Speaking of STOC, early registration deadline of April 28th is fast approaching. Bill and I will both be there.

Lots of great papers in the Complexity Conference headlined by Braverman but let me focus on the paper One-Way Functions and the Isomorphism Conjecture by Manindra Agrawal and Osamu Watanabe. That is the Agrawal of Primes in P fame. Kayal and Saxena each have Complexity papers as well.

Many complexity theorists don't believe the Berman-Hartmanis Isomorphism Conjecture, that all NP-complete sets are essentially the same, because the conjecture fails given sufficiently hard one-way functions. Agrawal and Watanabe argue that the one-way functions that we commonly see in cryptography have an easy to invert "cylinder" which allows one to achieve an isomorphism for NP-complete sets based on using these functions as reductions. Agrawal and Watanabe show roughly that under an assumption that all such functions have such cylinders one gets nonuniform isomorphisms.

Given the Agrawal-Watanabe paper I started to believe that maybe the Berman-Hartmanis conjecture holds after all. On the other hand, Oded Goldreich has since given a candidate one-way function that may not have the cylinder property. Now I don't know what to believe.

It would be nice if these conferences upload the abstracts of the accepted papers... following the spirit of recent SODA and STOC.

ReplyDeleteLance is really very creative in the way he released the EC acceptance list :-)

ReplyDeleteI uploaded a list of the EC accepts with abstracts.

ReplyDeleteAwesome !! Thanks Lance.

ReplyDeletehttp://ar.wikipedia.org/wiki/نقاش_المستخدم:علي_الباشا_2

ReplyDelete2009 ACM Electronic Commerce

ReplyDeleteAccepted Papers with Abstracts

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Pricing Traffic in a Spanning Network

Herve Moulin, Rice University

Each user of the network needs to connect a pair of target nodes. There are no variable congestion costs, only a direct connection cost for each pair of nodes. A centralized mechanism elicits target pairs from users, and builds the cheapest forest meeting all demands. We look for cost sharing rules satisfying Routing-proofness: no user can lower its cost by reporting as several users along an alternative path connecting his target nodes; Stand Alone core stability: no group of users pay more than the cost of a sub-network meeting all connection needs of the group. We construct first two core stable and routing-proof rules when connecting costs are all 0 or 1. One is derived from the random spanning tree weighted by the volume of traffic on each edge; the other is the weighted Shapley value of the Stand Alone cooperative game. For arbitrary connecting costs, we prove that the core is non empty if the graph of target pairs connects all pairs of nodes. Then we extend both rules above by the piecewise-linear technique. The former rule is computable in polynomial time, the latter is not.

Modeling Volatility in Prediction Markets

Nikolay Archak, New York University

Panagiotis Ipeirotis, New York University

Nowadays, there is a significant experimental evidence that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods. Interpretation of market prices as probabilities has been extensively studied in the literature. However there is very little research on the volatility of prediction market prices. Given that volatility is fundamental in estimating the significance of price movements, it is important to have a better understanding of volatility of the contract prices. In this paper, we present a model of a prediction market with a binary payoff on a competitive event involving two parties. In our model, each party has underlying "ability'' process that describes its potential to win and evolves as an Ito diffusion, a generalized form of a Brownian motion. We show that, if the prediction market is efficient and unbiased, the price of the corresponding contract also follows a diffusion and its instantaneous volatility is a function of the current contract price and its time to expiration. In the experimental section, we validate our model on a set of InTrade prediction markets and show that it is consistent with observed volatility of contract returns and outperforms existing volatility models in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction market contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors.

But Who Will Monitor the Monitor?

David Rahman, University of Minnesota

Consider a group of individuals in a strategic environment with moral hazard and adverse selection, and suppose that providing incentives for a given outcome requires a monitor to detect deviations. What about the monitor’s deviations? This paper analyzes mediated contracts to answer such a question, and asserts that the monitor's deviations are effectively irrelevant. Hence, nobody needs to monitor the monitor. The paper also characterizes exactly when such contracts can provide monitors with the right incentives to perform. In doing so, several characterizations of virtual implementation are derived.

Policy Teaching Through Reward Function Learning

Haoqi Zhang, Harvard University

David Parkes, Harvard University

Yiling Chen, Harvard University

Policy teaching [21] considers a Markov Decision Process setting in which an interested party aims to influence an agent's decisions by providing limited incentives. In this paper, we consider the specific objective of inducing a pre-specified desired policy. We examine both the case in which the agent's reward function is known and unknown to the interested party, presenting a linear program for the former case and formulating an active, indirect elicitation method for the latter. We provide conditions for convergence and logarithmic convergence, and present a polynomial time algorithm that ensures logarithmic elicitation convergence with arbitrarily high probability (neither guarantees are available for the alternate, value-based policy teaching model of Zhang and Parkes [21]). We adapt a two-sided slack maximization heuristic to this setting, and demonstrate its effectiveness in a simulated ad-network setting. We extend our methods to settings with partial observations and partial target policies, and offer a game-theoretic interpretation of our methods.

