Person:

Shnayder, Victor

As 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.

We 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.

Peer 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.

We 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.