white manipulation in judgment aggregation

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University of Tsukuba, LGS09, 26 - 29 August 2009 White Manipulation in Judgment Aggregation Gabriella Pigozzi Davide Grossi ILLC Amsterdam Marija Slavkovik W hat is this all about judgment aggregation (JA) has two problems: aggregation

University of Tsukuba, LGS’09, 26 - 29 August 2009 White Manipulation in Judgment Aggregation Gabriella Pigozzi Davide Grossi ILLC Amsterdam Marija Slavkovik
W hat is this all about judgment aggregation (JA) has two problems: aggregation functions that satisfy a desirable set of properties do not exist aggregation operators that exist are manipulable the question is: is lying, cheating and manipulation really that bad ? LGS’09, University of Tsukuba , 26 - 29 August 2009 2
W hite M anipulability the colloquial term “white lies” LGS’09, University of Tsukuba , 26 - 29 August 2009 3
W hite M anipulability the colloquial term “white lies” LGS’09, University of Tsukuba , 26 - 29 August 2009 3
W hite M anipulability the colloquial term “white lies” LGS’09, University of Tsukuba , 26 - 29 August 2009 3
W hite M anipulability the colloquial term “white lies” manipulation - lying with the intent to improve the outcome for the agent who lies LGS’09, University of Tsukuba , 26 - 29 August 2009 3
W hite M anipulability the colloquial term “white lies” manipulation - lying with the intent to improve the outcome for the agent who lies white manipulation - lying with the intent to improve the outcome for all the agents involved LGS’09, University of Tsukuba , 26 - 29 August 2009 3
I n the rest of the talk introduce the basic concepts of judgment aggregation redefine the judgment aggregation function introduce in JA: scoring functions, social welfare notions define white manipulation initial results conclusions LGS’09, University of Tsukuba , 26 - 29 August 2009 4
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no f : profiles − → judgment sets LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no f : profiles − → judgment sets LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no f : profiles − → judgment sets LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no f : profiles − → judgment sets impasse LGS’09, University of Tsukuba , 26 - 29 August 2009 5
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no judgment aggregation functions are not manipulable if they satisfy independence and (weak) monotonicity[1] LGS’09, University of Tsukuba , 26 - 29 August 2009 6
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no judgment aggregation functions are not manipulable if they satisfy independence and (weak) monotonicity[1] LGS’09, University of Tsukuba , 26 - 29 August 2009 6
J udgment A ggregation how individual judgments on logically connected issues can be aggregated into a collective judgment on the same issues hiring committee example with rule : x ↔ ( a ∧ b ) a = X is good at teaching b = X is good at research x = hire X prof. A yes no no prof. B yes yes yes prof. C no yes no Majority yes yes no judgment aggregation functions are not manipulable if they satisfy independence and (weak) monotonicity[1] LGS’09, University of Tsukuba , 26 - 29 August 2009 6

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