Eliciting Truthful Answers to Multiple-Choice Questions

Nicolas Lambert, Stanford University

Yoav Shoham, Stanford University

Motivated by the prevalence of online questionnaires in electronic commerce, and of multiple-choice questions in such questionnaires, we consider the problem of eliciting truthful answers to multiple-choice questions from a knowledgeable respondent. Specifically, each question is a statement regarding an uncertain future event, and is multiple-choice -- the responder must select exactly one of the given answers. The principal offers a payment, whose amount is a function of the answer selected and the true outcome (which the principal will eventually observe). This problem significantly generalizes recent work on truthful elicitation of distribution properties, which itself generalized a long line of work in elicitation of complete distributions. Our results include necessary and sufficient conditions for the existence of payments that induce truthful answers, and provide various characterizations of those payments. We study in greater details the common case of questions with ordinal answers. Finally, we design prediction markets adapted for the purpose of answering multiple-choice questions.

Computational Analysis of Perfect-Information Position Auctions

David Thompson, University of British Columbia

Kevin Leyton-Brown, University of British Columbia

Position auctions were widely used by search engines to sell keyword advertising before being well understood (and, indeed, studied) theoretically. To date, theorists have made significant progress, for example showing that a given auction is efficient or revenue-dominates a benchmark auction such as VCG. This paper augments that line of work, relying on computational equilibrium analysis. By computing Nash equilibria and calculating their expected revenue and social welfare, we can quantitatively answer questions that theoretical methods have not. Broadly, the questions we answer are: (1) How often do the theoretically predicted ``good'' (i.e. efficient, high-revenue) equilibria of GSP occur? (2) In models where GSP is known to be inefficient, how much welfare does it waste? We also use our data to examine the larger question: Is GSP a good choice, compared with the alternatives?

A New Perspective on Implementation by Voting Trees

Felix Fischer, Ludwig-Maximilians University

Ariel Procaccia, Microsoft

Alex Samorodnitsky, Hebrew University

Voting trees describe an iterative procedure for selecting a single vertex from a tournament. They provide a very general abstract model of decision-making among a group of individuals, and it has therefore been studied for which voting rules there exists a tree implementing them, i.e., one that chooses according to the rule for every tournament. While partial results concerning implementable rules and necessary conditions for implementability have been obtained over the past forty years, a complete characterization of voting rules implementable by trees has proven surprisingly hard to find. A prominent rule that cannot be implemented by trees is the Copeland rule, which singles out vertices with maximum degree. In this paper, we suggest a new angle of attack and re-examine the implementability of the Copeland solution using paradigms and techniques that are at the core of theoretical computer science. Indeed, we study the extent to which voting trees can approximate the maximum degree in a tournament, and give upper and lower bounds on the worst-case ratio between the degree of the vertex chosen by a tree and the maximum degree, both for the deterministic model concerned with a single fixed tree, and for randomizations over arbitrary sets of trees. Our main positive result is a randomization over surjective trees of polynomial size that provides an approximation ratio of at least 1/2. The proof is based on a connection between a randomization over caterpillar trees and a rapidly mixing Markov chain.

Approximate Mechanism Design Without Money

Ariel Procaccia, Microsoft

Moshe Tennenholtz, Microsoft

The literature on algorithmic mechanism design is mostly concerned with game-theoretic versions of optimization problems to which standard economic money-based mechanisms cannot be applied efficiently. Recent years have seen the design of various truthful approximation mechanisms that rely on enforcing payments. In this paper, we advocate the reconsideration of highly structured optimization problems in the context of mechanism design. We argue that, in such domains, approximation can be leveraged to obtain truthfulness without resorting to payments. This stands in contrast to previous work where payments are ubiquitous, and (more often than not) approximation is a necessary evil that is required to circumvent computational complexity. We present a case study in approximate mechanism design without money. In our basic setting agents are located on the real line and the mechanism must select the location of a public facility; the cost of an agent is its distance to the facility. We establish tight upper and lower bounds for the approximation ratio given by strategyproof mechanisms without payments, with respect to both deterministic and randomized mechanisms, under two objective functions: the social cost, and the maximum cost. We then extend our results in two natural directions: a domain where two facilities must be located, and a domain where each agent controls multiple locations.

Information Aggregation in Dynamic Markets with Strategic Traders

Michael Ostrovsky, Stanford University

This paper studies information aggregation in dynamic markets with partially informed strategic traders. A natural condition on traded securities and information structures, "separability," is introduced. If a security is separable under a given information structure, then information about its value always gets aggregated, for any prior distribution over the states of the world: In equilibrium, the market price of the security converges to its expected value given the traders' pooled information. If the security is non-separable, then there exists a prior such that information does not get aggregated. Special cases satisfying separability include Arrow-Debreu securities, whose value is one in one state of the world and zero in all others, and "additive" securities, whose value is the sum of traders' signals.

Social Lending

Ning Chen, Nanyang Technological University

Arpita Ghosh, Yahoo! Research

Nicolas Lambert, Stanford University

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 a_i they want to lend, and bid on the interest-rate, b_i, 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. However, the two mechanisms behave very differently --- VCG is truthful, whereas Prosper is not, and the total payment can be vastly different. We first provide a complete analysis of the Nash equilibria of the Prosper mechanism. Next, we show that while the payment in the VCG mechanism is always within a factor of O(log D) of that in any equilibrium of Prosper, even the cheapest Nash equilibrium of Prosper can be as large as a factor D of VCG; both factors are tight. Thus, while the Prosper mechanism is a simple uniform price mechanism, it can lead to much larger payments than VCG. 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.

