1,076 research outputs found
Social welfare in one-sided matchings: Random priority and beyond
We study the problem of approximate social welfare maximization (without
money) in one-sided matching problems when agents have unrestricted cardinal
preferences over a finite set of items. Random priority is a very well-known
truthful-in-expectation mechanism for the problem. We prove that the
approximation ratio of random priority is Theta(n^{-1/2}) while no
truthful-in-expectation mechanism can achieve an approximation ratio better
than O(n^{-1/2}), where n is the number of agents and items. Furthermore, we
prove that the approximation ratio of all ordinal (not necessarily
truthful-in-expectation) mechanisms is upper bounded by O(n^{-1/2}), indicating
that random priority is asymptotically the best truthful-in-expectation
mechanism and the best ordinal mechanism for the problem.Comment: 13 page
Competitive Equilibrium from Equal Incomes for Two-Sided Matching
Competitive Equilibrium from Equal Incomes for Two-Sided Matching
Using the assignment of students to schools as our leading example, we study many-to-one
two-sided matching markets without transfers. Students are endowed with cardinal preferences
and schools with ordinal ones, while preferences of both sides need not be strict. Using the
idea of a competitive equilibrium from equal incomes (CEEI, Hylland and Zeckhauser (1979)),
we propose a new mechanism, the Generalized CEEI, in which students face different prices
depending on how schools rank them. It always produces fair (justified-envy-free) and ex ante
e¢ cient random assignments and stable deterministic assignments if both students and schools
are truth-telling. We show that each student's incentive to misreport vanishes when the market
becomes large, given all others are truthful. The mechanism is particularly relevant to school
choice as schools' priority orderings over students are usually known and can be considered
as their ordinal preferences. More importantly, in settings like school choice where agents have
similar ordinal preferences, the mechanismis explicit use of cardinal preferences may significantly
improve eficiency. We also discuss its application in school choice with group-specific quotas
and in one-sided matching
Efficiency of Truthful and Symmetric Mechanisms in One-sided Matching
We study the efficiency (in terms of social welfare) of truthful and
symmetric mechanisms in one-sided matching problems with {\em dichotomous
preferences} and {\em normalized von Neumann-Morgenstern preferences}. We are
particularly interested in the well-known {\em Random Serial Dictatorship}
mechanism. For dichotomous preferences, we first show that truthful, symmetric
and optimal mechanisms exist if intractable mechanisms are allowed. We then
provide a connection to online bipartite matching. Using this connection, it is
possible to design truthful, symmetric and tractable mechanisms that extract
0.69 of the maximum social welfare, which works under assumption that agents
are not adversarial. Without this assumption, we show that Random Serial
Dictatorship always returns an assignment in which the expected social welfare
is at least a third of the maximum social welfare. For normalized von
Neumann-Morgenstern preferences, we show that Random Serial Dictatorship always
returns an assignment in which the expected social welfare is at least
\frac{1}{e}\frac{\nu(\opt)^2}{n}, where \nu(\opt) is the maximum social
welfare and is the number of both agents and items. On the hardness side,
we show that no truthful mechanism can achieve a social welfare better than
\frac{\nu(\opt)^2}{n}.Comment: 13 pages, 1 figur
Social Welfare in One-sided Matching Markets without Money
We study social welfare in one-sided matching markets where the goal is to
efficiently allocate n items to n agents that each have a complete, private
preference list and a unit demand over the items. Our focus is on allocation
mechanisms that do not involve any monetary payments. We consider two natural
measures of social welfare: the ordinal welfare factor which measures the
number of agents that are at least as happy as in some unknown, arbitrary
benchmark allocation, and the linear welfare factor which assumes an agent's
utility linearly decreases down his preference lists, and measures the total
utility to that achieved by an optimal allocation. We analyze two matching
mechanisms which have been extensively studied by economists. The first
mechanism is the random serial dictatorship (RSD) where agents are ordered in
accordance with a randomly chosen permutation, and are successively allocated
their best choice among the unallocated items. The second mechanism is the
probabilistic serial (PS) mechanism of Bogomolnaia and Moulin [8], which
computes a fractional allocation that can be expressed as a convex combination
of integral allocations. The welfare factor of a mechanism is the infimum over
all instances. For RSD, we show that the ordinal welfare factor is
asymptotically 1/2, while the linear welfare factor lies in the interval [.526,
2/3]. For PS, we show that the ordinal welfare factor is also 1/2 while the
linear welfare factor is roughly 2/3. To our knowledge, these results are the
first non-trivial performance guarantees for these natural mechanisms
Social Welfare in One-Sided Matching Mechanisms
We study the Price of Anarchy of mechanisms for the well-known problem of
one-sided matching, or house allocation, with respect to the social welfare
objective. We consider both ordinal mechanisms, where agents submit preference
lists over the items, and cardinal mechanisms, where agents may submit
numerical values for the items being allocated. We present a general lower
bound of on the Price of Anarchy, which applies to all
mechanisms. We show that two well-known mechanisms, Probabilistic Serial, and
Random Priority, achieve a matching upper bound. We extend our lower bound to
the Price of Stability of a large class of mechanisms that satisfy a common
proportionality property, and show stronger bounds on the Price of Anarchy of
all deterministic mechanisms
Truthful approximations to range voting
We consider the fundamental mechanism design problem of approximate social
welfare maximization under general cardinal preferences on a finite number of
alternatives and without money. The well-known range voting scheme can be
thought of as a non-truthful mechanism for exact social welfare maximization in
this setting. With m being the number of alternatives, we exhibit a randomized
truthful-in-expectation ordinal mechanism implementing an outcome whose
expected social welfare is at least an Omega(m^{-3/4}) fraction of the social
welfare of the socially optimal alternative. On the other hand, we show that
for sufficiently many agents and any truthful-in-expectation ordinal mechanism,
there is a valuation profile where the mechanism achieves at most an
O(m^{-{2/3}) fraction of the optimal social welfare in expectation. We get
tighter bounds for the natural special case of m = 3, and in that case
furthermore obtain separation results concerning the approximation ratios
achievable by natural restricted classes of truthful-in-expectation mechanisms.
