I’m not a savvy trader by any means, but this sets off warning bells for me that more savvy traders will find clever exploits in LVPMs. You can’t force anyone to abide by the spirit of the market’s question, they will seek the profit incentive wherever it lies.
Hmm I’m not sure what exploits you have in mind. Do you mean something like, they flip the meaning of the market around, and then people who later look at the market get confused and bet in the opposite direction of what they intended? And then the flipper can make money by undoing their bets?
Or do you mean something else?
I feel somewhat satisfied that the “question reversal” symmetry is ruled out by the market maker restricting the possible PDF space (in half along one specified dimension, IIUC).
Yep, in half along one specified dimension.
I’m curious how that would be practically implemented in payouts.
There’s a few different ways.
If someone bets to set the market state to a θ that flips the fixed dimension, the market implementation could just automatically replace their bet with −θ so it doesn’t flip the dimension. The payouts are symmetric under flips, so they will get paid the same regardless of whether they bet θ or −θ.
Alternatively you could just forbid predictions in the UI that flip the fixed dimension.
I worry about these other possible symmetries or rotations, which I can’t yet wrap my head around. I would love an illustrating example showing how they work and why we should or shouldn’t worry about them.
Flipping is the only symmetry that exists for unidimensional cases.
If you have multiple dimensions, you have a whole continuum of symmetries, because you can continuously rotate the dimensions into each other.
Thanks for being patient with my questions. I’m definitely not solid enough on these concepts yet to point out an exploit or misalignment. It would be super helpful if you fill out your LVPM playground page in the near future with a functional AMM to let people probe at the system.
Flipping is the only symmetry that exists for unidimensional cases.
If you have multiple dimensions, you have a whole continuum of symmetries, because you can continuously rotate the dimensions into each other.
What do you mean by unidimensional cases? So like if the binary LVPM Y is made up of binary markets X1 to XN , I would have called that an N-dimensional case, since the PDF is N-dimensional. How many symmetries and “possible convergences” does this Y have?
No problem, answering questions is the point of this post. 😅
It would be super helpful if you fill out your LVPM playground page in the near future with a functional AMM to let people probe at the system.
The playground page already has this. If you scroll down to the bottom of the page, you will see this info:
To play with the payout and get an intuition for it, you can use the checkboxes below. Your bets have currently cost proportional to 0 Mana, and if the outcome below happens, you get a payout proportional to 0 Mana, for a total profit proportional to 0 Mana.
Japan hyperinflation by 2030?
US hyperinflation by 2030?
Ukraine hyperinflation by 2030?
If you make bets, then the numbers in this section get updated with the costs and the payouts.
But I guess my interface probably isn’t very friendly overall, even if it is there. 😅
What do you mean by unidimensional cases? So like if the binary LVPM Y is made up of binary markets X1 to XN , I would have called that an N-dimensional case, since the PDF is N-dimensional. How many symmetries and “possible convergences” does this Y have?
I am talking about the dimensionality of Y, not the dimensionality of →X.
In the markets I described in the post and implemented for my demo, Y is always one-dimensional. However, it is possible to make more advance implementations where Y can be multidimensional. For instance in the uncertainty about the outcome of the Ukraine war, there is probably not just a Ukraine wins <-> Russia wins axis, but also a war ends <-> war continues axis.
Hmm I’m not sure what exploits you have in mind. Do you mean something like, they flip the meaning of the market around, and then people who later look at the market get confused and bet in the opposite direction of what they intended? And then the flipper can make money by undoing their bets?
Or do you mean something else?
Yep, in half along one specified dimension.
There’s a few different ways.
If someone bets to set the market state to a θ that flips the fixed dimension, the market implementation could just automatically replace their bet with −θ so it doesn’t flip the dimension. The payouts are symmetric under flips, so they will get paid the same regardless of whether they bet θ or −θ.
Alternatively you could just forbid predictions in the UI that flip the fixed dimension.
Flipping is the only symmetry that exists for unidimensional cases.
If you have multiple dimensions, you have a whole continuum of symmetries, because you can continuously rotate the dimensions into each other.
Thanks for being patient with my questions. I’m definitely not solid enough on these concepts yet to point out an exploit or misalignment. It would be super helpful if you fill out your LVPM playground page in the near future with a functional AMM to let people probe at the system.
What do you mean by unidimensional cases? So like if the binary LVPM Y is made up of binary markets X1 to XN , I would have called that an N-dimensional case, since the PDF is N-dimensional. How many symmetries and “possible convergences” does this Y have?
No problem, answering questions is the point of this post. 😅
The playground page already has this. If you scroll down to the bottom of the page, you will see this info:
If you make bets, then the numbers in this section get updated with the costs and the payouts.
But I guess my interface probably isn’t very friendly overall, even if it is there. 😅
I am talking about the dimensionality of Y, not the dimensionality of →X.
In the markets I described in the post and implemented for my demo, Y is always one-dimensional. However, it is possible to make more advance implementations where Y can be multidimensional. For instance in the uncertainty about the outcome of the Ukraine war, there is probably not just a Ukraine wins <-> Russia wins axis, but also a war ends <-> war continues axis.