On the Complexity of Nash Dynamics and Sink equilibria

Vahab Mirrokni, Google

Alexander Skopalik, RWTH Aachen

Studying Nash dynamics is an important approach for analyzing the outcome of games with repeated behavior of self-interested agents. Sink equilibria have been introduced for studying social cost on Nash dynamics over pure strategies in games. However, they do not address the complexity of sink equilibria in these games. Recently, Fabrikant and Papadimitriou study complexity of Nash dynamics in two classes of games. In order to understand the complexity of Nash dynamics in a variety of games, we study the following three questions: (i) given a state in game, can we verify if this state is in a sink equilibrium? (ii) given an instance of a game, can we verify if there exists any sink equilibrium other than pure Nash equilibria? and (iii) given an instance of a game, can we verify if there exists a pure Nash equilibrium? In this paper, we almost answer all of the above questions for a variety of classes of games with succinct representation, including anonymous games, player specific and weighted congestion games, valid-utility games, and two-sided market games. In particular, for most of these problems, we show that (i) it is PSPACE-complete to verify if a given state is in a sink equilibrium, (ii) it is NP-hard to verify if there exists a pure Nash equilibrium in the game, (iii) it is PSPACE-complete to verify if there exists any sink equilibrium other than pure Nash equilibria. We illustrate general techniques that could be used to answer similar questions in other classes of games.

Self-Correcting Sampling-Based Dynamic Multi-Unit Auctions

Florin Constantin, Harvard

David Parkes, Harvard

In this paper we exploit methods of sample-based stochastic optimization for the purpose of strategyproof dynamic, multi-unit auctions. There are no analytic characterizations of optimal policies for this domain and thus a heuristic approach, such as that proposed here, seems necessary in practice. Following the suggestion of Parkes and Duong in an earlier paper, we perform sensitivity analysis on the allocation decisions of an online algorithm for stochastic optimization, and correct the decisions to enable a strategyproof auction. In applying this approach to the allocation of non-expiring goods, the technical problem that we must address is related to achieving strategyproofness for reports of departure. This cannot be achieved through self-correction without canceling many allocation decisions, and must instead be achieved by first modifying the underlying algorithm. We introduce the NowWait method for this purpose, prove its successful interfacing with sensitivity analysis and demonstrate good empirical performance. Our method is quite general, requiring a technical property of uncertainty independence and that values are not too positively correlated with agent patience. We also show how to incorporate “virtual valuations” (due to Myerson) in our method to increase the seller’s revenue.

Managing the Quality of CPC Traffic

Bobji Mungamuru, Stanford University

Hector Garcia-Molina, Stanford University

We show how an online advertising network can use filtering, predictive pricing and revenue sharing together to manage the quality of cost-per-click (CPC) traffic. Our results suggest that predictive pricing alone can and should be used instead of filtering to manage organic traffic quality, whereas either method can be used to deter click inflation.

An Exact Almost Optimal Algorithm for Target Set Selection in Social Networks

Oren Ben-Zwi, Haifa University

Danny Hermelin, Haifa University

Daniel Lokshtanov, University of Bergen

Ilan Newman, Haifa University

The Target Set Selection problem proposed by Kempe, Kleinberg, and Tardos, gives a nice clean combinatorial formulation for many problems arising in economy, sociology, and medicine. Its input is a graph with vertex thresholds, the social network, and the goal is to find a subset of vertices, the target set, that “activates” a prespecified number of vertices in the graph. Activation of a vertex is defined via a so-called activation process as follows: Initially, all vertices in the target set become active. Then at each step i of the process, each vertex gets activated if the number of its active neighbors at iteration i - 1 exceeds its threshold. Unsurprisingly perhaps, Target Set Selection is NP-complete. More surprising is the fact that both of its maximization and minimization variants turn out to be extremely hard to approximate, even for very restrictive special cases. Our contribution is twofold: First, we present an algorithm for Target Set Selection running in n^O(w) time, for graphs with n vertices and treewidth bounded by w. The algorithm utilizes various combinatorial properties of the problem; drifting somewhat from standard dynamic-programming algorithms for small treewidth graphs. Also, it can be adopted to much more general settings, including the case of directed graphs, weighted edges, and weighted vertices. On the other hand, we also show that it is highly unlikely to find an n^o(sqrt{w}) time algorithm for Target Set Selection, as this would imply a sub-exponential algorithm for all problems in SNP.

Revenue Submodularity

Mukund Sundararajan, Stanford

Tim Roughgarden, Stanford

Shaddin Dughmi, Stanford

We introduce {\em revenue submodularity}, the property that market expansion has diminishing returns on an auction's expected revenue. We prove that revenue submodularity is generally possible only in matroid markets, that Bayesian-optimal auctions are always revenue-submodular in such markets, and that the VCG mechanism is revenue-submodular in matroid markets with IID bidders and ``sufficient competition''. We also give two applications of revenue submodularity: good approximation algorithms for novel market expansion optimization problems, and approximate revenue guarantees for the VCG mechanism with IID bidders.

On Random Sampling Auctions for Digital Goods

Saeed Alaei, UMD-CP

Azarakhsh Malekian, UMD-CP

Aravind Srinivasan, UMD-CP

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 $15$-competitive 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 $4$-competitive for a large class of instances in which there are at least 6 bids greater than or equal to the optimal price. 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. We employ a mix of probabilistic techniques and dynamic programming to compute those bounds.

