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Revenue maximization with a single sample
 IN: PROCEEDINGS OF 12TH ACM CONFERENCE ON ELECTRONIC COMMERCE (2010
"... We design and analyze approximately revenuemaximizing auctions in general singleparameter settings. Bidders have publicly observable attributes, and we assume that the valuations of indistinguishable bidders are independent draws from a common distribution. Crucially, we assume all valuation distr ..."
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Cited by 28 (6 self)
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We design and analyze approximately revenuemaximizing auctions in general singleparameter settings. Bidders have publicly observable attributes, and we assume that the valuations of indistinguishable bidders are independent draws from a common distribution. Crucially, we assume all valuation distributions are a priori unknown to the seller. Despite this handicap, we show how to obtain approximately optimal expected revenue — nearly as large as what could be obtained if the distributions were known in advance — under quite general conditions. Our most general result concerns arbitrary downwardclosed singleparameter environments and valuation distributions that satisfy a standard hazard rate condition. We also assume that no bidder has a unique attribute value, which is obviously necessary with unknown and attributedependent valuation distributions. Here, we give an auction that, for every such environment and unknown valuation distributions, has expected revenue at least a constant fraction of the expected optimal welfare (and hence revenue). A key idea in our auction is to associate each bidder with another that has the same attribute, with the second bidder’s valuation acting as a random reserve price for the first. Conceptually, our analysis shows that even a single sample from a distribution — the second bidder’s valuation — is sufficient information to obtain nearoptimal expected revenue, even in quite general settings.
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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Cited by 20 (1 self)
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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.
On random sampling auctions for digital goods
, 2008
"... In the context of auctions for digital goods, an interesting Random Sampling Optimal Price auction (RSOP) has been proposed by Goldberg, Hartline and Wright; this leads to a truthful mechanism. Since random sampling is a popular approach for auctions that aims to maximize the seller’s revenue, this ..."
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Cited by 18 (0 self)
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In the context of auctions for digital goods, an interesting Random Sampling Optimal Price auction (RSOP) has been proposed by Goldberg, Hartline and Wright; this leads to a truthful mechanism. Since random sampling is a popular approach for auctions that aims to maximize the seller’s revenue, this method has been analyzed further by Feige, Flaxman, Hartline and Kleinberg, who have shown that it is 15competitive in the worst case – which is substantially better than the previously proved bounds but still far from the conjectured competitive ratio of 4. In this paper, we prove that RSOP is indeed 4competitive for a large class of instances in which the number λ of bidders receiving the item at the optimal uniform price, is at least 6. We also show that it is 4.68 competitive for the small class of remaining instances thus leaving a negligible gap between the lower and upper bound. Furthermore, we develop a robust version of RSOP – one in which the seller’s revenue is, with high probability, not much below its mean – when the above parameter λ grows large. We employ a mix of probabilistic techniques and dynamic programming to compute these bounds.
Dynamic Pricing with Limited Supply
, 2012
"... We consider the problem of designing revenue maximizing online postedprice mechanisms when the seller has limited supply. A seller has k identical items for sale and is facing n potential buyers (“agents”) that are arriving sequentially. Each agent is interested in buying one item. Each agent’s val ..."
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Cited by 12 (2 self)
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We consider the problem of designing revenue maximizing online postedprice mechanisms when the seller has limited supply. A seller has k identical items for sale and is facing n potential buyers (“agents”) that are arriving sequentially. Each agent is interested in buying one item. Each agent’s value for an item is an independent sample from some fixed (but unknown) distribution with support [0,1]. The seller offers a takeitorleaveit price to each arriving agent (possibly different for different agents), and aims to maximize his expected revenue. We focus on mechanisms that do not use any information about the distribution; such mechanisms are called detailfree (or priorindependent). They are desirable because knowing the distribution is unrealistic in many practical scenarios. We study how the revenue of such mechanisms compares to the revenue of the optimal offline mechanism that knows the distribution (“offline benchmark”). We present a detailfree online postedprice mechanism whose revenue is at most O((klogn) 2/3) less than the offline benchmark, for every distribution that is regular. In fact, this guarantee holds without any assumptions if the benchmark is relaxed to fixedprice mechanisms. Further, we prove a matching lower bound. The performance guarantee for the same mechanism can be improved toO ( √ klogn), with a distributiondependent constant, if the ratio k n is sufficiently small. We show that, in the worst case over all demand distributions, this is essentially the best rate that can be obtained with a distributionspecific constant. On a technical level, we exploit the connection to multiarmed bandits (MAB). While dynamic pricing with unlimited supply can easily be seen as an MAB problem, the intuition behind MAB approaches breaks when applied to the setting with limited supply. Our highlevel conceptual contribution is that even the limited supply setting can be fruitfully treated as a bandit problem.
