Update Shapley.md
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@ -31,10 +31,10 @@ ShapleyValue(Leibniz) = 1/2 * [ (Value of adding Lebniz to coalition {} / Number
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If the value of creating calculus is 1, then:
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Value of adding Lebniz to coalition {} = 1
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Number of coalitions excluding Leibniz of the same size as {} = 1
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Value of adding Lebniz to coalition {Newton} = 0
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Number of coalitions excluding Leibniz of the same size as {Newton} = 1
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Value of adding Lebniz to coalition {} = 1
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Number of coalitions excluding Leibniz of the same size as {} = 1
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Value of adding Lebniz to coalition {Newton} = 0
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Number of coalitions excluding Leibniz of the same size as {Newton} = 1
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Value(Leibniz) = 1/2 * (1/1 + 0/1) = 1/2
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@ -63,15 +63,15 @@ If the project gets completed, what is the value of each player?
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Value(AS) = 1/3 * [(Value of adding him to {} / 1) + (Value of adding him to {DB}/2) + (Value of adding him to {MS}/2) + (Value of adding him to {MS, DB}/1) ]
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {DB} = 0
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Value of adding him to {MS} = 0.4 ## The mad scientist tries to talk to him, but he's not very convincing
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {MS, DB} = 1
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Number of coalitions excluding him of size 2 = 1
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {DB} = 0
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Value of adding him to {MS} = 0.4 // The mad scientist tries to talk to him, but he's not very convincing
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {MS, DB} = 1
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Number of coalitions excluding him of size 2 = 1
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Value(AS) = 1/3 * (0/1 + 0/2 + 0.4/2 + 1/1) = 0.4
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Value(AS) = 1/3 * (0/1 + 0/2 + 0.4/2 + 1/1) = 0.4
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### David Burns
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@ -81,13 +81,13 @@ If David Burns writes the book, but there is nobody, his impact is 0. If he exis
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Value(DB) = 1/3 * [(Value of adding him to {} / 1) + (Value of adding him to {AS}/2) + (Value of adding him to {MS}/2) + (Value of adding him to {MS, AS}/1) ]
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {AS} = 0
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Value of adding him to {MS} = 0
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {MS, AS} = 1-0.4 = 0.6 ## These are MARGINAL values.
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Number of coalitions excluding him of size 2 = 1
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {AS} = 0
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Value of adding him to {MS} = 0
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {MS, AS} = 1-0.4 = 0.6 // These are MARGINAL values.
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Number of coalitions excluding him of size 2 = 1
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Value(AS) = 1/3 * (0/1 + 0/2 + 0/2 + 0.6/1) = 0.2
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@ -95,23 +95,23 @@ Value(AS) = 1/3 * (0/1 + 0/2 + 0/2 + 0.6/1) = 0.2
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Value(MS) = 1/3 * [(Value of adding him to {} / 1) + (Value of adding him to {DB}/2) + (Value of adding him to {AS}/2) + (Value of adding him to {AS, DB}/1) ]
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {DB} = 0
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Value of adding him to {AS} = 0.4
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {AS, DB} = 1
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Number of coalitions excluding him of size 2 = 1
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Value of adding him to {} = 0
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Number of coalitions excluding him of size 0 = 1
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Value of adding him to {DB} = 0
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Value of adding him to {AS} = 0.4
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Number of coalitions excluding him of size 1 = 2
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Value of adding him to {AS, DB} = 1
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Number of coalitions excluding him of size 2 = 1
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Value(MS) = 1/3 * (0/1 + 0/2 + 0.4/2 + 1/1) = 0.4
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Total value = 0.4 + 0.2 + 0.4 = 1 (checks out)
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### Counterfactual values:
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Counterfactual value of AS = 1
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Counterfactual value of MS = 1
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Counterfactual value of DB = 0.6
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Sum of the counterfactual values = 2.6
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Counterfactual value of AS = 1
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Counterfactual value of MS = 1
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Counterfactual value of DB = 0.6
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Sum of the counterfactual values = 2.6
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### Why is this important
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@ -243,9 +243,11 @@ end <- Sys.time()
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end-start
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sum(2/(c(1:1000))^2)```
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```
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## Sources
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https://en.wikipedia.org/wiki/Shapley_value
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https://christophm.github.io/interpretable-ml-book/shapley.html
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https://towardsdatascience.com/understanding-how-ime-shapley-values-explains-predictions-d75c0fceca5a
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https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d
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https://en.wikipedia.org/wiki/Shapley_value
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https://christophm.github.io/interpretable-ml-book/shapley.html
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https://towardsdatascience.com/understanding-how-ime-shapley-values-explains-predictions-d75c0fceca5a
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https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d
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https://www.eeg.tuwien.ac.at/conference/iaee2017/files/presentation/Pr_662_Jamasb_Tooraj.pdf (This last one is an interesting use fo Shapley values for international coordination)
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