Crowdsourcing and All-Pay Auctions

Dominic DiPalantino, Stanford University

Milan Vojnovic, Microsoft Research Cambridge

In this paper we present and analyze a model in which users select among, and subsequently compete in, a collection of contests offering various rewards. The objective is to capture the essential features of a crowdsourcing system, an environment in which diverse tasks are presented to a large community. We aim to demonstrate the precise relationship between incentives and participation in such systems. We model contests as all-pay auctions with incomplete information; as a consequence of revenue equivalence, our model may also be interpreted more broadly as one in which users select among auctions of heterogeneous goods. We present two regimes in which we find an explicit correspondence in equilibrium between the offered rewards and the users' participation levels. The regimes respectively model situations in which different contests require similar or unrelated skills. Principally, we find that rewards yield logarithmically diminishing returns with respect to participation levels. We compare these results to empirical data from the crowdsourcing site Taskcn.com; we find that as we conditionalize the data on more experienced users, the model more closely conforms to the empirical data.

On the Price of Mediation

Milan Bradonjic, Los Alamos National Labs

Gunes Ercal-Ozkaya, Kansas University

Adam Meyerson, UCLA

Alan Roytman, UCLA

We study the relationship between the social cost of correlated equilibria and the social cost of Nash equilibria. In contrast to previous work focusing on the possible benefits of a benevolent mediator, we define and bound the Price of Mediation (PoM): the ratio of the cost of the worst correlated equilibrium to the cost of the worst Nash. We observe that in practice, the heuristics used for mediation are frequently non-optimal, and from an economic perspective mediators may be inept or self-interested. Recent results on computation of equilibria also motivate our work. We consider the Price of Mediation for general games with small numbers of players and pure strategies. For games with two players each having two pure strategies we prove a tight bound of two on the PoM. For larger games (either more players, or more pure strategies per player, or both) we show that the PoM can be unbounded. Most of our results focus on symmetric congestion games (also known as load balancing games). We show that for general convex cost functions, the PoM can grow exponentially in the number of players. We prove that PoM is one for linear costs and at most a small constant (but can be larger than one) for concave costs. For polynomial cost functions, we prove bounds on the PoM which are exponential in the degree.

A Unified Framework for Dynamic Pari-Mutuel Information Market Design

Shipra Agrawal, Stanford University

Erick Delage, Stanford University

Mark Peters, Stanford University

Zizhuo Wang, Stanford University

Yinyu Ye, Stanford University

Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson [7] introduced a market maker derived from the logarithmic scoring rule, and later Chen & Pennock [5] developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et al. [9] is based on ideas from a call auction setting using a convex optimization model. In this work, we develop a unified framework that bridges these seemingly unrelated models for centrally organizing contingent claim markets. The framework, developed as a generalization of SCPM, will support many desirable properties such as proper scoring, truthful bidding (in a myopic sense), efficient computation, and guarantees on worst case loss. In fact, our unified framework will allow us to express various proper scoring rules, existing or new, from classical utility functions in a convex optimization problem representing the market organizer. Additionally, we utilize concepts from duality to show that the market model is equivalent to a risk minimization problem where a convex risk measure is employed. This will allow us to more clearly understand the differences in the risk attitudes adopted by various mechanisms, and particularly deepen our intuition about popular mechanisms like Hanson’s market-maker. In aggregate, we believe this work advances our understanding of the objectives that the market organizer is optimizing in popular parimutuel mechanisms by recasting them into one unified framework.

Characterizing Truthful Multi-Armed Bandit Mechanisms

Moshe Babaioff, Microsoft Research

Yogeshwer Sharma, Cornell University

Aleksandrs Slivkins, Microsoft Research

We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. If the advertisers bid their true private values, our problem is equivalent to the multi-armed bandit problem, and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by "regret", the difference in social welfare between the algorithm and the benchmark which always selects the same "best" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that truthful mechanisms have certain strong structural properties -- essentially, they must separate exploration from exploitation -- *and* they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret.

Substitutes or Complements: Another Step Forward in Recommendations

Jiaqian Zheng, Fudan University

Xiaoyuan Wu, eBay Research Lab

Junyu Niu, Fudan University

Alvaro Bolivar, eBay Research Lab

In this paper, we introduce the method tagging substitute-complement attributes on miscellaneous recommending relations, and elaborate how this step contributes to electronic merchandising. There are already decades of works in building recommender systems. Steadily outperforming previous algorithms is difficult under the conventional framework. However, in real merchandising scenarios, we find describing the weight of recommendation simply as a scalar number is hardly expressive, which hinders the further progress of recommender systems. We study a large log of user browsing data, revealing the typical substitute-complement relations among items that can further extend recommender systems in enriching the presentation and improving the practical quality. Finally, we provide an experimental analysis and sketch an online prototype to show that tagging attributes can grant more intelligence to recommender systems by differentiating recommended candidates to fit respective scenarios.

