, in Step 2, you pick any number besides one of those finitely many numbers.
Thus, you win with probability 1 no matter what function Bob selects.
(引用終り)

http://rio2016.5ch.net/test/read.cgi/math/1512046472/52

53: 現代数学の系譜 雑談 古典ガロア理論も読む ◆e.a0E5TtKE  [sage] 2017/11/30(木) 22:29:07.23 ID:IqNIthYM

>>52 つづき

スレ45 https://rio2016.5ch.net/test/read.cgi/math/1508931882/473
473 自分返信：現代数学の系譜 工学物理雑談 古典ガロア理論も読む[sage] 投稿日：2017/11/06(月) 00:08:48.04 ID:1Au30FRy [7/13]

先に私の見解を書いておくが、ピエロくんの紹介してくれた >>312 PDF が参考になるね（＾＾
The Mathematics of Coordinated Inference: A Study of Generalized Hat Problems (Developments in Mathematics) 2013 edition by Hardin, Christopher S., Taylor, Alan D.

これで、上記とちょっと違って、７章”The Topological Setting”とかなっていて、さすがに上記は、まずいということらしい。（＾＾

例えば、

P9
”In Chapter 7 we start to move further away from the hat problem
metaphor and think instead of trying to predict a function's value at a
point based on knowing (something about) its values on nearby points. The
most natural setting for this is a topological space and if we wanted to
only consider continuous colorings, then the limit operator would serve as
a unique optimal predictor. But we want to consider arbitrary colorings.
Thus we have each point in a topological space representing an agent and
if f and g are two colorings, then f ≡a g if f and g agree on some deleted
neighborhood of the point a. It turns out that an optimal predictor in this
case is wrong only on a set that is "scattered" (a concept with origins going
back to Cantor). Moreover, this predictor again turns out to be essentially
unique, and this is the main result in Chapter 8.”

などとある

さすれば、時枝もそのままじゃ（Topologicalな条件を加えないと）、成り立たないと思うがどう？（＾＾

以上

http://rio2016.5ch.net/test/read.cgi/math/1512046472/53

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