TY - GEN

T1 - Incentive-Compatible Selection Mechanisms for Forests

AU - Babichenko, Yakov

AU - Dean, Oren

AU - Tennenholtz, Moshe

N1 - Publisher Copyright:
© 2020 ACM.

PY - 2020/7/13

Y1 - 2020/7/13

N2 - Given a directed forest-graph, a probabilistic selection mechanismis a probability distribution over the vertex set. A selection mechanism is incentive-compatible(IC), if the probability assigned to a vertex does not change when we alter its outgoing edge (or even remove it). The quality of a selection mechanism is the worst-case ratio between the expected progeny under the mechanism's distribution and the maximal progeny in the forest. In this paper we prove an upper bound of 4/5 and a lower bound of 1/łn16 ∼0.36 for the quality of any IC selection mechanism. The lower bound is achieved by two novel mechanisms and is a significant improvement to the results of Babichenko et al. (WWW '18). The first, simpler mechanism, has the nice feature of generating distributions which are fair (i.e., monotone and proportional). The downside of this mechanism is that it is not exact (i.e., the probabilities might sum-up to less than 1). Our second, more involved mechanism, is exact but not fair. We also prove an impossibility for an IC mechanism that is both exact and fair and has a positive quality.

AB - Given a directed forest-graph, a probabilistic selection mechanismis a probability distribution over the vertex set. A selection mechanism is incentive-compatible(IC), if the probability assigned to a vertex does not change when we alter its outgoing edge (or even remove it). The quality of a selection mechanism is the worst-case ratio between the expected progeny under the mechanism's distribution and the maximal progeny in the forest. In this paper we prove an upper bound of 4/5 and a lower bound of 1/łn16 ∼0.36 for the quality of any IC selection mechanism. The lower bound is achieved by two novel mechanisms and is a significant improvement to the results of Babichenko et al. (WWW '18). The first, simpler mechanism, has the nice feature of generating distributions which are fair (i.e., monotone and proportional). The downside of this mechanism is that it is not exact (i.e., the probabilities might sum-up to less than 1). Our second, more involved mechanism, is exact but not fair. We also prove an impossibility for an IC mechanism that is both exact and fair and has a positive quality.

KW - incentive compatibility

KW - selection mechanisms

UR - http://www.scopus.com/inward/record.url?scp=85089276280&partnerID=8YFLogxK

U2 - 10.1145/3391403.3399456

DO - 10.1145/3391403.3399456

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85089276280

T3 - EC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation

SP - 111

EP - 131

BT - EC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation

T2 - 21st ACM Conference on Economics and Computation, EC 2020

Y2 - 13 July 2020 through 17 July 2020

ER -