Designing Incentives for Online Question and Answers Forums

Shaili Jain, Harvard University

Yiling Chen, Harvard University

David Parkes, Harvard University

In this paper, we provide a simple game-theoretic model of an online question and answer forum. Each user has a unique piece of information that is relevant to a question and can decide when to report this information. The asker prefers to receive information sooner rather than later, and will stop the process when satisfied with the cumulative value of the posted information. The information aggregates as it is posted, with previous pieces of information combined into the latest answers. We consider two distinct cases: a complements case, in which each successive piece of information is worth more to the asker than the previous one; and a substitutes case, in which each successive piece of information is worth less. A ``best answer'' scoring rule is adopted to model Yahoo! Answers, and is effective for substitutes information, where it isolates an equilibrium in which all users respond in the first round. But we find that this rule is ineffective for complements information, isolating instead an equilibrium in which all users respond in the final round. In addressing this, we demonstrate that an ``approval voting'' scoring rule and a ``buyer-distributes-the-points'' scoring rule can improve the performance of the system with complements information, by providing incentives for early responders as well as the user to submit the final answer.

Destroy to Save

Geoffroy de Clippel, Brown University

Victor Naroditskiy, Brown University

Amy Greenwald, Brown University

We study the problem of how to allocate $m$ identical items among $n>m$ agents, assuming each agent desires exactly one item and has a private value for consuming the item. We assume the items are jointly owned by the agents, not by one uninformed center, so an auction cannot be used to solve our problem. Instead, the agents who receive items compensate those who do not. This problem has been studied by others recently, and their solutions have modified the classic VCG mechanism. Advantages of this approach include strategy-proofness and allocative efficiency. Further, in an auction setting, VCG guarantees budget balance, because payments are absorbed by the center. In our setting, however, where payments are redistributed to the agents, some money must be burned in order to retain strategy-proofness. Our key finding is that destroying items can save money, and hence lead to greater social surplus. More specifically, our first observation is that a mechanism is strategy-proof iff it admits a threshold representation. Given this observation, we restrict attention to specific threshold and payment functions for which we can numerically solve for an optimal mechanism. Whereas the worst-case ratio of the realized social surplus to the maximum possible is close to 1 when $m=1$ and 0 when $m=n-1$ under the VCG mechanism, the best mechanism we find coincides with VCG when $m=1$ but has a ratio approaching 1 when $m=n-1$ as $n$ increases.

The Adwords Problem: Online Keyword Matching with Budgeted Bidders under Random Permutations

Nikhil Devanur, Microsoft Research

Thomas Hayes, University of New Mexico

We consider the problem of a search engine trying to assign a sequence of search keywords to a set of competing bidders, each with a daily spending limit. The goal is to maximize the revenue generated by these keyword sales, bearing in mind that, as some bidders may eventually exceed their budget, not all keywords should be sold to the highest bidder. We assume that the sequence of keywords (or equivalently, of bids) is revealed on-line. Our concern will be the competitive ratio for this problem versus the off-line optimum. We extend the current literature on this problem by considering the setting where the keywords arrive in a random order. In this setting we are able to achieve a competitive ratio of $1-\epsilon$ under some mild, but necessary, assumptions. In contrast, it is already known that when the keywords arrive in an adversarial order, the best competitive ratio is bounded away from 1. Our algorithm is motivated by PAC learning, and proceeds in two parts: a training phase, and an exploitation phase.

The Price of Truthfulness for Pay-Per-Click Auctions

Nikhil Devanur, Microsoft Research

Sham Kakade, TTI Chicago

We analyze the problem of designing a truthful pay-per-click auction where the click-through-rates (CTR) of the bidders are unknown to the auction. Such an auction faces the classic explore/exploit dilemma: while gathering information about the click through rates of advertisers, the mechanism may loose revenue; however, this gleaned information may prove valuable in the future for a more profitable allocation. In this sense, such mechanisms are prime candidates to be designed using multi-armed bandit techniques. However, a naive application of multi-armed bandit algorithms would not take into account the \emph{strategic} considerations of the players --- players might manipulate their bids (which determine the auction's revenue) in a way as to maximize their own utility. Hence, we consider the natural restriction that the auction be truthful. The revenue that we could hope to achieve is the expected revenue of a Vickerey auction that knows the true CTRs, and we define the \emph{2nd price regret} to be the difference between the expected revenue of the auction and this Vickerey revenue. This work sharply characterizes what regret is achievable, under a truthful restriction. We show that this truthful restriction imposes \emph{statistical} limits on the achievable regret --- the achievable regret is $\Theta^*(T^{2/3})$, while for traditional bandit algorithms (without the truthful restriction) the achievable regret is $\Theta^*(T^{1/2})$ (where $T$ is the number of rounds). We term the extra $T^{1/6}$ factor, the `price of truthfulness'.

Limited and Online Supply and the Bayesian foundations of prior-free mechanism design

Nikhil Devanur, Microsoft Research

Jason Hartline, Northwestern University

We study auctions for selling a limited supply of a single commodity in the case where the supply is known in advance and the case it is unknown and must be instead allocated in an online fashion. The latter variant was proposed by Madhian and Saberi as a model of an important phenomena in auctions for selling Internet advertising: advertising impressions must be allocated as they arrive and the total quantity available is unknown in advance. We describe the Bayesian optimal mechanism for these variants and extend the random sampling auction of Goldberg et al.~\cite{GHW-01} to address the prior-free case.

