3 research outputs found
Computation-Aware Data Aggregation
Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes\u27 input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time.
In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ? log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule
Optimized Distortion and Proportional Fairness in Voting
A voting rule decides on a probability distribution over a set of
alternatives, based on rankings of those alternatives provided by agents. We
assume that agents have cardinal utility functions over the alternatives, but
voting rules have access to only the rankings induced by these utilities. We
evaluate how well voting rules do on measures of social welfare and of
proportional fairness, computed based on the hidden utility functions.
In particular, we study the distortion of voting rules, which is a worst-case
measure. It is an approximation ratio comparing the utilitarian social welfare
of the optimum outcome to the welfare of the outcome selected by the voting
rule, in the worst case over possible input profiles and utility functions that
are consistent with the input. The literature has studied distortion with
unit-sum utility functions, and left a small asymptotic gap in the best
possible distortion. Using tools from the theory of fair multi-winner
elections, we propose the first voting rule which achieves the optimal
distortion for unit-sum utilities. Our voting rule also
achieves optimum distortion for unit-range and approval
utilities.
We then take a similar worst-case approach to a quantitative measure of the
fairness of a voting rule, called proportional fairness. Informally, it
measures whether the influence of cohesive groups of agents on the voting
outcome is proportional to the group size. We show that there is a voting rule
which, without knowledge of the utilities, can achieve an -approximation to proportional fairness, the best possible approximation. As
a consequence of its proportional fairness, we show that this voting rule
achieves distortion with respect to Nash welfare, and provides an
-approximation to the core, making it interesting for applications
in participatory budgeting.Comment: 34 pages including appendix, accepted at ACM EC 202
HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets
Expert crowdsourcing (e.g., Upwork.com) provides promising benefits such as productivity improvements for employers, and flexible working arrangements for workers. Yet to realize these benefits, a key persistent challenge is effective hiring at scale. Current approaches, such as reputation systems and standardized competency tests, develop weaknesses such as score inflation over time, thus degrading market quality. This paper presents HirePeer, a novel alternative approach to hiring at scale that leverages peer assessment to elicit honest assessments of fellow workers' job application materials, which it then aggregates using an impartial ranking algorithm. This paper reports on three studies that investigate both the costs and the benefits to workers and employers of impartial peer-assessed hiring. We find, to solicit honest assessments, algorithms must be communicated in terms of their impartial effects. Second, in practice, peer assessment is highly accurate, and impartial rank aggregation algorithms incur a small accuracy cost for their impartiality guarantee. Third, workers report finding peer-assessed hiring useful for receiving targeted feedback on their job materials