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Truthful Incentives in Crowdsourcing Tasks using Regret Minimization Mechanisms
"... What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester’s utilit ..."
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What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester’s utility and workers ’ profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester’s budget or workers becoming disinterested in the task. In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multiarmed bandits to design mechanisms that are budget feasible, achieve nearoptimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers’ true costs. Our main contribution is a novel, noregret posted price mechanism, BPUCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180 % increase in utility.
The Stackelberg Minimum Spanning Tree Game
 In Proc. of 10th WADS
, 2007
"... Abstract. We consider a oneround twoplayer network pricing game, the Stackelberg Minimum Spanning Tree game or StackMST. The game is played on a graph (representing a network), whose edges are colored either red or blue, and where the red edges have a given fixed cost (representing the competitor’ ..."
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Abstract. We consider a oneround twoplayer network pricing game, the Stackelberg Minimum Spanning Tree game or StackMST. The game is played on a graph (representing a network), whose edges are colored either red or blue, and where the red edges have a given fixed cost (representing the competitor’s prices). The first player chooses an assignment of prices to the blue edges, and the second player then buys the cheapest possible minimum spanning tree, using any combination of red and blue edges. The goal of the first player is to maximize the total price of purchased blue edges. This game is the minimum spanning tree analog of the wellstudied Stackelberg shortestpath game. We analyze the complexity and approximability of the first player’s best strategy in StackMST. In particular, we prove that the problem is APXhard even if there are only two different red costs, and give an approximation algorithm whose approximation ratio is at most min{k, 3 + 2 ln b, 1 + ln W}, where k is the number of distinct red costs, b is the number of blue edges, and W is the maximum ratio between red costs. We also give a natural integer linear programming formulation of the problem, and show that the integrality gap of the fractional relaxation asymptotically matches the approximation guarantee of our algorithm. 1
Social Lending
, 2009
"... Prosper, the largest online social lending marketplace with nearly a million members and $178 million in funded loans, uses an auction amongst lenders to finance each loan. In each auction, the borrower specifies D, the amount he wants to borrow, and a maximum acceptable interest rate R. Lenders spe ..."
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Prosper, the largest online social lending marketplace with nearly a million members and $178 million in funded loans, uses an auction amongst lenders to finance each loan. In each auction, the borrower specifies D, the amount he wants to borrow, and a maximum acceptable interest rate R. Lenders specify the amounts ai they want to lend, and bid on the interest rate, bi, they’re willing to receive. Given that a basic premise of social lending is cheap loans for borrowers, how does the Prosper auction do in terms of the borrower’s payment, when lenders are strategic agents with private true interest rates? The Prosper mechanism is exactly the same as the VCG mechanism applied to a modified instance of the problem, where lender i is replaced by ai dummy lenders, each willing to lend one unit at interest rate bi. However, the two mechanisms behave very differently — the VCG mechanism is truthful, whereas Prosper is not, and the total payment of the borrower can be vastly different in the two mechanisms. We first provide a complete analysis and characterization of the Nash equilibria of the Prosper mechanism. Next, we show that while the borrower’s payment in the VCG mechanism is always within a factor of O(log D) of the payment in any equilibrium of Prosper, even the cheapest Nash equilibrium of the Prosper mechanism can be as large as a factor D of the VCG payment; both factors are tight. Thus, while the Prosper mechanism is a simple uniform price mechanism, it can lead to much larger payments for the borrower than the VCG mechanism. Finally, we provide a model to study Prosper as a dynamic auction, and give tight bounds on the price for a general class of bidding strategies.
FalseNameProof Mechanisms for Hiring a Team
"... Abstract. We study the problem of hiring a team of selfish agents to perform a task. Each agent is assumed to own one or more elements of a set system, and the auctioneer is trying to purchase a feasible solution by conducting an auction. Our goal is to design auctions that are truthful and falsena ..."
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Abstract. We study the problem of hiring a team of selfish agents to perform a task. Each agent is assumed to own one or more elements of a set system, and the auctioneer is trying to purchase a feasible solution by conducting an auction. Our goal is to design auctions that are truthful and falsenameproof, meaning that it is in the agents ’ best interest to reveal ownership of all elements (which may not be known to the auctioneer a priori) as well as their true incurred costs. We first propose and analyze a falsenameproof mechanism for the special cases where each agent owns only one element in reality. We prove that its frugality ratio is bounded by n2 n, which nearly matches a lower bound of Ω(2 n) for all falsenameproof mechanisms in this scenario. We then propose a second mechanism. It requires the auctioneer to choose a reserve cost a priori, and thus does not always purchase a solution. In return, it is falsenameproof even when agents own multiple elements. We experimentally evaluate the payment (as well as social surplus) of the second mechanism through simulation. 1
Auctions for Structured Procurement
, 2007
"... This paper considers a general setting for structured procurement and the problem a buyer faces in designing a procurement mechanism to maximize profit. This brings together two agendas in algorithmic mechanism design, frugality in procurement mechanisms (e.g., for paths and spanning trees) and prof ..."
