Person: Shnayder, Victor
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Shnayder
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Shnayder, Victor
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Publication Truthful prioritization for dynamic bandwidth sharing(ACM, 2014) Shnayder, Victor; Kawadia, Vikas; Hoon, Jeremy; Parkes, DavidWe design a protocol for dynamic prioritization of data on shared routers such as untethered 3G/4G devices. The mechanism prioritizes bandwidth in favor of users with the highest value, and is incentive compatible, so that users can simply report their true values for network access. A revenue pooling mechanism also aligns incentives for sellers, so that they will choose to use prioritization methods that retain the incentive properties on the buy-side. In this way, the design allows for an open architecture. In addition to revenue pooling, the technical contribution is to identify a class of stochastic demand models and a prioritization scheme that provides allocation monotonicity. Simulation results confirm efficiency gains from dynamic prioritization relative to prior methods, as well as the effectiveness of revenue pooling.Publication Making Peer Prediction Practical(2016-07-13) Shnayder, Victor; Parkes, David C.; Chen, Yiling; Morrisett, GregMy dissertation is on crowdsourcing---using crowds of people to accomplish tasks that are impractical or far more expensive otherwise. I focus specifically on crowdsourcing of information, where workers do tasks such as analyze images, translate sentences, report whether a cafe has public wi-fi, or assess the writing quality of essays. To encourage participation, workers can be paid, or given non-monetary rewards. In many applications, it is difficult to assess whether responses from a large crowd are accurate, and this can tempt workers into submitting nonsense, allowing them to complete tasks faster and get higher rewards. There are a number of ways to detect this and encourage worker effort and accurate reporting; I apply a technique called peer prediction, which rewards workers based on patterns of agreement among their reports. I am particularly motivated by the challenge of providing education at scale: how to enable billions of people to learn what they want, at a cost even the very poor can afford. Specifically, I study peer assessment of open-ended assignments as a way to scale human feedback. I treat this as a crowdsourcing problem, and study how peer prediction can encourage effort and accurate assessment when students give feedback to their peers. Previous work in peer prediction has highlighted the need for reward mechanisms where exerting effort and reporting truthfully is better for workers than other reporting strategies. I make three main contributions: I present a new Correlated Agreement mechanism for peer prediction in multi-signal environments, that guarantees that uninformed reporting is less attractive than being truthful. I show that replicator dynamics is a useful tool to analyze the likelihood of truthful behavior and its stability when workers are not assumed to be fully rational, and learn from experience instead. Finally, I analyze a dataset of three million peer assessments from online courses on the edX platform, studying several challenges for using peer prediction for peer assessment in education: reward variability, reward magnitude, and low-effort reporting. I compare several peer prediction mechanisms, and conclude that peer prediction is a promising technique in this domain when combined with other efforts to improve feedback quality.Publication Sensor Networks for Medical Care(2005) Shnayder, Victor; Chen, Bor-rong; Lorincz, Konrad; Fulford-Jones, Thaddeus R. F.; Welsh, MattSensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and data rate.Publication Practical Peer Prediction for Peer Assessment(AAAI, 2016) Shnayder, Victor; Parkes, DavidWe provide an empirical analysis of peer prediction mechanisms, which reward participants for information in settings when there is no ground truth against which to score reports. We simulate the mechanisms on a dataset of three million peer assessments from the edX MOOC platform. We evaluate different mechanisms on score variability, which is connected to fairness, risk aversion, and participant learning. We also assess the magnitude of the incentives to invest effort, and study the effect of participant coordination on low-information signals. We find that the correlated agreement mechanism has lower variation in reward than other mechanisms. A concern is that the gain from exerting effort is relatively low across all mechanisms, due to frequent disagreement between peers. Our conclusions are relevant for crowdsourcing in education as well as other domains.Publication Measuring Performance of Peer Prediction Mechanisms Using Replicator Dynamics(2016) Shnayder, Victor; Frongillo, Rafael; Parkes, DavidPeer prediction is the problem of eliciting private, but correlated, information from agents. By rewarding an agent for the amount that their report "predicts" that of another agent, mechanisms can promote effort and truthful reports. A common concern in peer prediction is the multiplicity of equilibria, perhaps including high-payoff equilibria that reveal no information. Rather than assume agents counter-speculate and compute an equilibrium, we adopt replicator dynamics as a model for population learning. We take the size of the basin of attraction of the truthful equilibrium as a proxy for the robustness of truthful play. We study different mechanism designs, using models estimated from real peer evaluations in several massive on-line courses. Among other observations, we confirm that recent mechanisms present a significant improvement in robustness over earlier approaches.Publication Decision Markets with Good Incentives(Springer Verlag, 2011) Chen, Yiling; Kash, I; Ruberry, Michael Edward; Shnayder, VictorDecision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are – like prediction markets using strictly proper scoring rules – myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision maker’s worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.Publication Truthful Prioritization Schemes for Spectrum Sharing(IEEE, 2012) Shnayder, Victor; Hoon, Jeremy; Parkes, David; Kawadia, VikasAs the rapid expansion of smart phones and associated data-intensive applications continues, we expect to see renewed interest in dynamic prioritization schemes as a way to increase the total utility of a heterogeneous user base, with each user experiencing variable demand and value for access. We adapt a recent sampled-based mechanism for resource allocation to this setting, which is more effective in aligning incentives in a setting with variable demand than an earlier method for pricing network resources due to Varian and Mackie-Mason (1994). Complementing our theoretical analysis, which also considers incentives on the sell-side of the market, we present the results of a simulation study, confirming the effectiveness of our protocol in aligning incentives and boosting welfare.