Christina Aperjis

Peer-assisted content distribution with prices

Peer-assisted content distribution matches user demand for content with available supply at other peers in the network. Inspired by this supply-and-demand interpretation of the nature of content sharing, we employ price theory to study peer-assisted content distribution. The market-clearing prices are those which align supply and demand, and the system is studied through the characterization of price equilibria. We discuss the efficiency and robustness gains of price-based multilateral exchange, and show that simply maintaining a single price per peer (even across multiple files) suffices to achieve these benefits. Our main contribution is a system design---PACE (Price-Assisted Content Exchange)---that effectively and practically realizes multilateral exchange. Its centerpiece is a market-based mechanism for exchanging currency for desired content, with a single, decentralized price per peer. Honest users are completely shielded from any notion of prices, budgeting, allocation, or other market issues, yet strategic or malicious clients cannot unduly damage the systems efficient operation. Our design encourages sharing of desirable content and network-friendly resource utilization. Bilateral barter-based systems such as BitTorrent have been attractive in large part because of their simplicity. Our research takes a significant step in understanding the efficiency and robustness gains possible with multilateral exchange.

A peer-to-peer system as an exchange economy

We formulate a peer-to-peer system for filesharing as an exchange economy: a price is associated with each file, and users exchange files only when they can afford it. This formulation solves the free-riding problem, since uploading files is a necessary condition for being able to download. However, we do not explicitly introduce a currency; users must upload files in order to earn a budget for downloading. We discuss existence, uniqueness, and dynamic stability of the competitive equilibrium, which is always guaranteed to be Pareto efficient. In addition, a novel aspect of our approach is an allocation mechanism for clearing the market out of equilibrium. We analyze this mechanism when users can anticipate how their actions affect the allocation mechanism (price anticipating behavior). For this regime we characterize the Nash equilibria that will occur, and show that as the number of users increases, the Nash equilibrium rates become approximately Pareto efficient.

Collective attention and the dynamics of group deals

We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and formulate a predictive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time.We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the total number of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold which is reached very early. Furthermore, the personal benefit of propagating a deal is greater in LivingSocial.

Bilateral and multilateral exchanges for peer-assisted content distribution

Users of the BitTorrent file-sharing protocol and its variants are incentivized to contribute their upload capacity in a bilateral manner: Downloading is possible in return for uploading to the same user. An alternative is to use multilateral exchange to match user demand for content to available supply at other users in the system. We provide a formal comparison of peer-to-peer system designs based on bilateral exchange with those that enable multilateral exchange via a price-based market mechanism to match supply and demand. First, we compare the two types of exchange in terms of the equilibria that arise. A multilateral equilibrium allocation is Pareto-efficient, while we demonstrate that bilateral equilibrium allocations are not Pareto-efficient in general. We show that Pareto efficiency represents the “gap” between bilateral and multilateral equilibria: A bilateral equilibrium allocation corresponds to a multilateral equilibrium allocation if and only if it is Pareto-efficient. Our proof exploits the fact that Pareto efficiency implies reversibility of an appropriately constructed Markov chain. Second, we compare the two types of exchange through the expected percentage of users that can trade in a large system, assuming a fixed file popularity distribution. Our theoretical results as well as analysis of a BitTorrent dataset provide quantitative insight into regimes where bilateral exchange may perform quite well even though it does not always give rise to Pareto-efficient equilibrium allocations.

Pricing private data

We consider a market where buyers can access unbiased samples of private data by appropriately compensating the individuals to whom the data corresponds (the sellers) according to their privacy attitudes. We show how bundling the buyers’ demand can decrease the price that buyers have to pay per data point, while ensuring that sellers are willing to participate. Our approach leverages the inherently randomized nature of sampling, along with the risk-averse attitude of sellers in order to discover the minimum price at which buyers can obtain unbiased samples. We take a prior-free approach and introduce a mechanism that incentivizes each individual to truthfully report his preferences in terms of different payment schemes. We then show that our mechanism provides optimal price guarantees in several settings.

Rankr: A Mobile System for Crowdsourcing Opinions

Evaluating large sets of items, such as business ideas, is a difficult task. While no one person has time to evaluate all the items, many people can contribute by each evaluating a few. Moreover, given the mobility of people, it is useful to allow them to evaluate items from their mobile devices. We present the design and implementation of a mobile service, Rankr, which provides a lightweight and efficient way to crowdsource the relative ranking of ideas, photos, or priorities through a series of pairwise comparisons. We discover that users prefer viewing two items simultaneously versus viewing one image at a time with better fidelity. Additionally, we developed an algorithm that determines the next most useful pair of candidates a user can evaluate to maximize the information gained while minimizing the number of votes required. Voters do not need to compare and manually rank all of the candidates.

Optimal Windows for Aggregating Ratings in Electronic Marketplaces

Aseller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now whereas honest behavior results in a better reputation---and thus higher payments---in the future. We study the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the sellers ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful but for higher-quality sellers to be less truthful.

Global budgets for local recommendations

We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile setting. The service automates the content search process by taking advantage of geographic and social context, while using crowdsourced expertise to present a personalized feed of targeted information ranked by a novel geo-aware rating and incentive mechanism. Users rate the relevance of recommendations for particular locations using a limited, global voting budget. This budget is, in turn, increased by accurately predicting local content popularity. One of the key goals of our mechanism is to encourage ratings, and in an evaluation of the live system we found that the rating to click ratio was 107 times higher than the ratio for videos on YouTube, 34 times higher than the ratio for applications on the Android Market, and 3 times higher than the ratio for Web pages on Digg. To investigate whether our mechanism also had qualitative effects on the ratings we conducted a number of experiments on Amazon Mechanical Turk, with 500 users, comparing our mechanism to the de-facto 5-star ratings commonly in use on the Web. Our results show that budgets improved the ranking and incentives improved the aggregate rating of a series of location-dependent Web pages.

A Comparison of Bilateral and Multilateral Exchanges for Peer-Assisted Content Distribution

Peer-assisted content distribution matches user demand for content with available supply at other peers in the network. Inspired by this supply-and-demand interpretation of the nature of content sharing, we employ price theory to study peer-assisted content distribution. In this approach, the market-clearing prices are those which exactly align supply and demand, and the system is studied through the characterization of price equilibria. We rigorously analyze the efficiency and robustness gains that are enabled by price-based multilateral exchange. We show that multilateral exchanges satisfy several desirable efficiency and robustness properties that bilateral exchanges do not, e.g. , equilibria in bilateral exchange may fail to exist, be inefficient if they do exist, and fail to remain robust to collusive deviations even if they are Pareto efficient. Further, we show that an equilibrium in bilateral exchange corresponds to a multilateral exchange equilibrium if and only if it is robust to deviations by coalitions of users.

Human Speed-Accuracy Tradeoffs in Search

When foraging for information, users face a tradeoff between the accuracy and value of the acquired information and the time spent collecting it, a problem which also surfaces when seeking answers to a question posed to a large community. We empirically study how people behave when facing these conflicting objectives using data from Yahoo Answers, a community driven question-and-answer site. We first study how users behave when trying to maximize the amount of acquired information while minimizing the waiting time. We find that users are willing to wait longer for an additional answer if they have received a small number of answers. We then assume that users make a sequence of decisions, deciding to wait for an additional answer as long as the quality of the current answer exceeds some threshold. The resulting probability distribution for the number of answers that a question gets is an inverse Gaussian, a fact that is validated by our data.