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Othman

AAAI Conferences

The Myerson-Satterthwaite theorem is a foundational impossibility result in mechanism design which states that no mechanism can be Bayes-Nash incentive compatible, individually rational, and not run a deficit. It holds universally for priors that are continuous, gapless, and overlapping. Using automated mechanism design, we investigate how often the impossibility occurs over discrete valuation domains. While the impossibility appears to hold generally for settings with large numbers of possible valuations (approaching the continuous case), domains with realistic valuation structure circumvent the impossibility with surprising frequency. Even if the impossibility applies, the amount of subsidy required to achieve individual rationality and incentive compatibility is relatively small, even over large unstructured domains.


Data is not facts - the impossibility of being unbiased – Data Science Central

@machinelearnbot

We talk a lot about making decisions based on data but we need to be careful about how hard and fast those decisions are. Our decisions are only as good as our data and our analysis. Data is always a sample of the full scope of reality and analytics is always an interpretation of that sample. We need to be cognizant of the differences between Opinions, Facts and Conclusions. And, just as important, we need to recognize the relationship between our judgement and our ego: all disagreements are personal to some degree.


Data is not facts - the impossibility of being unbiased

@machinelearnbot

We talk a lot about making decisions based on data but we need to be careful about how hard and fast those decisions are. Our decisions are only as good as our data and our analysis. Data is always a sample of the full scope of reality and analytics is always an interpretation of that sample. We need to be cognizant of the differences between Opinions, Facts and Conclusions. And, just as important, we need to recognize the relationship between our judgement and our ego: all disagreements are personal to some degree.


Clustering Redemption–Beyond the Impossibility of Kleinberg's Axioms

Neural Information Processing Systems

Kleinberg (2002) stated three axioms that any clustering procedure should satisfy and showed there is no clustering procedure that simultaneously satisfies all three. One of these, called the consistency axiom, requires that when the data is modified in a helpful way, i.e. if points in the same cluster are made more similar and those in different ones made less similar, the algorithm should output the same clustering. To circumvent this impossibility result, research has focused on considering clustering procedures that have a clustering quality measure (or a cost) and showing that a modification of Kleinberg's axioms that takes cost into account lead to feasible clustering procedures. In this work, we take a different approach, based on the observation that the consistency axiom fails to be satisfied when the "correct" number of clusters changes. We modify this axiom by making use of cost functions to determine the correct number of clusters, and require that consistency holds only if the number of clusters remains unchanged.


How Pervasive Is the Myerson-Satterthwaite Impossibility?

AAAI Conferences

The Myerson-Satterthwaite theorem is a foundational impossibility result in mechanism design which states that no mechanism can be Bayes-Nash incentive compatible, individually rational, and not run a deficit. It holds universally for priors that are continuous, gapless, and overlapping.  Using automated mechanism design, we investigate how often the impossibility occurs over discrete valuation domains.  While the impossibility appears to hold generally for settings with large numbers of possible valuations (approaching the continuous case), domains with realistic valuation structure circumvent the impossibility with surprising frequency.  Even if the impossibility applies, the amount of subsidy required to achieve individual rationality and incentive compatibility is relatively small, even over large unstructured domains.