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Arpita Ghosh

Publications and source records attributed to Arpita Ghosh.

30 records · Page 2Linked to original sources
arXiv2012

Crowdsourcing with Endogenous Entry

We investigate the design of mechanisms to incentivize high quality in crowdsourcing environments with strategic agents, when entry is an endogenous, strategic choice. Modeling endogenous entry in crowdsourcing is important because there is a nonzero cost to making a contribution of any quality which can be avoided by not participating, and indeed many sites based on crowdsourced content do not have adequate participation. We use a mechanism with monotone, rank-based, rewards in a model where agents strategically make participation and quality choices to capture a wide variety of crowdsourcing environments, ranging from conventional crowdsourcing contests to crowdsourced content as in online Q&A forums. We first explicitly construct the unique mixed-strategy equilibrium for such monotone rank-order mechanisms, and use these participation probabilities and quality distribution to address the design of incentives for two kinds of rewards that arise in crowdsourcing. We first show that for attention rewards as in crowdsourced content, the entire equilibrium distribution improves when the rewards for every rank but the last are as high as possible. In particular, when producing the lowest quality content has low cost, the optimal mechanism displays all but the worst contribution. We next investigate settings where there is a total reward that can be arbitrarily distributed amongst all participants, as in crowdsourcing contests. Unlike with exogenous entry, here the expected number of participants can be increased by subsidizing entry, which could potentially improve the expected quality of the best contribution. However, we show that free entry is dominated by taxing entry- making all entrants pay a small fee, which is rebated to the winner along with whatever rewards were already assigned, can improve the quality of the best contribution over a winner-take-all contest with no taxes.

cs.GT↗
arXiv2012

Implementing Optimal Outcomes in Social Computing: A Game-Theoretic Approach

In many social computing applications such as online Q&A forums, the best contribution for each task receives some high reward, while all remaining contributions receive an identical, lower reward irrespective of their actual qualities. Suppose a mechanism designer (site owner) wishes to optimize an objective that is some function of the number and qualities of received contributions. When potential contributors are strategic agents, who decide whether to contribute or not to selfishly maximize their own utilities, is such a "best contribution" mechanism, M_B, adequate to implement an outcome that is optimal for the mechanism designer? We first show that in settings where a contribution's value is determined primarily by an agent's expertise, and agents only strategically choose whether to contribute or not, contests can implement optimal outcomes: for any reasonable objective, the rewards for the best and remaining contributions in M_B can always be chosen so that the outcome in the unique symmetric equilibrium of M_B maximizes the mechanism designer's utility. We also show how the mechanism designer can learn these optimal rewards when she does not know the parameters of the agents' utilities, as might be the case in practice. We next consider settings where a contribution's value depends on both the contributor's expertise as well as her effort, and agents endogenously choose how much effort to exert in addition to deciding whether to contribute. Here, we show that optimal outcomes can never be implemented by contests if the system can rank the qualities of contributions perfectly. However, if there is noise in the contributions' rankings, then the mechanism designer can again induce agents to follow strategies that maximize his utility. Thus imperfect rankings can actually help achieve implementability of optimal outcomes when effort is endogenous and influences quality.

cs.GT↗
arXiv2011

Selling Privacy at Auction

We initiate the study of markets for private data, though the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply. On the other hand, the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such auctions can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions for two natural settings which are optimal up to small constant factors: 1. In the setting in which the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. 2. In the setting in which the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analysts budget. In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. In both of these results, we ignore the privacy cost due to possible correlations between an individuals private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy.

cs.GT↗
arXiv2011

Anisotropic Nature of Work Function in Strong Quantizing Magnetic Field

Following an extremely interesting idea \cite{R1}, published long ago, the work function associated with the emission of ultra-relativistic electrons from magnetically deformed metallic crystal of astrophysical relevance is obtained using relativistic version of Thomas-Fermi type model. In the present scenario, surprisingly, the work function becomes anisotropic; the longitudinal part is an increasing function of magnetic field strength, whereas the transverse part diverges.

astro-ph.SR↗
arXiv2011

Truthful Assignment without Money

We study the design of truthful mechanisms that do not use payments for the generalized assignment problem (GAP) and its variants. An instance of the GAP consists of a bipartite graph with jobs on one side and machines on the other. Machines have capacities and edges have values and sizes; the goal is to construct a welfare maximizing feasible assignment. In our model of private valuations, motivated by impossibility results, the value and sizes on all job-machine pairs are public information; however, whether an edge exists or not in the bipartite graph is a job's private information. We study several variants of the GAP starting with matching. For the unweighted version, we give an optimal strategyproof mechanism; for maximum weight bipartite matching, however, we show give a 2-approximate strategyproof mechanism and show by a matching lowerbound that this is optimal. Next we study knapsack-like problems, which are APX-hard. For these problems, we develop a general LP-based technique that extends the ideas of Lavi and Swamy to reduce designing a truthful mechanism without money to designing such a mechanism for the fractional version of the problem, at a loss of a factor equal to the integrality gap in the approximation ratio. We use this technique to obtain strategyproof mechanisms with constant approximation ratios for these problems. We then design an O(log n)-approximate strategyproof mechanism for the GAP by reducing, with logarithmic loss in the approximation, to our solution for the value-invariant GAP. Our technique may be of independent interest for designing truthful mechanisms without money for other LP-based problems.

