Nice observation. I’m certainly not meaning to advocate for Shapley value—this was largely an attempt to adjust my negative attitude about Shapley value’s flaws, and the attempt was not very successful, but I thought it would be useful to others struggling to understand it, as I was.
I can imagine a way to address the probability issue you raise could be to create probabilistic value entries, where, let’s say we add the participants in the ratio they exist in reality, so pretending there are twice as many nurses as doctors, you could fill out values for a 3 person coalition (2 nurses, 1 doctor), with a value of 10 awarded to any coalition that satisfies a successful surgery (at least one doctor and one nurse). This results in the nurses being awarded a Shapley Value of 1.67 and the doctor being awarded 6.67.
This hack would give a reasonable answer, in this limited situation. But the oxygen:match-strike would lead to a computationally prohibitive outcome given that this hack would require a massive increase in coalitions, leading to exponentially massive computation required.
Thanks for your comments. I might incorporate the empty coalition actually, thank for alerting me to that.
Adding such virtual players is an interesting idea. I guess it should be possible to properly add probability weighting, without virtual players, once one has fully understood the reason for Shapley’s permutation method of weighting. (Which I have not.)
Another thing I noticed: It seems one can generalize a “coalition” of “players” as a conjunction of propositions, where the propositions of the players not participating in the coalition are negated. So for players/propositions A, B, C, the coalition {A, C} would correspond to the proposition A∧¬B∧C. The Shapley value of a player would simply be the value of a proposition, like A. Since coalitions are just propositions as well, this has the (intuitive?) consequence that coalitions can also be players.
Furthermore, instead of talking about “values” one could talk about “utility”, as in expected utility theory. There is actually a formalization of utility theory, by Richard Jeffrey, which applies to a Boolean algebra of propositions (or “events”, as they are called in probability theory). So theoretically it should be possible to determine which restrictions placed on a Jeffrey utility function are equivalent to Shapley’s formula. Which could, presumably, aid understanding the assumptions behind it, and help with things like integrating probabilities into the Shapley formalism, since those are already integrated into Jeffrey’s theory.
Thanks Cubefox, very interesting ideas, I like the idea of generalising a coalition so that it can be treated as a player, that seems to make a lot of practical sense, I’ll look into Jeffrey to try and get my head around that.
If you want to look into Jeffrey, his utility theory is in his book “The Logic of Decision”, second/1983 edition, sections 4.4 and 5.5 to 5.9. It’s less than 10 pages overall, the rest isn’t really necessary.
Nice observation. I’m certainly not meaning to advocate for Shapley value—this was largely an attempt to adjust my negative attitude about Shapley value’s flaws, and the attempt was not very successful, but I thought it would be useful to others struggling to understand it, as I was.
I can imagine a way to address the probability issue you raise could be to create probabilistic value entries, where, let’s say we add the participants in the ratio they exist in reality, so pretending there are twice as many nurses as doctors, you could fill out values for a 3 person coalition (2 nurses, 1 doctor), with a value of 10 awarded to any coalition that satisfies a successful surgery (at least one doctor and one nurse). This results in the nurses being awarded a Shapley Value of 1.67 and the doctor being awarded 6.67.
This hack would give a reasonable answer, in this limited situation. But the oxygen:match-strike would lead to a computationally prohibitive outcome given that this hack would require a massive increase in coalitions, leading to exponentially massive computation required.
Thanks for your comments. I might incorporate the empty coalition actually, thank for alerting me to that.
Adding such virtual players is an interesting idea. I guess it should be possible to properly add probability weighting, without virtual players, once one has fully understood the reason for Shapley’s permutation method of weighting. (Which I have not.)
Another thing I noticed: It seems one can generalize a “coalition” of “players” as a conjunction of propositions, where the propositions of the players not participating in the coalition are negated. So for players/propositions A, B, C, the coalition {A, C} would correspond to the proposition A∧¬B∧C. The Shapley value of a player would simply be the value of a proposition, like A. Since coalitions are just propositions as well, this has the (intuitive?) consequence that coalitions can also be players.
Furthermore, instead of talking about “values” one could talk about “utility”, as in expected utility theory. There is actually a formalization of utility theory, by Richard Jeffrey, which applies to a Boolean algebra of propositions (or “events”, as they are called in probability theory). So theoretically it should be possible to determine which restrictions placed on a Jeffrey utility function are equivalent to Shapley’s formula. Which could, presumably, aid understanding the assumptions behind it, and help with things like integrating probabilities into the Shapley formalism, since those are already integrated into Jeffrey’s theory.
Of course this would probably be a lot of work...
Thanks Cubefox, very interesting ideas, I like the idea of generalising a coalition so that it can be treated as a player, that seems to make a lot of practical sense, I’ll look into Jeffrey to try and get my head around that.
If you want to look into Jeffrey, his utility theory is in his book “The Logic of Decision”, second/1983 edition, sections 4.4 and 5.5 to 5.9. It’s less than 10 pages overall, the rest isn’t really necessary.
Brilliant, thanks.