68 research outputs found
Sum of Us: Strategyproof Selection from the Selectors
We consider directed graphs over a set of n agents, where an edge (i,j) is
taken to mean that agent i supports or trusts agent j. Given such a graph and
an integer k\leq n, we wish to select a subset of k agents that maximizes the
sum of indegrees, i.e., a subset of k most popular or most trusted agents. At
the same time we assume that each individual agent is only interested in being
selected, and may misreport its outgoing edges to this end. This problem
formulation captures realistic scenarios where agents choose among themselves,
which can be found in the context of Internet search, social networks like
Twitter, or reputation systems like Epinions.
Our goal is to design mechanisms without payments that map each graph to a
k-subset of agents to be selected and satisfy the following two constraints:
strategyproofness, i.e., agents cannot benefit from misreporting their outgoing
edges, and approximate optimality, i.e., the sum of indegrees of the selected
subset of agents is always close to optimal. Our first main result is a
surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof
mechanism can provide a finite approximation ratio. Our second main result is a
randomized strategyproof mechanism with an approximation ratio that is bounded
from above by four for any value of k, and approaches one as k grows
Strategyproof Decision-Making in Panel Data Settings and Beyond
We consider the classical problem of decision-making using panel data, in
which a decision-maker gets noisy, repeated measurements of multiple units (or
agents). We consider a setup where there is a pre-intervention period, when the
principal observes the outcomes of each unit, after which the principal uses
these observations to assign a treatment to each unit. Unlike this classical
setting, we permit the units generating the panel data to be strategic, i.e.
units may modify their pre-intervention outcomes in order to receive a more
desirable intervention. The principal's goal is to design a strategyproof
intervention policy, i.e. a policy that assigns units to their correct
interventions despite their potential strategizing. We first identify a
necessary and sufficient condition under which a strategyproof intervention
policy exists, and provide a strategyproof mechanism with a simple closed form
when one does exist. Along the way, we prove impossibility results for
strategic multiclass classification, which may be of independent interest. When
there are two interventions, we establish that there always exists a
strategyproof mechanism, and provide an algorithm for learning such a
mechanism. For three or more interventions, we provide an algorithm for
learning a strategyproof mechanism if there exists a sufficiently large gap in
the principal's rewards between different interventions. Finally, we
empirically evaluate our model using real-world panel data collected from
product sales over 18 months. We find that our methods compare favorably to
baselines which do not take strategic interactions into consideration, even in
the presence of model misspecification
Machine Learning-powered Course Allocation
We introduce a machine learning-powered course allocation mechanism.
Concretely, we extend the state-of-the-art Course Match mechanism with a
machine learning-based preference elicitation module. In an iterative,
asynchronous manner, this module generates pairwise comparison queries that are
tailored to each individual student. Regarding incentives, our machine
learning-powered course match (MLCM) mechanism retains the attractive
strategyproofness in the large property of Course Match. Regarding welfare, we
perform computational experiments using a simulator that was fitted to
real-world data. Our results show that, compared to Course Match, MLCM
increases average student utility by 4%-9% and minimum student utility by
10%-21%, even with only ten comparison queries. Finally, we highlight the
practicability of MLCM and the ease of piloting it for universities currently
using Course Match
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