cs.GT↗
arXiv2011

A Theoretical Study of the Magnetically Deformed Inner Crust Matter of Magnetars

We have studied various physical properties of magnetically deformed atoms and the associated matter, replacing the atoms by the deformed Wigner-Seitz (WS) cells at the crustal region of strongly magnetized neutron stars (magnetars). A relativistic version of Thomas-Fermi (TF) model in presence of strong magnetic field in cylindrical coordinates is used to study the properties of such matter.

astro-ph.SR↗
arXiv2010

A Relativistic Generalization of Fowler-Nordheim Cold Emission in Presence of Strong Magnetic Field

A relativistic version of cold emission of electrons in presence of strong magnetic field, relevant for strongly magnetized neutron stars is obtained. It is found that in this scenario, a scalar type potential barrier does not allow quantum tunneling through the surface. Whereas, in presence of a vector type surface barrier, the probability of electron emission is much larger compared to the original Fowler-Nordheim cold emission of electrons. It is found that the relativistic version in presence of strong magnetic field does not follow exponential decay.

astro-ph.SR↗
arXiv2010

Competitive Equilibria in Matching Markets with Budgets

We study competitive equilibria in the classic Shapley-Shubik assignment model with indivisible goods and unit-demand buyers, with budget constraints: buyers can specify a maximum price they are willing to pay for each item, beyond which they cannot afford the item. This single discontinuity introduced by the budget constraint fundamentally changes the properties of equilibria: in the assignment model without budget constraints, a competitive equilibrium always exists, and corresponds exactly to a stable matching. With budgets, a competitive equilibrium need not always exist. In addition, there are now two distinct notions of stability, depending on whether both or only one of the buyer and seller can strictly benefit in a blocking pair, that no longer coincide due to the budget-induced discontinuity. We define weak and strong stability for the assignment model with transferable utilities, and show that competitive equilibria correspond exactly to strongly stable matchings. We consider the algorithmic question of efficiently computing competitive equilibria in an extension of the assignment model with budgets, where each buyer specifies his preferences over items using utility functions $u_{ij}$, where $u_{ij}(p_j)$ is the utility of buyer $i$ for item $j$ when its price is $p_j$. Our main result is a strongly polynomial time algorithm that decides whether or not a competitive equilibrium exists and if yes, computes a minimum one, for a general class of utility functions $u_{ij}$. This class of utility functions includes the standard quasi-linear utility model with a budget constraint, and in addition, allows modeling marketplaces where, for example, buyers only have a preference ranking amongst items subject to a maximum payment limit for each item, or where buyers want to optimize return on investment (ROI) instead of a quasi-linear utility and only know items' relative values.

cs.GT↗
arXiv2009

Work Function of Strongly Magnetized Neutron Star Crustal Matter and the Associated Magneto-Sphere

Following an extremely interesting idea \cite{R1}, published long ago, the work function at the outer crust region of a strongly magnetized neutron star is obtained using relativistic version of Thomas-Fermi type model. In the present scenario, the work function becomes anisotropic; the longitudinal part is an increasing function of magnetic field strength, whereas the transverse part diverges. An approximate estimate of the electron density in the magnetosphere due to field emission and photo emission current, from the polar cap region are obtained.

astro-ph.HE↗
arXiv2009

Bidding for Representative Allocations for Display Advertising

Display advertising has traditionally been sold via guaranteed contracts -- a guaranteed contract is a deal between a publisher and an advertiser to allocate a certain number of impressions over a certain period, for a pre-specified price per impression. However, as spot markets for display ads, such as the RightMedia Exchange, have grown in prominence, the selection of advertisements to show on a given page is increasingly being chosen based on price, using an auction. As the number of participants in the exchange grows, the price of an impressions becomes a signal of its value. This correlation between price and value means that a seller implementing the contract through bidding should offer the contract buyer a range of prices, and not just the cheapest impressions necessary to fulfill its demand. Implementing a contract using a range of prices, is akin to creating a mutual fund of advertising impressions, and requires {\em randomized bidding}. We characterize what allocations can be implemented with randomized bidding, namely those where the desired share obtained at each price is a non-increasing function of price. In addition, we provide a full characterization of when a set of campaigns are compatible and how to implement them with randomized bidding strategies.

cs.MA↗
arXiv2009

Universally Utility-Maximizing Privacy Mechanisms

A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Privacy can be rigorously quantified using the framework of {\em differential privacy}, which requires that a mechanism's output distribution is nearly the same whether or not a given database row is included or excluded. The goal of this paper is strong and general utility guarantees, subject to differential privacy. We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a loss function). Our main result is: for each fixed count query and differential privacy level, there is a {\em geometric mechanism} $M^*$ -- a discrete variant of the simple and well-studied Laplace mechanism -- that is {\em simultaneously expected loss-minimizing} for every possible user, subject to the differential privacy constraint. This is an extremely strong utility guarantee: {\em every} potential user $u$, no matter what its side information and preferences, derives as much utility from $M^*$ as from interacting with a differentially private mechanism $M_u$ that is optimally tailored to $u$.

cs.DB↗