14 research outputs found
Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
We study a problem of optimal information gathering from multiple data
providers that need to be incentivized to provide accurate information. This
problem arises in many real world applications that rely on crowdsourced data
sets, but where the process of obtaining data is costly. A notable example of
such a scenario is crowd sensing. To this end, we formulate the problem of
optimal information gathering as maximization of a submodular function under a
budget constraint, where the budget represents the total expected payment to
data providers. Contrary to the existing approaches, we base our payments on
incentives for accuracy and truthfulness, in particular, {\em peer-prediction}
methods that score each of the selected data providers against its best peer,
while ensuring that the minimum expected payment is above a given threshold. We
first show that the problem at hand is hard to approximate within a constant
factor that is not dependent on the properties of the payment function.
However, for given topological and analytical properties of the instance, we
construct two greedy algorithms, respectively called PPCGreedy and
PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the
optimal solution. Finally, we evaluate our methods using a realistic crowd
sensing testbed.Comment: Longer version of AAAI'18 pape
Budget-Feasible Mechanism Design for Non-Monotone Submodular Objectives: Offline and Online
The framework of budget-feasible mechanism design studies procurement
auctions where the auctioneer (buyer) aims to maximize his valuation function
subject to a hard budget constraint. We study the problem of designing truthful
mechanisms that have good approximation guarantees and never pay the
participating agents (sellers) more than the budget. We focus on the case of
general (non-monotone) submodular valuation functions and derive the first
truthful, budget-feasible and -approximate mechanisms that run in
polynomial time in the value query model, for both offline and online auctions.
Prior to our work, the only -approximation mechanism known for
non-monotone submodular objectives required an exponential number of value
queries.
At the heart of our approach lies a novel greedy algorithm for non-monotone
submodular maximization under a knapsack constraint. Our algorithm builds two
candidate solutions simultaneously (to achieve a good approximation), yet
ensures that agents cannot jump from one solution to the other (to implicitly
enforce truthfulness). Ours is the first mechanism for the problem
where---crucially---the agents are not ordered with respect to their marginal
value per cost. This allows us to appropriately adapt these ideas to the online
setting as well.
To further illustrate the applicability of our approach, we also consider the
case where additional feasibility constraints are present. We obtain
-approximation mechanisms for both monotone and non-monotone submodular
objectives, when the feasible solutions are independent sets of a -system.
With the exception of additive valuation functions, no mechanisms were known
for this setting prior to our work. Finally, we provide lower bounds suggesting
that, when one cares about non-trivial approximation guarantees in polynomial
time, our results are asymptotically best possible.Comment: Accepted to EC 201
On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives
We study a class of procurement auctions with a budget constraint, where an
auctioneer is interested in buying resources or services from a set of agents.
Ideally, the auctioneer would like to select a subset of the resources so as to
maximize his valuation function, without exceeding a given budget. As the
resources are owned by strategic agents however, our overall goal is to design
mechanisms that are truthful, budget-feasible, and obtain a good approximation
to the optimal value. Budget-feasibility creates additional challenges, making
several approaches inapplicable in this setting. Previous results on
budget-feasible mechanisms have considered mostly monotone valuation functions.
In this work, we mainly focus on symmetric submodular valuations, a prominent
class of non-monotone submodular functions that includes cut functions. We
begin first with a purely algorithmic result, obtaining a
-approximation for maximizing symmetric submodular functions
under a budget constraint. We view this as a standalone result of independent
interest, as it is the best known factor achieved by a deterministic algorithm.
