Where $F: [0,1] \to [0,1]$; along with $F(1)=1$. Do the field auto-morphism equations
$F(x+y)=F(x)+F(y) \quad\forall x\in\, [0,1]$
$F(xy)=F(x)F(y)\quad \forall x\in\, [0,1]$
Uniquely specify that $F(x)=x$, and that $F$ is an involution $F(F(x))=x$
I find that hard to completely believe, do these equations say anything precise about the value $F(x)$ where $x$ is transcendental number,? $\in [0,1]$.
Transcendental values, that is $\in [0,1]$ compatible with the probability calculus Which are presumably, all or, almost all such transcendental values in $[0,1]$.
Which would be entailed by $F(x)=x$) without any further regularity requirements and conversely as well except only monotonicity, (not even strict)mono-tonicity and not even $F(1)=1$.
If, so What about,along with
$$F:[0,1]→[0,1]$$;
$$F(1)=1$$
$$(1) \forall (x,y) \in [0,1];\, F(x+y)=F(x)+F(y)$$
Please see the comment by below.
Where merely (1) holds, without any further continuity, or regularity conditions explicitly added. Also note, that the domain is restricted to $[0,1]$ so when $x+y>1$ but $(x,y)\in dom(F)$ but $x+y \notin dom(F)$. Does this result still under this restriction. Its presumably implicit. The function is not defined for values of x+y>1 (it will nonetheless restrict these function sums, at least if x+y is rational $F(60)+F(70)=1.3$ nonetheless
Note
Please see comment below by Mohsen Shahriari
Please see the Azcel (1989) quotation (eq)18 and (2) corollary 9 ;
$$\text{eq}(2)\, \forall(x,y) \in \mathbb{R_{2}^{+}};\,G(x+y)=G(x)+G(y)$$
Where if $G:_\mathbb{R^{+}} \to R$ satisfies $(2)$ and is continuous at a point, or monotonic, or Lebesgue measurable, or bounded from one side on a set of positive measure, then there exists constants, c, such that $$(18): 'G(x)=cx\, \forall x\, \geq 0$$
"in particular if $(2)$ holds with '$g(x) \geq 0$'; then "$(18)$" holds, with $c\geq 0$ (Azcel 1989, page 18)
I presumed that the last sentence was meant to read that given:
'the regularity conditions(in bold) of corollary $(9)$ are satisfied (continuity at a point etc), and $G:_R+ \to \mathbb{R}$ satisfies $(2)$, then if, in addition $g(x) \geq 0$ holds, the general solution is the same, except that $c$ is restricted to be non-negative.
(18)'$$ \forall x \geq 0;\, G(x)=cx ,\quad\text{and} c \geq 0$$
In contrast, to $(19)$ id '$G:_R+\to R$' satisfies '$(2) \, {\&} g(x) \geq 0$' then '$x\geq 0\,;\,G(x)=cx; \,;c \geq 0$' holds full stop.
And that the continuity/regularity conditions are immediately satisfied.
Although, $(2)$ suggests that it needs to hold for all non-negative reals, I am not sure, but I presume that this remain valid if the Function is only defined on a particular non negative interval ie $[0,1]$ and that with relation to that relevant interval $(2)$ holds, ie here $[0,1]$.
If so, that would mean that any function $F:[0,1]→[0,1]$; would already satisfy $F(x)\geq 0$ as its co-domain is $[0,1]$?
Then, as its domain, is a non-negative real interval $[0,1]$ as well,ie $$(A)F:[0,1]→[0,1]$$
Then if $(1)$ and $(A)$ below hold; $(A)$ being Cauchy' equation over the entire domain then $F(x)=x$ immediately, with no further regularity or continuity requirements; not even mono-tonicity having being presumed for the irrational values: $$(1)F(1)=1$$
$$(2) \forall (x,y)\, \in [0,1]; F(x+y)=F(x)+F(y)$$.
Which are all for all elements of the non-negative real valued domain.
I think I must be missing something here, as this would appear to reduce $$F(x)=ax .;\,a \geq 0$$;
From being the unique continuous solution for a function, $F$ that satisfies Cauchy's equation, over its entire domain, whose domain is $[0,1]$ and co-domain is $[0,1]$, if in addition $F(1)=1$,. to the unique solution full stop.
As continuity is automatic, $F(x)=x$ over $[0,1]$changes to only solution (not merely the only unique solution ) solution>
Thus in the context of an infinite modal probability (canoncal simplex vector space) probability function representation.
