188 research outputs found
Equilibria in Sequential Allocation
Sequential allocation is a simple mechanism for sharing multiple indivisible
items. We study strategic behavior in sequential allocation. In particular, we
consider Nash dynamics, as well as the computation and Pareto optimality of
pure equilibria, and Stackelberg strategies. We first demonstrate that, even
for two agents, better responses can cycle. We then present a linear-time
algorithm that returns a profile (which we call the "bluff profile") that is in
pure Nash equilibrium. Interestingly, the outcome of the bluff profile is the
same as that of the truthful profile and the profile is in pure Nash
equilibrium for \emph{all} cardinal utilities consistent with the ordinal
preferences. We show that the outcome of the bluff profile is Pareto optimal
with respect to pairwise comparisons. In contrast, we show that an assignment
may not be Pareto optimal with respect to pairwise comparisons even if it is a
result of a preference profile that is in pure Nash equilibrium for all
utilities consistent with ordinal preferences. Finally, we present a dynamic
program to compute an optimal Stackelberg strategy for two agents, where the
second agent has a constant number of distinct values for the items
The Core of the Participatory Budgeting Problem
In participatory budgeting, communities collectively decide on the allocation
of public tax dollars for local public projects. In this work, we consider the
question of fairly aggregating the preferences of community members to
determine an allocation of funds to projects. This problem is different from
standard fair resource allocation because of public goods: The allocated goods
benefit all users simultaneously. Fairness is crucial in participatory decision
making, since generating equitable outcomes is an important goal of these
processes. We argue that the classic game theoretic notion of core captures
fairness in the setting. To compute the core, we first develop a novel
characterization of a public goods market equilibrium called the Lindahl
equilibrium, which is always a core solution. We then provide the first (to our
knowledge) polynomial time algorithm for computing such an equilibrium for a
broad set of utility functions; our algorithm also generalizes (in a
non-trivial way) the well-known concept of proportional fairness. We use our
theoretical insights to perform experiments on real participatory budgeting
voting data. We empirically show that the core can be efficiently computed for
utility functions that naturally model our practical setting, and examine the
relation of the core with the familiar welfare objective. Finally, we address
concerns of incentives and mechanism design by developing a randomized
approximately dominant-strategy truthful mechanism building on the exponential
mechanism from differential privacy
Pareto Optimal Matchings in Many-to-Many Markets with Ties
We consider Pareto-optimal matchings (POMs) in a many-to-many market of
applicants and courses where applicants have preferences, which may include
ties, over individual courses and lexicographic preferences over sets of
courses. Since this is the most general setting examined so far in the
literature, our work unifies and generalizes several known results.
Specifically, we characterize POMs and introduce the \emph{Generalized Serial
Dictatorship Mechanism with Ties (GSDT)} that effectively handles ties via
properties of network flows. We show that GSDT can generate all POMs using
different priority orderings over the applicants, but it satisfies truthfulness
only for certain such orderings. This shortcoming is not specific to our
mechanism; we show that any mechanism generating all POMs in our setting is
prone to strategic manipulation. This is in contrast to the one-to-one case
(with or without ties), for which truthful mechanisms generating all POMs do
exist
Bounded Temporal Fairness for FIFO Financial Markets
Financial exchange operators cater to the needs of their users while
simultaneously ensuring compliance with the financial regulations. In this
work, we focus on the operators' commitment for fair treatment of all competing
participants. We first discuss unbounded temporal fairness and then investigate
its implementation and infrastructure requirements for exchanges. We find that
these requirements can be fully met only under ideal conditions and argue that
unbounded fairness in FIFO markets is unrealistic. To further support this
claim, we analyse several real-world incidents and show that subtle
implementation inefficiencies and technical optimizations suffice to give
unfair advantages to a minority of the participants. We finally introduce,
{\epsilon}-fairness, a bounded definition of temporal fairness and discuss how
it can be combined with non-continuous market designs to provide equal
participant treatment with minimum divergence from the existing market
operation
Allocation in Practice
How do we allocate scarcere sources? How do we fairly allocate costs? These
are two pressing challenges facing society today. I discuss two recent projects
at NICTA concerning resource and cost allocation. In the first, we have been
working with FoodBank Local, a social startup working in collaboration with
food bank charities around the world to optimise the logistics of collecting
and distributing donated food. Before we can distribute this food, we must
decide how to allocate it to different charities and food kitchens. This gives
rise to a fair division problem with several new dimensions, rarely considered
in the literature. In the second, we have been looking at cost allocation
within the distribution network of a large multinational company. This also has
several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on
Artificial Intelligence (KI 2014), Springer LNC
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