[EM] Exact spatial model probabilities?

[Forest Simmons]

I should probably give more of an explanation for the choice of the atomic
electo-potential, i.e. the scalar potential function V(r) for one voter
located at R:

V(r)=||r-R||

The force=-grad V(r) represents the influence of the voter pushing or
pulling in one direction or another. The true winner should be at a point
where the resultant force of the entire electorate is balanced at as close
to zero as possible.

So what is the force of one voter under this choice of potential V(r)?

It is -grad V(r) anchored at the voter position R.

-grad|r-R|||=-(r-R)/||r-R||, which is a unit vector pointing from r to R.

In other, words each voter exerts a pull of one dyne towards herself in the
tug of war to find the winning position.

One voter one vote translates to one voter one dyne. Each voter is allotted
 the same amount of pull ... but different
voters pull in different directions. Sincere voters pull towards their own
location in in voter space.

If there exists a unique equilibrium point where all of the pulls balance,
that point is the geometric median of the voter distribution.

It is easy to see that in any distribution of voters along a straight line,
this balance occurs where r is at the ordinary median of the
didtribution,with half of the voters pulling with same strength in each
direction.

That's the heuristic for the choice of "electo-potential" ... one person,
one dyne of pull towards their own position.

El mié., 26 de ene. de 2022 10:23 a. m., Forest Simmons <
forest.simmons21 at gmail.com> escribió:

> Just one correction ****
>
> El mié., 26 de ene. de 2022 10:02 a. m., Forest Simmons <
> forest.simmons21 at gmail.com> escribió:
>
>>
>>
>> El mié., 26 de ene. de 2022 3:12 a. m., Kristofer Munsterhjelm <
>> km_elmet at t-online.de> escribió:
>>
>>> On 26.01.2022 04:23, Forest Simmons wrote:
>>>
>>> > The dirac delta is the convolution identity distribution ... convolving
>>> > it with another distribution leaves it unchanged with cliffs and sharp
>>> > corners intact.
>>> >
>>> > But if you convolve with a smooth approximation of Dirac,  like a
>>> > gaussian with tiny variance, you get an infinitely differentiable
>>> > approximation of the "horrible" function.
>>>
>>> Right. I once wrote a fully differentiable genetic algorithm (I was
>>> intending to use it for hyperparameter tuning). There were two problems.
>>> First, local optima. Second, suppose that you have a plain statement
>>> like:
>>>
>>> if (x>y) {
>>>         return z;
>>> } else {
>>>         return f(z);
>>> }
>>>
>>> When smoothing, this turns into something like
>>>
>>> return z * sig(x-y, k) + f(z) * sig(y-x, k)
>>>
>>> where sig(x, k) is a sigmoid function that returns 1 if x>>0, 0 if x<<0,
>>> and k is a steepness parameter controlling sig'(x) around 0.
>>>
>>> If k is too high, then gradient descent fails because there's no
>>> noticeable slope down to the optimum; it's flat almost everywhere and
>>> then goes to zero exactly at the optimum (vanishing gradient problem).
>>>
>>> So we need some smoothness, which means that both branch values come
>>> into play. But this makes the function slow to evaluate as the program
>>> must "go both ways" on every conditional.
>>>
>>
>> Here's a better way to get a smooth potential function V(x,y,z,) in the
>> present context:
>> First the potential V(x,y,z)for all voters concentrated at one point
>> (X,Y,Z):
>>
>> V(r)=||r-R||, where r=(x,y,z) and R=(X,Y,Z).
>>
>> To smooth, replace this with
>>
>> V(r)=(epsilon+||r-R||^2)^(1/2)
>>
>> This is more realistic anyway because you cannot truly have all voters
>> concentrated at one point. The epsilon smear factor would be a function of
>> the variance of the voters clustered around the point R.
>>
>> That variance could be given exactly for a uniform distribution on a ball
>> of radius delta centered at R, for example.
>>
>> Then for multiple factions...
>>
>> V(r)=Sum(over R in Omega) of
>> f(R)*sqrt(epsilon(R)+||R-r||^.5),
>>
>> where Omega is the set of faction centers, and f(R) is the fraction of
>> voters clustered near R, i.e. in a delta(R) [related to epsilon(R)]
>> neighborhood of R.
>>
>> If we have a smooth (i.e. pre-smeared) pdf f(R), we can write the
>> "electo-potential" V(r) as an integral
>> V(r)=Integral(over R in Omega)of
>>                ||r-R||f(r)dxdydz
>>
>> The hamiltonian for the wandering voter particle is the total energy,
>> kinetic + potential, E=T+V, where T =.5 ||dR/dt||^2.
>>
>> The system (in vector form) of ODE's for the motion of the particle is
>>
>> (d/dr)^2=-grad V(r)
>>
> *** Should be
> (d/dt)^2=-grad V(r)
>
>>
>> I need to check the HMC link that Daniel gave me to see what notation
>> they are using.
>>
>>>
>>> > Electrical engineers have a vast library of standard test patterns to
>>> > use as input signals for use in designing and testing circuits.
>>> >
>>> > We need a similar library of test distributions for use in designing
>>> and
>>> > testing election methods.
>>> >
>>> > Election methods could even be profiled by their responses to these
>>> test
>>> > patterns.
>>>
>>> This, I do agree with. There have been a lot of voting method proposals
>>> lately, and we need some way to easily determine:
>>>
>>> - what is its VSE (under what models)
>>> - what is its voter-strategy susceptibility
>>> - what is its candidate-strategy susceptibility (cloning)
>>> - what criteria does it definitely fail
>>>
>>> and it would be nice to also know what criteria it definitely passes,
>>> though that requires formal verification, which is much harder than just
>>> testing a bunch of cases.
>>>
>>> (I remember using REDLOG to come up with BTV once, but I don't remember
>>> the details. Perhaps I should look into the current state of the art for
>>> provers, like Z3... so many things to do and so little time in which to
>>> do them!)
>>>
>>> -km
>>>
>>
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