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This paper considers a general setting for structured procurement and the problem a buyer faces in designing a procurement mechanism to maximize profit. This brings together two agendas in algorithmic mechanism design, frugality in procurement mechanisms (e.g., for paths and spanning trees) and profit maximization in auctions (e.g., for digital goods). In the standard approach to frugality in procurement, a buyer attempts to purchase a set of elements which satisfy a feasibility requirement as cheaply as possible. For profit maximization in auctions, a seller wishes to sell some number of goods for as much as possible. We unify these objectives by endowing the buyer with a decreasing marginal benefit per feasible set purchased and then considering the problem of designing a mechanism to buy a number of sets which maximize the buyer’s profit, i.e., the difference between their benefit for the sets and the cost of procurement. For the case where the feasible sets are bases of a matroid, we follow the approach of reducing the mechanism design optimization problem to a mechanism design decision problem. We give a profit extraction mechanism that solves the decision problem for matroids and show that a reduction based on random sampling approximates the optimal profit. We also consider the problem of nonmatroid procurement and show that in this setting the approach does not succeed.
Mechanisms for complementfree procurement
, 2011
"... We study procurement auctions when the buyer has complementfree (subadditive )objectives in the budget feasibility model [18]. For general subadditive functions we give a randomized universally truthful mechanism which is an O(log² n) approximation, and an O(log³ n) deterministic truthful approxima ..."
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Cited by 7 (4 self)
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We study procurement auctions when the buyer has complementfree (subadditive )objectives in the budget feasibility model [18]. For general subadditive functions we give a randomized universally truthful mechanism which is an O(log² n) approximation, and an O(log³ n) deterministic truthful approximation mechanism; both mechanisms are in the demand oracle model. For cut functions, an interesting case of nonincreasing objectives, we give both randomized and deterministic truthful and budget feasible approximation mechanisms that achieve a constant approximation factor.
On the Price of Truthfulness in Path Auctions
"... Abstract. We study the frugality ratio of truthful mechanisms in path auctions, which measures the extent to which truthful mechanisms “overpay” compared to nontruthful mechanisms. In particular we consider the fundamental case that the graph is composed of two nodedisjoint stpaths of length s1 ..."
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Abstract. We study the frugality ratio of truthful mechanisms in path auctions, which measures the extent to which truthful mechanisms “overpay” compared to nontruthful mechanisms. In particular we consider the fundamental case that the graph is composed of two nodedisjoint stpaths of length s1 and s2 respectively, and prove an optimal √ s1s2 lower bound (an improvement over � s1s2/2). This implies that the √mechanism of Karlin et al. for path auctions is 2competitive (an improvement over 2 √ 2), and is optimal if the graph is a seriesparallel network. Moreover, our results extend to universally truthful randomized mechanisms as well. 1
Adaptive seeding in social networks
 IN FOCS
, 2013
"... The algorithmic challenge of maximizing information diffusion through wordofmouth processes in social networks has been heavily studied in the past decade. Despite immense progress and an impressive arsenal of techniques, the algorithmic framework makes idealized assumptions regarding access to ..."
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Cited by 4 (2 self)
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The algorithmic challenge of maximizing information diffusion through wordofmouth processes in social networks has been heavily studied in the past decade. Despite immense progress and an impressive arsenal of techniques, the algorithmic framework makes idealized assumptions regarding access to the network that can often result in poor performance of stateoftheart techniques. In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a twostage stochastic optimization model designed to leverage the high potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which is a constant factor approximation of the optimal adaptive policy for any influence function in the Triggering model.
Path Auctions with Multiple Edge Ownership
, 2009
"... In this paper, we study path auction games in which multiple edges may be owned by the same agent. The edge costs and the set of edges owned by the same agent are privately known to the owner of the edges. In this setting, we show that, assuming that edges not on the winning path always get 0 paymen ..."
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In this paper, we study path auction games in which multiple edges may be owned by the same agent. The edge costs and the set of edges owned by the same agent are privately known to the owner of the edges. In this setting, we show that, assuming that edges not on the winning path always get 0 payment, there is no individually rational, strategyproof mechanism in which only edge costs are reported. If the agents are asked to report costs as well as identity information, we show that there is no Pareto efficient mechanism that is falsename proof. We then study a firstprice path auction in this model. We show that, in the special case of parallelpath graphs, there is always a Pareto efficient pure strategy ɛNash equilibrium in bids. However, this result does not extend to general graphs: we construct a graph in which there is no Pareto efficient pure strategy ɛNash equilibrium.