11 research outputs found
Truthful Aggregation of Budget Proposals with Proportionality Guarantees
We study a participatory budgeting problem, where a set of strategic agents wish to split a divisible budget among different projects, by aggregating their proposals on a single division. Unfortunately, the straight-forward rule that divides the budget proportionally is susceptible to manipulation. In a recent work, Freeman et al. [arXiv:1905.00457] proposed a class of truthful mechanisms, called moving phantom mechanisms. Among others, they propose a proportional mechanism, in the sense that in the extreme case where all agents prefer a single project to receive the whole amount, the budget is assigned proportionally. While proportionality is a naturally desired property, it is defined over a limited type of preference profiles. To address this, we expand the notion of proportionality, by proposing a quantitative framework which evaluates a budget aggregation mechanism according to its worst-case distance from the proportional allocation. Crucially, this is defined for every preference profile. We study this measure on the class of moving phantom mechanisms, and we provide approximation guarantees. For two projects, we show that the Uniform Phantom mechanism is the optimal among all truthful mechanisms. For three projects, we propose a new, proportional mechanism which is virtually optimal among all moving phantom mechanisms. Finally, we provide impossibility results regarding the approximability of moving phantom mechanisms
Settling the Score: Portioning with Cardinal Preferences
We study a portioning setting in which a public resource such as time or
money is to be divided among a given set of candidates, and each agent proposes
a division of the resource. We consider two families of aggregation rules for
this setting - those based on coordinate-wise aggregation and those that
optimize some notion of welfare - as well as the recently proposed Independent
Markets mechanism. We provide a detailed analysis of these rules from an
axiomatic perspective, both for classic axioms, such as strategyproofness and
Pareto optimality, and for novel axioms, which aim to capture proportionality
in this setting. Our results indicate that a simple rule that computes the
average of all proposals satisfies many of our axioms, including some that are
violated by more sophisticated rules.Comment: Appears in the 26th European Conference on Artificial Intelligence
(ECAI), 202
Project-Fair and Truthful Mechanisms for Budget Aggregation
We study the budget aggregation problem in which a set of strategic voters
must split a finite divisible resource (such as money or time) among a set of
competing projects. Our goal is twofold: We seek truthful mechanisms that
provide fairness guarantees to the projects. For the first objective, we focus
on the class of moving phantom mechanisms [Freeman et al., 2021], which are --
to this day -- essentially the only known truthful mechanisms in this setting.
For project fairness, we consider the mean division as a fair baseline, and
bound the maximum difference between the funding received by any project and
this baseline. We propose a novel and simple moving phantom mechanism that
provides optimal project fairness guarantees. As a corollary of our results, we
show that our new mechanism minimizes the distance to the mean (a
measure suggested by Caragiannis et al. [2022]) for three projects and gives
the first non-trivial bounds on this quantity for more than three projects
Epistemic Selection of Costly Alternatives: The Case of Participatory Budgeting
We initiate the study of voting rules for participatory budgeting using the
so-called epistemic approach, where one interprets votes as noisy reflections
of some ground truth regarding the objectively best set of projects to fund.
Using this approach, we first show that both the most studied rules in the
literature and the most widely used rule in practice cannot be justified on
epistemic grounds: they cannot be interpreted as maximum likelihood estimators,
whatever assumptions we make about the accuracy of voters. Focusing then on
welfare-maximising rules, we obtain both positive and negative results
regarding epistemic guarantees
A Mechanism for Participatory Budgeting With Funding Constraints and Project Interactions
Participatory budgeting (PB) has been widely adopted and has attracted
significant research efforts; however, there is a lack of mechanisms for PB
which elicit project interactions, such as substitution and complementarity,
from voters. Also, the outcomes of PB in practice are subject to various
minimum/maximum funding constraints on 'types' of projects. There is an
insufficient understanding of how these funding constraints affect PB's
strategic and computational complexities.
We propose a novel preference elicitation scheme for PB which allows voters
to express how their utilities from projects within 'groups' interact. We
consider preference aggregation done under minimum and maximum funding
constraints on 'types' of projects, where a project can have multiple type
labels as long as this classification can be defined by a 1-laminar structure
(henceforth called 1-laminar funding constraints). Overall, we extend the
Knapsack voting model of Goel et al. in two ways - enriching the preference
elicitation scheme to include project interactions and generalizing the
preference aggregation scheme to include 1-laminar funding constraints.
We show that the strategyproofness results of Goel et al. for Knapsack voting
continue to hold under 1-laminar funding constraints. Although project
interactions often break the strategyproofness, we study a special case of vote
profiles where truthful voting is a Nash equilibrium under substitution project
interactions. We then turn to the study of the computational complexity of
preference aggregation. Social welfare maximization under project interactions
is NP-hard. As a workaround for practical instances, we give a fixed parameter
tractable (FPT) algorithm for social welfare maximization with respect to the
maximum number of projects in a group
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates
Elections and opinion polls often have many candidates, with the aim to
either rank the candidates or identify a small set of winners according to
voters' preferences. In practice, voters do not provide a full ranking;
instead, each voter provides their favorite K candidates, potentially in ranked
order. The election organizer must choose K and an aggregation rule.
We provide a theoretical framework to make these choices. Each K-Approval or
K-partial ranking mechanism (with a corresponding positional scoring rule)
induces a learning rate for the speed at which the election correctly recovers
the asymptotic outcome. Given the voter choice distribution, the election
planner can thus identify the rate optimal mechanism. Earlier work in this area
provides coarse order-of-magnitude guaranties which are not sufficient to make
such choices. Our framework further resolves questions of when randomizing
between multiple mechanisms may improve learning, for arbitrary voter noise
models.
Finally, we use data from 5 large participatory budgeting elections that we
organized across several US cities, along with other ranking data, to
demonstrate the utility of our methods. In particular, we find that
historically such elections have set K too low and that picking the right
mechanism can be the difference between identifying the ultimate winner with
only a 80% probability or a 99.9% probability after 400 voters.Comment: To appear in HCOMP 201