A Qualitative Vickrey Auction

Paul Harrenstein, University of Munich

Mathijs de Weerdt, Delft University of Technology

Vincent Conitzer, Duke University

Restricting the preferences of the agents by assuming that their utility functions linearly depend on a payment allows for the positive results of the Vickrey auction and the Vickrey-Clarke-Groves mechanism. Such quasilinear preferences, however, involve the controversial assumption that there is some commonly desired commodity or numeraire---money, shells, beads, etcetera---which is commensurable with utility. We propose a generalization of the Vickrey auction that does not assume that the agents' preferences are quasilinear, but still has some of its desirable properties. In this auction, a bid can be any alternative, rather than just a monetary offer. Such an auction is also applicable to situations where no numeraire is available, where there is a fixed budget, or where money is no issue. We show that in two general settings, this qualitative Vickrey auction has a dominant strategy equilibrium, invariably yields a weakly Pareto efficient outcome in this equilibrium, and is individually rational. The first setting is one where the center has a linear preference order over a finite set of alternatives, and the second setting is when the bidders' preferences can be represented by continuous utility functions over a closed metric space of alternatives and the center's utility is equipeaked. The traditional Vickrey auction turns out to be a special case of this second setting.

Selling Ad Campaigns: Online Algorithms with Cancellations

Moshe Babaioff, Micosoft Research

Jason Hartline, Northwestern Univ.

Robert Kleinberg, Cornell University

We study online pricing problems in markets with cancellations, i.e., markets in which prior allocation decisions can be revoked, but at a cost. In our model, a seller receives requests online and chooses which requests to accept, subject to constraints on the subsets of requests which may be accepted simultaneously. A request, once accepted, can be canceled at a cost which is a fixed fraction of the request value. This scenario models a market for web advertising campaigns, in which the buyback cost represents the cost of cancelling an existing contract. We analyze a simple constant-competitive algorithm for a single-item auction in this model, and we prove that its competitive ratio is optimal among deterministic algorithms, but that the competitive ratio can be improved using a randomized algorithm. We then model ad campaigns using knapsack domains, in which the requests differ in size as well as in value. We give a deterministic online algorithm that achieves a bi-criterion approximation in which both approximation factors approach $1$ as the buyback factor and the size of the maximum request approach $0$. We show that the bi-criterion approximation is unavoidable for deterministic algorithms, but that a randomized algorithm is capable of achieving a constant competitive ratio. Finally, we discuss an extension of our randomized algorithm to matroid domains (in which the sets of simultaneously satisfiable requests constitute the independent sets of a matroid) as well as present results for domains in which the buyback factor of different requests varies.

The Price of Uncertainty

Maria-Florina Balcan, Microsoft Research

Avrim Blum, Carnegie Mellon University

Yishay Mansour, Tel-Aviv University and Google Research

In this work we study the degree to which small fluctuations in costs in well-studied potential games can impact the result of natural best-response and improved-response dynamics. We call this the {\em Price of Uncertainty} and study it in a wide variety of potential games (including fair cost-sharing games, set-cover games, routing games, and job-scheduling games), finding a number of surprising results. In particular, we show that in certain cases, even extremely small fluctuations can cause these dynamics to spin out of control and move to states of much higher social cost, whereas in other cases these dynamics are much more stable even to large degrees of fluctuation. We also consider the resilience of these dynamics to a small number of Byzantine players about which no assumptions are made. We show again a sharp contrast between different games. In certain cases (e.g., fair cost-sharing, set-covering, job-scheduling) even a single Byzantine player can cause best-response dynamics to transition to states of substantially higher cost, whereas in others (e.g., the class of $\beta$-nice games which includes routing, market-sharing and many others) these dynamics are much more resilient.

Network Bargaining: Algorithms and Structural Results

Tanmoy Chakraborty, University of Pennsylvania

Michael Kearns, University of Pennsylvania

Sanjeev Khanna, University of Pennsylvania

We consider the following setting: Players are represented by vertices in a social network, and edges represent bilateral agreements/deals between them. Each deal yields some fixed wealth if its two endpoints can agree on how to share the profit, or else it yields zero profit. Many business transactions can be modeled as such a deal, but the most natural interpretation is a business partnership. Chakraborty and Kearns (WINE 2008) introduced a simple axiomatic model that stipulates an equilibrium concept, where all players are rationally satisfied with their shares. We explore this equilibrium concept in this paper. We describe an FPTAS to compute approximate equilibrium on all bipartite graphs. We show that equilibrium is not unique, but also give some conditions that ensure uniqueness on regular graphs. Finally, we explore the effect of network structure on solutions given by our model, using simulation methods and statistical analysis.

Simple versus Optimal Mechanisms

Jason Hartline, Northwestern U.

Tim Roughgarden, Stanford U.

The monopolist’s theory of optimal single-item auctions for agents with independent private values can be summarized by two statements. The ?rst is from Myerson (1981): the optimal auction is Vickrey with a reserve price. The second is from Bulow and Klemperer (1996): it is better to recruit one more agent and run the Vickrey auction than to run the optimal auction. These results hold for single-item auctions only under the assumption that the agents’ valuations are independently and identically drawn from a distribution that satis?es a natural (and common) regularity condition. These fundamental guarantees for the Vickrey auction fail to hold in general single-parameter agent mechanism design problems. We give precise (and weak) conditions under which approximate analogs of these two results hold, thereby demonstrating that simple mechanisms remain almost optimal in quite general single-parameter agent settings.