In particular, we show that for m = 3 and a sufficiently large number of
agents, the best mechanism that is ordinal as well as mixed-unilateral has an
approximation ratio between 0.610 and 0.611, the best ordinal mechanism has an
approximation ratio between 0.616 and 0.641, while the best mixed-unilateral
mechanism has an approximation ratio bigger than 0.660. In particular, the best
mixed-unilateral non-ordinal (i.e., cardinal) mechanism strictly outperforms
all ordinal ones, even the non-mixed-unilateral ordinal ones
Welfare Maximization and Truthfulness in Mechanism Design with Ordinal Preferences
We study mechanism design problems in the {\em ordinal setting} wherein the
preferences of agents are described by orderings over outcomes, as opposed to
specific numerical values associated with them. This setting is relevant when
agents can compare outcomes, but aren't able to evaluate precise utilities for
them. Such a situation arises in diverse contexts including voting and matching
markets.
Our paper addresses two issues that arise in ordinal mechanism design. To
design social welfare maximizing mechanisms, one needs to be able to
quantitatively measure the welfare of an outcome which is not clear in the
ordinal setting. Second, since the impossibility results of Gibbard and
Satterthwaite~\cite{Gibbard73,Satterthwaite75} force one to move to randomized
mechanisms, one needs a more nuanced notion of truthfulness.
We propose {\em rank approximation} as a metric for measuring the quality of
an outcome, which allows us to evaluate mechanisms based on worst-case
performance, and {\em lex-truthfulness} as a notion of truthfulness for
randomized ordinal mechanisms. Lex-truthfulness is stronger than notions
studied in the literature, and yet flexible enough to admit a rich class of
mechanisms {\em circumventing classical impossibility results}. We demonstrate
the usefulness of the above notions by devising lex-truthful mechanisms
achieving good rank-approximation factors, both in the general ordinal setting,
as well as structured settings such as {\em (one-sided) matching markets}, and
its generalizations, {\em matroid} and {\em scheduling} markets.Comment: Some typos correcte
Truthful Facility Assignment with Resource Augmentation: An Exact Analysis of Serial Dictatorship
We study the truthful facility assignment problem, where a set of agents with
private most-preferred points on a metric space are assigned to facilities that
lie on the metric space, under capacity constraints on the facilities. The goal
is to produce such an assignment that minimizes the social cost, i.e., the
total distance between the most-preferred points of the agents and their
corresponding facilities in the assignment, under the constraint of
truthfulness, which ensures that agents do not misreport their most-preferred
points.
We propose a resource augmentation framework, where a truthful mechanism is
evaluated by its worst-case performance on an instance with enhanced facility
capacities against the optimal mechanism on the same instance with the original
capacities. We study a very well-known mechanism, Serial Dictatorship, and
provide an exact analysis of its performance. Although Serial Dictatorship is a
purely combinatorial mechanism, our analysis uses linear programming; a linear
program expresses its greedy nature as well as the structure of the input, and
finds the input instance that enforces the mechanism have its worst-case
performance. Bounding the objective of the linear program using duality
arguments allows us to compute tight bounds on the approximation ratio. Among
other results, we prove that Serial Dictatorship has approximation ratio
when the capacities are multiplied by any integer . Our
results suggest that even a limited augmentation of the resources can have
wondrous effects on the performance of the mechanism and in particular, the
approximation ratio goes to 1 as the augmentation factor becomes large. We
complement our results with bounds on the approximation ratio of Random Serial
Dictatorship, the randomized version of Serial Dictatorship, when there is no
resource augmentation
College admissions and the role of information : an experimental study
We analyze two well-known matching mechanismsâthe Gale-Shapley, and the Top
Trading Cycles (TTC) mechanismsâin the experimental lab in three different informational
settings, and study the role of information in individual decision making. Our results suggest
thatâin line with the theoryâin the college admissions model the Gale-Shapley mechanism
outperforms the TTC mechanisms in terms of efficiency and stability, and it is as successful as
the TTC mechanism regarding the proportion of truthful preference revelation. In addition, we
find that information has an important effect on truthful behavior and stability. Nevertheless,
regarding efficiency, the Gale-Shapley mechanism is less sensitive to the amount of information
participants hold
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