4 research outputs found
Machine Learning? In MY Election? It\u27s More Likely Than You Think: Voting Rules via Neural Networks
Impossibility theorems in social choice have represented a barrier in the creation of universal, non-dictatorial, and non-manipulable voting rules, highlighting a key trade-off between social welfare and strategy-proofness. However, a social planner may be concerned with only a particular preference distribution and wonder whether it is possible to better optimize this trade-off. To address this problem, we propose an end-to-end, machine learning-based framework that creates voting rules according to a social planner\u27s constraints, for any type of preference distribution. After experimenting with rank-based social choice rules, we find that automatically-designed rules are less susceptible to manipulation than most existing rules, while still attaining high social welfare
Partial Strategyproofness: Relaxing Strategyproofness for the Random Assignment Problem
We present partial strategyproofness, a new, relaxed notion of
strategyproofness for studying the incentive properties of non-strategyproof
assignment mechanisms. Informally, a mechanism is partially strategyproof if it
makes truthful reporting a dominant strategy for those agents whose preference
intensities differ sufficiently between any two objects. We demonstrate that
partial strategyproofness is axiomatically motivated and yields a parametric
measure for "how strategyproof" an assignment mechanism is. We apply this new
concept to derive novel insights about the incentive properties of the
probabilistic serial mechanism and different variants of the Boston mechanism.Comment: Working Pape
The Pareto Frontier for Random Mechanisms
We study the trade-offs between strategyproofness and other desiderata, such
as efficiency or fairness, that often arise in the design of random ordinal
mechanisms. We use approximate strategyproofness to define manipulability, a
measure to quantify the incentive properties of non-strategyproof mechanisms,
and we introduce the deficit, a measure to quantify the performance of
mechanisms with respect to another desideratum. When this desideratum is
incompatible with strategyproofness, mechanisms that trade off manipulability
and deficit optimally form the Pareto frontier. Our main contribution is a
structural characterization of this Pareto frontier, and we present algorithms
that exploit this structure to compute it. To illustrate its shape, we apply
our results for two different desiderata, namely Plurality and Veto scoring, in
settings with 3 alternatives and up to 18 agents.Comment: Working Pape