An Optimal Lower Bound for Anonymous Scheduling Mechanisms

Itai Ashlagi, Harvard

Shahar Dobzinski, Hebrew University

Ron Lavi, Technion

We consider the problem of designing truthful mechanisms to minimize the makespan on $m$ unrelated machines. In their seminal paper, Nisan and Ronen~\cite{NR99} showed a lower bound of $2$, and an upper bound of $m$, thus leaving a large gap. They conjectured that their upper bound is tight, but were unable to prove it. Despite many attempts that yield positive results for several special cases, the conjecture is far from being solved: the lower bound was only recently slightly increased to 2.61~\cite{CKV07,KV07}, while the best upper bound remained unchanged. In this paper we show the optimal lower bound on truthful anonymous mechanisms: no such mechanism can guarantee an approximation ratio better than $m$. This is the first concrete evidence to the correctness of the Nisan-Ronen conjecture, especially given that the classic scheduling algorithms are anonymous, and all state-of-the-art mechanisms for special cases of the problem are anonymous as well.

The Price of Anarchy in Bertrand Games

Shuchi Chawla, Univ of Wisconsin Madison

Feng Niu, Univ of Wisconsin Madison

The Internet is composed of multiple economically-independent service providers that sell bandwidth in their networks so as to maximize their own revenue. Users, on the other hand, route their traffic selfishly to maximize their own utility. How does this selfishness impact the efficiency of operation of the network? To answer this question we consider a two-stage network pricing game where service providers first select prices to charge on their links, and users then pick paths to route their traffic. We give tight bounds on the price of anarchy of the game with respect to social value—the total value obtained by all the traffic routed. Unlike recent work on network pricing, in our pricing game users do not face congestion costs; instead service providers must ensure that capacity constraints on their links are satis?ed. Our model extends the classic Bertrand game in economics to network settings.

Bayes-Nash Equilibria of the Generalized Second Price

Renato Gomes, Northwestern University

Kane Sweeney, Northwestern University

In this paper we develop a complete Bayes-Nash analysis of the Generalized Second Price (GSP) auction, by far the most widely used mechanism to sell sponsored search positions. Interestingly, our results are in sharp contrast with the previous literature that adopts a complete information framework. We start by studying the efficient Bayes-Nash equilibrium (simply "equilibrium", from now on) of the GSP. After deriving its symmetric bidding strategy, we show that an efficient equilibrium exists if and only if the click-through rates of any two slots are sufficiently further apart. But then, if an efficient equilibrium fails to exist, what should we expect from the GSP? Are there other (possibly) inefficient equilibria? We provide a negative answer to this question; and show that this auction possesses no inefficient pure strategy equilibria. We also study the revenue properties of the GSP when an efficient equilibrium does exist. We prove the counter-intuitive result that revenue may decrease as click-through rates of the second to last slots increase provided the distribution of advertisers' values per click is concentrated on high values. Fortunately, setting the appropriate reserve prices may improve the performance of this mechanism. Indeed, we prove that revenue-maximizing slot-specific reserve prices are uniform across slots and equal their single-unit counterpart (being invariant to click-through rates). Further, with optimal reserve prices, we show that the search engine's revenue increases with the click-through rates of all slots (reversing our previous result on the GSP without reserve prices).

Sybilproof Trust Exchange Protocols

Paul Resnick, University of Michigan

Rahul Sami, University of Michigan

We study protocols to enable an agent (the executor) to make potentially profitable but risky interactions with another agent (the counterparty) in the absence of a common currency or direct trust relationship between the two parties. In such situations, it is possible to enable the investment indirectly through a chain of credit or ``trust'' links. We introduce a novel model of indirect trust exchange protocols that provides insight into many disparate applications, including open currency systems, network trust aggregation systems, and manipulation-resistant recommender systems. In each case, direct interaction between executors and counterparties can lead to an increase in the level of the community's trust capital over time. Allowing indirect trust exchanges opens up more investment opportunities, but also expands the strategy space of an attacker seeking to exploit the community for her own ends. We show that with indirect exchange protocols, some friction is unavoidable: any trust exchange protocol that satisfies a natural strategic safety property that we term sum-sybilproofness can sometimes lead to a reduction in expected overall trust capital even when all interactions are profitable in expectation. Thus, in some circumstances, there will be limits on the percentage of transactions that can be facilitated through indirect rather than direct trust. We present indirect trust exchange protocols that achieve the optimal rate of expected growth in capital, among all protocols satisfying the sum-sybilproofness condition.

Collective Revelation: A Mechanism for Self-Verified, Weighted, and Truthful Predictions

Sharad Goel, Yahoo! Research

Daniel Reeves, Yahoo! Research

David Pennock, Yahoo! Research

Decision makers often benefit from the subjective judgment of experts. For example, estimates of disease prevalence are quite valuable, yet can be difficult to measure objectively. Any mechanism for eliciting and aggregating expert assessments ideally should: (1) incentivize participants to be truthful; (2) adjust for the fact that some experts are better informed than others; and (3) circumvent the need for objective, "ground truth" observations. Subsets of these properties are attainable by previous elicitation methods, including proper scoring rules, prediction markets, and the Bayesian truth serum. Our mechanism of collective revelation, however, is the first to simultaneously achieve all three. Furthermore, we introduce a general technique for constructing budget-balanced mechanisms---where no net payments are made to participants---that applies both to collective revelation and to past peer-prediction methods.

