164 research outputs found
Crowdsourced Bayesian auctions
We investigate the problem of optimal mechanism design, where an auctioneer wants to sell a set of goods to buyers, in order to maximize revenue. In a Bayesian setting the buyers' valuations for the goods are drawn from a prior distribution D, which is often assumed to be known by the seller. In this work, we focus on cases where the seller has no knowledge at all, and "the buyers know each other better than the seller knows them". In our model, D is not necessarily common knowledge. Instead, each buyer individually knows a posterior distribution associated with D. Since the seller relies on the buyers' knowledge to help him set a price, we call these types of auctions crowdsourced Bayesian auctions.
For this crowdsourced Bayesian model and many environments of interest, we show that, for arbitrary valuation distributions D (in particular, correlated ones), it is possible to design mechanisms matching to a significant extent the performance of the optimal dominant-strategy-truthful mechanisms where the seller knows D.
To obtain our results, we use two techniques: (1) proper scoring rules to elicit information from the players; and (2) a reverse version of the classical Bulow-Klemperer inequality. The first lets us build mechanisms with a unique equilibrium and good revenue guarantees, even when the players' second and higher-order beliefs about each other are wrong. The second allows us to upper bound the revenue of an optimal mechanism with n players by an n/n--1 fraction of the revenue of the optimal mechanism with n -- 1 players. We believe that both techniques are new to Bayesian optimal auctions and of independent interest for future work.United States. Office of Naval Research (Grant number N00014-09-1-0597
Brief Announcement: Bayesian Auctions with Efficient Queries
Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations.
In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players\u27 value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be of any constant approximation to the optimal revenue. For single-item auctions and multi-item auctions with unit-demand or additive valuation functions, we prove tight upper-bounds via efficient query schemes, without requiring the distributions to be regular or have monotone hazard rate. Thus, in those auction settings the seller needs to access much less than the full distributions in order to achieve approximately optimal revenue
Mean Field Equilibria for Competitive Exploration in Resource Sharing Settings
We consider a model of nomadic agents exploring and competing for
time-varying location-specific resources, arising in crowdsourced
transportation services, online communities, and in traditional location based
economic activity. This model comprises a group of agents, and a set of
locations each endowed with a dynamic stochastic resource process. Each agent
derives a periodic reward determined by the overall resource level at her
location, and the number of other agents there. Each agent is strategic and
free to move between locations, and at each time decides whether to stay at the
same node or switch to another one. We study the equilibrium behavior of the
agents as a function of dynamics of the stochastic resource process and the
nature of the externality each agent imposes on others at the same location. In
the asymptotic limit with the number of agents and locations increasing
proportionally, we show that an equilibrium exists and has a threshold
structure, where each agent decides to switch to a different location based
only on their current location's resource level and the number of other agents
at that location. This result provides insight into how system structure
affects the agents' collective ability to explore their domain to find and
effectively utilize resource-rich areas. It also allows assessing the impact of
changing the reward structure through penalties or subsidies.Comment: 17 pages, 1 figure, 1 table, to appear in proceedings of the 25th
International World Wide Web Conference(WWW2016
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The Irrefutable History of You: Distributed Ledgers and Semantics for Ubiquitous Personal Ratings
A recurring theme in the science-fiction series Black Mirror is the consequence for society of an over-focus on social networking. The episode Nosedive imagines a future in which every public interaction a person has is rated by the other parties, and every aspect of ones life depends on the overall rating computed from these. In this paper, we show how such a scenario is already technically possible using existing technologies such as distributed ledgers, and discuss means by which the negative possibilities may be ameliorated using semantic approaches
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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