I can propose an approach which circumvents having to actually prove that maximal lottery-lotteries exist, but which preserves the spirit and works the same for voting theory purposes.
Epistemic status: raw thoughts. I think this approach is better than the previous attemps
The main idea is: we only really care about the probability each candidate is elected. So we can compute a sequence of maximal lottery-lotteries for approximations of and take the limit. The limit (most likely) will not be a maximal lottery-lottery, but the corresponding lottery will satisfy lottery Condorcet. It would also satisfy lottery independence if not for uniqueness issues, which I am really not sure I can fix. Because of this, the proofs are handwavy.
First, I think that the naïve defition of the lottery-Condorcet criterion is insufficient, because there might be ties related to indecision which break under infinitesimal perturbations in the voter base. This seems unnatural to me, so I propose to amend the criterion to something like this: (Placeholder) Definition: A candidate is a robust lottery-Condorcet winner if for all lotteries , it holds that for some and .
Then, we use the fact that if we perturb the voterbase, we will actually have a maximal lottery-lottery. Lemma: If admits a continuous probability density wrt the Lebesgue measure, then there exists a maximal lottery-lottery.
Proof: Analogously to the discrete case, we consider the zero-sum game between Alice and Bob, where the choices are , and the payoff is . Expessing the integral over the density, we see that it is continuous for . Then there exists a Nash equilibrium, which corresponds to a maximal lottery-lottery which has a continuous density.
Now, for any , we may consider a sequence of electorates, where and . They are constructed as convolutions of with the heat kernel . We denote the corresponding measures as . The important thing about them is that when in the weak-* topology, and that for , the measures admit continuous density wrt lebesgue.
The choice of pertubation with the heat kernel is pretty arbitrary, we could also relax to for , and get roughly the same results.
Then for each , there must be a maximal lottery-lottery and a corresponding measure on . Then, since is compact in the weak-* topology, the measures , after passing to a subsequence, weak-* converge to some measure . In particular, the probabilites in the resulting lottery converge as integrals of the coordinate functions on the probability simplex, so the lottery corresponding to is well-defined.
If there is a robust lottery-Condorcet winner , then, by the definition of robustness, it would also be a lottery-Condorcet winner for all electorates with small enough, so there is only one limit = .
Why does this satisfy lottery independence? Well, it does not, because the choice of the limiting measure is non-canonical. If there was always a unique , we could argue that the intermediate relaxations do satisfy lottery independence, and since lottery independence is a linear relation, the limit should satisfy it too. Which is not true. I feel that this should be fixable, but I don’t know how.
I suspect we need to do some sort of averaging. For example: We take the set of all limit lotteries . We take the convex hull CH of those. Then, we take the barycenter of the lebesgue measure on (if lies in a hyperplane, we take the lebesgue measure on this hyperplane). If we add a lottery candidate, then there will be an affine map between and , so the barycenters must coincide. Then we are done.
I can propose an approach which circumvents having to actually prove that maximal lottery-lotteries exist, but which preserves the spirit and works the same for voting theory purposes.
and take the limit. The limit (most likely) will not be a maximal lottery-lottery, but the corresponding lottery will satisfy lottery Condorcet.
is a robust lottery-Condorcet winner if for all lotteries , it holds that for some and .
admits a continuous probability density wrt the Lebesgue measure, then there exists a maximal lottery-lottery.
Epistemic status: raw thoughts. I think this approach is better than the previous attemps
The main idea is: we only really care about the probability each candidate is elected. So we can compute a sequence of maximal lottery-lotteries for approximations of
It would also satisfy lottery independence if not for uniqueness issues, which I am really not sure I can fix. Because of this, the proofs are handwavy.
First, I think that the naïve defition of the lottery-Condorcet criterion is insufficient, because there might be ties related to indecision which break under infinitesimal perturbations in the voter base. This seems unnatural to me, so I propose to amend the criterion to something like this:
(Placeholder) Definition: A candidate
Then, we use the fact that if we perturb the voterbase, we will actually have a maximal lottery-lottery.
Lemma: If
Proof: Analogously to the discrete case, we consider the zero-sum game between Alice and Bob, where the choices are , and the payoff is . Expessing the integral over the density, we see that it is continuous for . Then there exists a Nash equilibrium, which corresponds to a maximal lottery-lottery which has a continuous density.
Now, for any , we may consider a sequence of electorates, where and . They are constructed as convolutions of with the heat kernel . We denote the corresponding measures as . The important thing about them is that when in the weak-* topology, and that for , the measures admit continuous density wrt lebesgue.
to for , and get roughly the same results.
The choice of pertubation with the heat kernel is pretty arbitrary, we could also relax
Then for each , there must be a maximal lottery-lottery and a corresponding measure on . Then, since is compact in the weak-* topology, the measures , after passing to a subsequence, weak-* converge to some measure . In particular, the probabilites in the resulting lottery converge as integrals of the coordinate functions on the probability simplex, so the lottery corresponding to is well-defined.
If there is a robust lottery-Condorcet winner , then, by the definition of robustness, it would also be a lottery-Condorcet winner for all electorates with small enough, so there is only one limit = .
is non-canonical. If there was always a unique , we could argue that the intermediate relaxations do satisfy lottery independence, and since lottery independence is a linear relation, the limit should satisfy it too. Which is not true. I feel that this should be fixable, but I don’t know how.
. We take the convex hull CH of those. Then, we take the barycenter of the lebesgue measure on (if lies in a hyperplane, we take the lebesgue measure on this hyperplane). If we add a lottery candidate, then there will be an affine map between and , so the barycenters must coincide. Then we are done.
Why does this satisfy lottery independence? Well, it does not, because the choice of the limiting measure
I suspect we need to do some sort of averaging. For example:
We take the set of all limit lotteries