Sharing Rewards Among Strangers Based on Peer Evaluations

  • Authors:

  • Arthur Carvalho;Kate Larson

  • Affiliations:

  • Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada;Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

  • Venue:
  • Decision Analysis
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

We study a problem where a new, unfamiliar group of agents has to decide how a joint reward should be shared among them. We focus on settings where the share that each agent receives depends on the evaluations of its peers concerning that agent's contribution to the group. We introduce a mechanism to elicit and aggregate evaluations as well as for determining agents' shares. The intuition behind the proposed mechanism is that each agent has its expected share maximized to the extent that it is well evaluated by its peers and that it is truthfully reporting its evaluations. For promoting truthfulness, the proposed mechanism uses a peer-prediction method built on strictly proper scoring rules. Under the assumption that agents are Bayesian decision makers, we show that our mechanism is incentive compatible and budget balanced. We also provide sufficient conditions under which the proposed mechanism is individually rational, resistant to some kinds of collusion, and fair.