Social Influence and the Diffusion of User-Created Content

Eytan Bakshy, University of Michigan

Brian Karrer, University of Michigan

Lada Adamic, University of Michigan

Social influence determines to a large extent what we adopt and when we adopt it. This is just as true in the digital domain as it is in real life, especially with the proliferation of user generated content that one must first become aware of and then select from. In this paper, we present an empirical study of the diffusion of user-contributed content and directly measure adoption behavior amongst users in a time-evolving social network. We develop techniques for identifying and modeling social influence based on the change in adoption rate following the actions of one's friends. We apply this model to time-resolved social network and user-to-user content transfer data from Second Life, an immersive massively multiplayer virtual world. We find that the social network plays a significant role in the adoption of content. Adoption rates quicken as the number of friends adopting increases and this effect varies with the connectivity of a particular user. We further find that sharing among friends occurs more rapidly than sharing among strangers, but that content that diffuses primarily through social influence tends to have a more limited audience. Finally, we examine the role of individuals, finding that some play a more active role in distributing content than others, but that these influencers are distinct from the early adopters.

Efficiency of (Revenue-)Optimal Mechanisms

Gagan Aggarwal, Google, Inc.

Gagan Goel, Georgia Tech

Aranyak Mehta, Google, Inc.

We compare the expected efficiency of revenue-maximizing (or {\em optimal}) mechanisms with that of efficiency-maximizing ones. We show that the efficiency of the revenue-maximizing mechanism for selling a single item with $(k + \log_{\frac{e}{e-1}}{k} + 1)$ bidders is at least as much as the efficiency of the efficiency-maximizing mechanism with $k$ bidders, when bidder valuations are drawn i.i.d. from a Monotone Hazard Rate distribution. Surprisingly, we also show that this bound is tight within a small additive constant of $4.7$. In other words, $\Theta(\log{k})$ extra bidders suffice for the revenue-maximizing mechanism to match the efficiency of the efficiency-maximizing mechanism, while $o(\log{k})$ do not. This is in contrast to the result of Bulow and Klemperer~\cite{bulow-klemperer} comparing the revenue of the two mechanisms, where only one extra bidder suffices. More precisely, they show that the revenue of the efficiency-maximizing mechanism with $k+1$ bidders is no less than the revenue of the revenue-maximizing mechanism with $k$ bidders. We extend our result for the case of selling $t$ identical items and show that $\Theta(\log{k}) + t \Theta(\log{\log{k}})$ extra bidders suffice for the revenue-maximizing mechanism to match the efficiency of the efficiency-maximizing mechanism. In order to prove our results, we do a classification of Monotone Hazard Rate (MHR) distributions and identify a family of MHR distributions, such that for each class in our classification, there is a member of this family that is pointwise lower than every distribution in that class. This lets us prove interesting structural theorems about distributions with Monotone Hazard Rate.

Optimal Collusion-Resistant Mechanisms with Verification

Paolo Penna, Università di Salerno

Carmine Ventre, University of Liverpool

We present the first general positive result on the construction of collusion-resistant mechanisms, that is, mechanisms that guarantee dominant strategies even when agents can form arbitrary coalitions and exchange compensations (sometimes referred to as transferable utilities or side payments). This is a much stronger solution concept as compared to truthful or even group strategyproof mechanisms, and only impossibility results were known for this type of mechanisms in the "classical" model. We describe collusion-resistant mechanisms with verification that return optimal solutions for a wide class of mechanism design problems (which includes utilitarian ones as a special case). Note that every collusion-resistant mechanism without verification must have an unbounded approximation factor and, in general, optimal solutions cannot be obtained even if we content ourselves with truthful ("non-collusion-resistant") mechanisms. All these results apply to problems that have been extensively studied in the algorithmic mechanism design literature like, for instance, task scheduling and inter-domain routing.

On Representing Coalitional Games with Externalities

Tomasz Michalak, University of Liverpool

Talal Rahwan, University of Southampton

Jacek Sroka, University of Warsaw

Adrew Dowell, University of Liverpool

Micheal Wooldridge, University of Liverpool

Peter McBurney, University of Liverpool

Nicholas Jennings, University of Southampton

We consider the issue of representing coalitional games in multiagent systems with externalities, i.e. in systems where the performance of one coalition may be affected by the functioning of another co-existing coalitions. On top of the conventional partition function game representation (PFG), we propose a number of new representations based on a new notion of externalities. In contrast to conventional game theory, our new concept is not related to the process by which the coalitions are formed, but rather to the effect that each coalition may have on the entire system and vice versa. We show that the new representations are fully expressive and, for many classes of games, more concise than the conventional PFG. Building upon the new representations, we propose a number of approaches to solve the coalition structure generation problem in systems with externalities. We show that, if externalities are characterised by various degrees of regularity, the new representations allow us to adapt some of the algorithms that were originally designed for domains with no externalities so that they can be used when externalities are present. Finally, building upon [16] and [9], we present a unified method to solve the coalition structure generation problem in any system, with or without externalities.

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ReplyDeleteThanks,

Martin Michael Musatov