We then proceed to propose truthful, budget feasible mechanisms (both
deterministic and randomized), paying particular attention on the Budgeted Max
Cut problem. Our results significantly improve the known approximation ratios
for these objectives, while establishing polynomial running time for cases
where only exponential mechanisms were known. At the heart of our approach lies
an appropriate combination of local search algorithms with results for monotone
submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201
Budget-feasible mechanism design for non-monotone submodular objectives: Offline and online
The framework of budget-feasible mechanism design studies procurement auctions where the auctioneer (buyer) aims to maximize his valuation function subject to a hard budget constraint. We study the problem of designing truthful mechanisms that have good approximation guarantees and never pay the participating agents (sellers) more than the budget. We focus on the case of general (non-monotone) submodular valuation functions and derive the first truthful, budget-feasible and O(1)-approximation mechanisms that run in polynomial time in the value query model, for both offline and online auctions. Since the introduction of the problem by Singer [40], obtaining efficient mechanisms for objectives that go beyond the class of monotone submodular functions has been elusive. Prior to our work, the only O(1)-approximation mechanism known for non-monotone submodular objectives required an exponential number of value queries. At the heart of our approach lies a novel greedy algorithm for non-monotone submodular maximization under a knapsack constraint. Our algorithm builds two candidate solutions simultaneously (to achieve a good approximation), yet ensures that agents cannot jump from one solution to the other (to implicitly enforce truthfulness). Ours is the first mechanism for the problem where-crucially-the agents are not ordered according to their marginal value per cost. This allows us to appropriately adapt these ideas to the online setting as well. To further illustrate the applicability of our approach, we also consider the case where additional feasibility constraints are present, e.g., at most k agents can be selected. We obtain O(p)-approximation mechanisms for both monotone and non-monotone submodular objectives, when the feasible solutions are independent sets of a p-system. With the exception of additive valuation functions, no mechanisms were known for this setting prior to our work. Finally, we provide lower bounds suggesting that, when one cares about non-trivial approximation guaran
Incentive Schemes for Participatory Sensing
We consider a participatory sensing scenario where a group of private sensors observes the same phenomenon, such as air pollution. Since sensors need to be installed and maintained, owners of sensors are inclined to provide inaccurate or random data. We design a novel payment mechanism that incentivizes honest behavior by scoring sensors based on the quality of their reports. The basic principle follows the standard Bayesian Truth Serum (BTS) paradigm, where highest rewards are obtained for reports that are surprisingly common. The mechanism, however, eliminates the main drawback of the BTS in a sensing scenario since it does not require sensors to report predictions regarding the overall distribution of sensors' measurements. As it is the case with other peer prediction methods, the mechanism admits uninformed equilibria. However, in the novel mechanism these equilibria result in worse payoff than truthful reporting
Big Data em cidades inteligentes: um mapeamento sistemático
O conceito de Cidades Inteligentes ganhou maior atenção nos cÃrculos acadêmicos, industriais e governamentais. À medida que a cidade se desenvolve ao longo do tempo, componentes e subsistemas como redes inteligentes, gerenciamento inteligente de água, tráfego inteligente e sistemas de transporte, sistemas de gerenciamento de resÃduos inteligentes, sistemas de segurança inteligentes ou governança eletrônica são adicionados. Esses componentes ingerem e geram uma grande quantidade de dados estruturados, semiestruturados ou não estruturados que podem ser processados usando uma variedade de algoritmos em lotes, microlotes ou em tempo real, visando a melhoria de qualidade de vida dos cidadãos. Esta pesquisa secundária tem como objetivo facilitar a identificação de lacunas neste campo, bem como alinhar o trabalho dos pesquisadores com outros para desenvolver temas de pesquisa mais fortes. Neste estudo, é utilizada a metodologia de pesquisa formal de mapeamento sistemático para fornecer uma revisão abrangente das tecnologias de Big Data na implantação de cidades inteligentes
Mechanism Design for Crowdsourcing: An Optimal 1-1/e Competitive Budget-Feasible Mechanism for Large Markets
In this paper we consider a mechanism design problem in the context of
large-scale crowdsourcing markets such as Amazon's Mechanical Turk,
ClickWorker, CrowdFlower. In these markets, there is a requester who wants to
hire workers to accomplish some tasks. Each worker is assumed to give some
utility to the requester. Moreover each worker has a minimum cost that he wants
to get paid for getting hired. This minimum cost is assumed to be private
information of the workers. The question then is - if the requester has a
limited budget, how to design a direct revelation mechanism that picks the
right set of workers to hire in order to maximize the requester's utility.
We note that although the previous work has studied this problem, a crucial
difference in which we deviate from earlier work is the notion of large-scale
markets that we introduce in our model. Without the large market assumption, it
is known that no mechanism can achieve an approximation factor better than
0.414 and 0.5 for deterministic and randomized mechanisms respectively (while
the best known deterministic and randomized mechanisms achieve an approximation
ratio of 0.292 and 0.33 respectively). In this paper, we design a
budget-feasible mechanism for large markets that achieves an approximation
factor of 1-1/e (i.e. almost 0.63). Our mechanism can be seen as a
generalization of an alternate way to look at the proportional share mechanism
which is used in all the previous works so far on this problem. Interestingly,
we also show that our mechanism is optimal by showing that no truthful
mechanism can achieve a factor better than 1-1/e; thus, fully resolving this
setting. Finally we consider the more general case of submodular utility
functions and give new and improved mechanisms for the case when the markets
are large.Comment: Accepted to FOCS 201