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Fair Reward Division

Microeconomics Seminar 2020-2021
joint with the Département de sciences économiques, Université de Montréal

Organizer : Sean Horan (U. of Montreal)

*  Invitation only. Please contact the organizer if you would like access.


RÉSUMÉAxiomatic approaches are an appealing method for designing fair allocation algorithms, as they provide a formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms. We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory for the fair division of rewards.  We use two crowdsourced experiments to study people’s impartial reward divisions in cooperative games, focusing on games that systematically vary the values of the single-player coalitions. Our results show that people select rewards that are remarkably consistent, but place much more emphasis on the single-player coalitions than the Shapley value does. Further, their reward divisions violate both the null player and additivity axioms but support weaker axioms. We argue for a more general methodology of testing axioms against experimental data, retaining some of the conceptual simplicity of the axiomatic approach while still using people’s opinions to drive the design of algorithms. 



BIOGRAPHIE : Kate Larson is a Professor in the Cheriton School of Computer Science at the University of Waterloo where she also holds a University Research Chair and the Pasupalak AI Fellowship. She is broadly interested in issues which arise in settings where groups of agents interact, where these agents may be AI-agents, humans, or a combination. In particular, she is interested in algorithmic questions arising in artificial intelligence and multiagent systems with a particular focus on algorithmic game theory, group decision making, preference and value modelling, and the insights that reinforcement learning can bring to these problems.

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