Mechanism Design via Consensus Estimates, Cross Checking, and Profit Extraction
, 2012
"... There is only one technique for priorfree optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distri ..."
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Cited by 8 (2 self)
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There is only one technique for priorfree optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the profit extraction and consensus techniques from [5] to structurally rich environments that include, e.g., singleminded combinatorial auctions.
PriorFree Auctions with Ordered Bidders
"... Priorfree auctions are robust auctions that assume no distribution over bidders ’ valuations and provide worstcase (inputbyinput) approximation guarantees. In contrast to previous work on this topic, we pursue good priorfree auctions with nonidentical bidders. Priorfree auctions can approxima ..."
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Cited by 7 (2 self)
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Priorfree auctions are robust auctions that assume no distribution over bidders ’ valuations and provide worstcase (inputbyinput) approximation guarantees. In contrast to previous work on this topic, we pursue good priorfree auctions with nonidentical bidders. Priorfree auctions can approximate meaningful benchmarks for nonidentical bidders only when “sufficient qualitative information ” about the bidder asymmetry is publicly known. We consider digital goods auctions where there is a total ordering of the bidders that is known to the seller, where earlier bidders are in some sense thought to have higher valuations. We use the framework of Hartline and Roughgarden (STOC ’08) to define an appropriate revenue benchmark: the maximum revenue that can be obtained from a bid vector using prices that are nonincreasing in the bidder ordering and bounded above by the secondhighest bid. This monotoneprice benchmark is always as large as the wellknown fixedprice benchmark F (2) , so designing priorfree auctions with good approximation guarantees is only harder. By design, an auction that approximates the monotoneprice benchmark satisfies a very strong guarantee: it is, in particular, simultaneously nearoptimal for essentially every Bayesian environment in which bidders ’ valuation distributions have nonincreasing monopoly prices, or in which the distribution of each bidder stochastically dominates that of the next. Of course, even if there is no distribution over bidders ’ valuations, such an auction still provides a quantifiable inputbyinput performance guarantee. In this paper, we design a simple priorfree auction for digital goods with ordered bidders, the Random Price Restriction (RPR) auction. We prove that its expected revenue on every bid profile b is Ω(M (2) (b) / log ∗ n), where M (2) denotes the monotoneprice benchmark and log ∗ n denotes
Envy, Truth, and Profit
"... We consider profit maximizing (incentive compatible) mechanism design in general environments that include, e.g., position auctions (for selling advertisements on Internet search engines) and singleminded combinatorial auctions. We analyze optimal envyfree pricings in these settings, and give econ ..."
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Cited by 4 (2 self)
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We consider profit maximizing (incentive compatible) mechanism design in general environments that include, e.g., position auctions (for selling advertisements on Internet search engines) and singleminded combinatorial auctions. We analyze optimal envyfree pricings in these settings, and give economic justification for using the optimal revenue of envyfree pricings as a benchmark for priorfree mechanism design and analysis. Moreover, we show that envyfree pricing has a simple nice structure and a strong connection to incentive compatible mechanism design, and we exploit this connection to design priorfree mechanisms with strong approximation guarantees.
Clinching Auctions with Online Supply
"... Auctions for perishable goods such as internet ad inventory need to make realtime allocation and pricing decisions as the supply of the good arrives in an online manner, without knowing the entire supply in advance. These allocation and pricing decisions get complicated when buyers have some global ..."
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Cited by 3 (2 self)
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Auctions for perishable goods such as internet ad inventory need to make realtime allocation and pricing decisions as the supply of the good arrives in an online manner, without knowing the entire supply in advance. These allocation and pricing decisions get complicated when buyers have some global constraints. In this work, we consider a multiunit model where buyers have global budget constraints, and the supply arrives in an online manner. Our main contribution is to show that for this setting there is an individuallyrational, incentivecompatible and Paretooptimal auction that allocates these units and calculates prices on the fly, without knowledge of the total supply. We do so by showing that the Adaptive Clinching Auction satisfies a supplymonotonicity property. We also analyze and discuss, using examples, how the insights gained by the allocation and payment rule can be applied to design better ad allocation heuristics in practice. Finally, while our main technical result concerns multiunit supply, we propose a formal model of online supply that captures scenarios beyond multiunit supply and has applications to sponsored search. We conjecture that our results for multiunit auctions can be extended to these more general models. 1
NearOptimal MultiUnit Auctions with Ordered Bidders
, 2013
"... We construct priorfree auctions with constantfactor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vec ..."
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Cited by 2 (1 self)
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We construct priorfree auctions with constantfactor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vector using supplyrespecting prices that are nonincreasing in the bidder ordering and bounded above by the secondhighest bid. As a consequence, our auctions are simultaneously nearoptimal in a wide range of Bayesian multiunit environments.