That is, infinitely many probability spaces (the entirety of $\triangle^{2}$. The canonical probability 2-simplex)the entire convex hull of its three vertices, as a euclidean triangle $\in \mathcal{R}^{3}$ with vertices #(0,0,1), (1,0,0), (0,1,0)# etc) .
The set of all and only three outcome probability triples.
all ,and only all triples $\in [0,1]^3$, whose elements are $3$ non negative reals $\in [0,1]$ which sum to 1,$ \sum_{t=1}^{t=3}=1$ .
$\langle x_{v_{i}},y_{v_{i}},z_{v_{i}},\emptyset_{v_{i}}=0, \Omega_{v_{i}}=1 \rangle_{v_{i}};\quad \forall (v_{i} \in \mathcal([0,1]^{3}\cap \triangle^{2}):$.
$\forall(v_{i}\in [0,1]^3): x_{v_{i}}+ y_{v_{i}}+z_{v_{i}}=1$. $\forall(v_{i}\in [0,1]^3): x_{v_{i}},y_{v_{i}},z_{v_{i}}\in [0,1]$.
which gives $\triangle^{2}$.
and the function $F$ being such that: $\forall(v_{i}\in triangle^{2}):F(x_{v_{i}})+F(y_{v_{i}})+F(z_{v_{i}})=1$.
$\forall(v_{i}\in triangle^{2}): F(x_{v_{i}}),F(y_{z_{i}}),F(z_{v_{i}})\in [0,1]$.
$$\forall(v_{i} \in \triangle^{2}):\text {in the entire 2-probability canonical simplex} :F(\Omega_{v_{i}})=1 \land F(\emptyset_{v_{i}})=0$$.
$$\forall (v_{i}\in \triangle^{2}):\, \forall(e^{j}_{i} \in v_{i}): 0<e^{j}_{i}<1 \iff 0<F(e^{j}_{i})<1 $$.
$$ \land \text{ on vertices,e.g}:.$$
$v_{b1}=\langle0,0,1\,\rangle_{v_{b1}}=$. $\langle\,e^{1}_{v_{b1}}=0,e^{2}_{v_{b1}}=0,e^{3}_{v_{b1}}=1\,\rangle_{v_{b1}}$.
$v_{b3}=\langle0,0,1\,\rangle_{v_{b3}}=$. $\langle\,e^{1}_{v_{b3}}=0,e^{2}_{v_{b3}}=0,e^{3}_{v_{b3}}= 1\,\rangle_{v_{b3}}$. $v_{b2}=\langle0,1,0\,\rangle_{v_{b2}}=$. $\langle\,e^{1}_{v_{b2}}=0,e^{2}_{v_{b2}}=1,e^{3}_{v_{b2}}=0\,\rangle_{v_{b2}}$ :
$F(e^{2}_{v_{b2}}=1)=F(e^{1}_{v_{b3}}=1)=F(e^{3}_{v_{b1}})=1)=1$ .
$\land F(0)=0\text{for the other 6dom value=0 basis elements/events}$ .
whose individual events, are globally ordered, vector independent(everywhere). mo-dally (between vector), locally (within a vector), vector regardless of whether $x_{v_{j}} , x_{v_{k}} ,\, y_{v_{i}},\,z_{v_{m}}$ are elements of the same vector or not.
And event independent,regardless of whether the events are both are not both $x$'s etc $( x,y,z)$ connected.
And a probability function $F$ whose domain is the probability simplex.
Or numerical elements /coordinates,in the vectors in the simplex) and is strictly totally ordered numerically by the equivalence classes and domain values of each event in each vector and between each vector, probability vectors $v_{i}$. where the events are denoted by their point wise numerical domain value in the simplex .
The function only has sum to unity within a vector (vector dependent_) for all vectors) but the rank is context free, (basis independent) which will lead to :.
