4 research outputs found
Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge
We study minimal single-task peer prediction mechanisms that have limited
knowledge about agents' beliefs. Without knowing what agents' beliefs are or
eliciting additional information, it is not possible to design a truthful
mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore
equilibrium strategy profiles that are only partially truthful. Using the
results from the multi-armed bandit literature, we give a characterization of
how inefficient these equilibria are comparing to truthful reporting. We
measure the inefficiency of such strategies by counting the number of dishonest
reports that any minimal knowledge-bounded mechanism must have. We show that
the order of this number is , where is the number of
agents, and we provide a peer prediction mechanism that achieves this bound in
expectation
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Measuring Performance of Peer Prediction Mechanisms Using Replicator Dynamics
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.Engineering and Applied Science
Elicitation and Aggregation of Crowd Information
This thesis addresses challenges in elicitation and aggregation of crowd information for settings where an information collector, called center, has a limited knowledge about information providers, called agents. Each agent is assumed to have noisy private information that brings a high information gain to the center when it is aggregated with the private information of other agents. We address two particular issues in eliciting crowd information: 1) how to incentivize agents to participate and provide accurate data; 2) how to aggregate crowd information so that the negative impact of agents who provide low quality information is bounded. We examine three different information elicitation settings. In the first elicitation setting, agents report their observations regarding a single phenomenon that represents an abstraction of a crowdsourcing task. The center itself does not observe the phenomenon, so it rewards agents by comparing their reports. Clearly, a rational agent bases her reporting strategy on what she believes about other agents, called peers. We prove that, in general, no payment mechanism can achieve strict properness (i.e., adopt truthful reporting as a strict equilibrium strategy) if agents only report their observations, even if they share a common belief system. This motivates the use of payment mechanisms that are based on an additional report. We show that a general payment mechanism cannot have a simple structure, often adopted by prior work, and that in the limit case, when observations can take real values, agents are constrained to share a common belief system. Furthermore, we develop several payment mechanisms for the elicitation of non-binary observations. In the second elicitation setting, a group of agents observes multiple a priori similar phenomena. Due to the a priori similarity condition, the setting represents a refinement of the former setting and enables one to achieve stronger incentive properties without requiring additional reports or constraining agents to share a common belief system. We extend the existing mechanisms to allow non-binary observations by constructing strongly truthful mechanisms (i.e., mechanisms in which truthful reporting is the highest-paying equilibrium) for different types of agents' population. In the third elicitation setting, agents observe a time evolving phenomenon, and a few of them, whose identity is known, are trusted to report truthful observations. The existence of trusted agents makes this setting much more stringent than the previous ones. We show that, in the context of online information aggregation, one can not only incentivize agents to provide informative reports, but also limit the effectiveness of malicious agents who deliberately misreport. To do so, we construct a reputation system that puts a bound on the negative impact that any misreporting strategy can have on the learned aggregate. Finally, we experimentally verify the effectiveness of novel elicitation mechanisms in community sensing simulation testbeds and a peer grading experiment