Lexicographic dominance is a total order between random variables. It is a form of stochastic ordering. It is defined as follows.[1]: 8  Random variable A has lexicographic dominance over random variable B (denoted ) if one of the following holds:

  • A has a higher probability than B of receiving the best outcome.
  • A and B have an equal probability of receiving the best outcome, but A has a higher probability of receiving the 2nd-best outcome.
  • A and B have an equal probability of receiving the best and 2nd-best outcomes, but A has a higher probability of receiving the 3rd-best outcome.

In other words: let k be the first index for which the probability of receiving the k-th best outcome is different for A and B. Then this probability should be higher for A.

Variants

Upward lexicographic dominance is defined as follows.[2] Random variable A has upward lexicographic dominance over random variable B (denoted ) if one of the following holds:

  • A has a lower probability than B of receiving the worst outcome.
  • A and B have an equal probability of receiving the worst outcome, but A has a lower probability of receiving the 2nd-worst outcome.
  • A and B have an equal probability of receiving the worst and 2nd-worst outcomes, but A has a lower probability of receiving the 3rd-worst outcomes.

To distinguish between the two notions, the standard lexicographic dominance notion is sometimes called downward lexicographic dominance and denoted .

Relation to other dominance notions

First-order stochastic dominance implies both downward-lexicographic and upward-lexicographic dominance.[3] The opposite is not true. For example, suppose there are four outcomes ranked z > y > x > w. Consider the two lotteries that assign to z, y, x, w the following probabilities:

  • A: .2, .4, .2, .2
  • B: .2, .3, .4, .1

Then the following holds:

  • , since they assign the same probability to z but A assigns more probability to y.
  • , since B assigns less probability to the worst outcome w.
  • , since B assigns more probability to the three best outcomes {z,y,x}. If, for example, the value of z,y,x is very near 1, and the value of w is 0, then the expected value of B is near 0.9 while the expected value of A is near 0.8.
  • , since A assigns more probability to the two best outcomes {z,y}. If, for example, the value of z,y is very near 1, and the value of x,w is 0, then the expected value of B is near 0.5 while the expected value of A is near 0.6.

Applications

Lexicographic dominance relations are used in social choice theory to define notions of strategyproofness,[2] incentives for participation,[4] ordinal efficiency[3] and envy-freeness.[5]

Hosseini and Larson[6] analyse the properties of rules for fair random assignment based on lexicographic dominance.

References

  1. ↑ Chakrabarty, Deeparnab; Swamy, Chaitanya (2014-01-12). "Welfare maximization and truthfulness in mechanism design with ordinal preferences". Proceedings of the 5th conference on Innovations in theoretical computer science. ITCS '14. New York, NY, USA: Association for Computing Machinery. pp. 105–120. doi:10.1145/2554797.2554810. ISBN 978-1-4503-2698-8. S2CID 2428592.
  2. 1 2 Cho, Wonki Jo (2016-01-01). "Incentive properties for ordinal mechanisms". Games and Economic Behavior. 95: 168–177. doi:10.1016/j.geb.2015.12.003. ISSN 0899-8256.
  3. 1 2 Cho, Wonki Jo; Doğan, Battal (2016-09-01). "Equivalence of efficiency notions for ordinal assignment problems". Economics Letters. 146: 8–12. doi:10.1016/j.econlet.2016.07.007. ISSN 0165-1765.
  4. ↑ Aziz, Haris (2016-11-08). "Participation Incentives in Randomized Social Choice". arXiv:1602.02174 [cs.GT].
  5. ↑ Cho, Wonki Jo (2018-06-01). "Probabilistic assignment: an extension approach". Social Choice and Welfare. 51 (1): 137–162. doi:10.1007/s00355-018-1110-z. ISSN 1432-217X. S2CID 19700606.
  6. ↑ Hadi Hosseini, Kate Larson (2015-07-24). Strategyproof Quota Mechanisms for Multiple Assignment Problems. OCLC 1106222190.
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