$\forall(v_{i},v_{j})\in\,\triangle^{2}:\forall(e^{t}_{j})\in \,v_{i}\,,t=[1..5]:\,\forall(e^{t}_{j})\in\,v_{j}\,,t\in [1..5]:$.
$$ \text{all probability vectors in simplex}.$$
$$\text{each element, in each vector denoted/defined by their probability/numerical coordinate value}.$$
$$e^{t_{[1..5]}}_{j}<e^{t_{[1..5]}}_{i}\,\iff\,F(e^{t_{[1..5]}}_{j}) < F(e^{t_{[1..5]}}_{i}).$$
$$e^{t_{[1..5]}}_{j} > e^{t_{[1..5]}}_{i}\,\iff\,F(e^{t_{[1..5]}}_{j}) < F(e^{t_{[1..5]}}_{i}).$$
$$e^{t_{[1..5]}}_{j}=e^{t_{[1..5]}}_{i}\,\iff F(e^{t_{[1..5]}}_{j}) = F(e^{t_{[1..5]}}_{i}).$$
$$\text{ie}$$. $x_{v_{i}}=e^{1}_{i},x_{v_{j}}=e^{1}_{j}$ $y_{v_{j}}=e^{2}_{i}, y_{v_{i}}=e^{2}_{j}\,,z_{v_{i}}=e^{3}_{i}$ $\,z_{v_{j}=e^{3}_{j}},\emptyset_{v_{i}}=e^{4}_{i}=0\,,$ $\emptyset_{v_{j}}=e^{4}_{j}=0$, $\Omega_{v_{i}}=e^{5}_{i}=1, \Omega_{v_{i}}=e^{5}_{j}=1).$
$$\text{ elements, atomic events + unit, and empty set)}.$$
$$\text{st. function is globally ordered by these values,regardless of whether those number/event, are members of the same vector} i=j\or \text{or not},\, i\neqj \text{or the same type of event super-scripts equal or not}$$.
Where'= '(means elements with the same numerical values s must have the same function value .
The elements can be distinct, but their domain value \in [0,1] is the same real number (these may be be a different event, and may be on a different vector but have the same simplex domain probability value and thus function range probability value).
$\if F(x)<F(y) $ are globally, locally and modall-y ranked point-wise, by the values in the canonical probability simplex, somewhat like QM ,event and vector independent.
but which are locally finite $dim\geq3 $ where $\text{dim=the same finite number of atomic events in each vector}.
Where the natural unit and bottom element on each space and on the vertices is:
$$\forall(v):F(\Omega_v)=1 \,\land F(\emptyset_{v})=0$$.
$$ \text{on vertices eg} \,, \langle1,0,0\rangle\,,F(1)=1,\land\, F(0)=0$$ .
And the domain and range of $F$ is $[0,1]$ a derivation of Cauchy's equation would grant linearity. Due to rank equalites disjoint nomramlization of both entities and global rank equalites vector independtnnoramlization of $F$ for disjoint events on the same vectors becomes bvector independent normalization between events $(1)$above becomes global and (to any arbitary three events, same vector or not)) $$(1)F(1-x-y)+F(x)+F(y)=1$$ $$F^{-1}(1-x-y)+F^{-1}(x)+F^{-1}(y)=1$$ $$x+y=z+m\iff F(x)+F(y)=F(z)+F(m)$$ $x+y>z+m\iff F(x)+F(y)>F(z)+F(m)$$ $$x+y1\iff F(x)+F(y)+F(z)>1$$
$$x+y = \frac{2}{3} \iff F(x)+F(y)=\frac{2}{3}$$ $$F(\frac{1}{3})=\frac{1}{3}$$ $$F(\frac{1}{2})<\frac{13}{24}$$ $${2}{3}>x>\frac{1}{3}\iff \frac{2}{3}<F(x)<\frac{1}{3}$$ $$\frac{1}{3}>x\geq\frac{1}{4} \iff frac{1}{3}>F(x)\geq\frac{11}{48}$$ \frac{2}{3}\geq x \iff frac{1}{3}\leq F(x)<\frac{1}{6}$$ etc
rational homogeneity and Cauchy's equation (deriving , and continuity (complete linearity) in one move, without further ado?
See Chapter 2 section 1 corollary 9,eq (18 on page(18) in
Aczel, Janos, Dhombres, J, Functional equations in several variables with applications to mathematics, information theory and to the natural and social sciences, Encyclopedia of Mathematics and its Applications 31. Cambridge: Cambridge University Press (ISBN 978-0-521-06389-0). xiii, 462 p. (2008). ZBL1139.39043.
In fact the first equation is enough. First use induction and prove that $ F \left( \frac 1 n \right) = \frac { F ( 1 ) } n $ and then note that since the value of $ F $ is always nonnegative, hence by the first equation, it's increasing which proves what was desired.