Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning the maximum grade to all submissions. With a naive grading scheme, such as averaging the assigned grades, all students would receive the maximum grade. In this paper, we develop three grading schemes that provide incentives for truthful peer grading. In the first scheme, the instructor grades a fraction p of the submissions, and penalizes students whose grade deviates from the instructor grade. We provide lower bounds on p to ensure truthfulness, and conclude that these schemes work only for moderate class sizes, up to a few hundred students. To overcome this limitation, we propose a hierarchical extension of this supervised scheme, and we show that it can handle classes of any size with bounded (and little) instructor work, and is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we propose unsupervised incentive schemes, in which the student incentive is based on statistical properties of the grade distribution, without any grading required by the instructor. We show that the proposed unsupervised schemes provide incentives to truthful grading, at the price of being possibly unfair to individual students.
The model presented in @cite_4 relaxes this assumption. The proposed scoring rule evaluates experts by comparing them to each other. The model assigns a higher score for an expert if her predictions are in agreement with predictions of other experts.
@cite_4
1904238289
When a decision maker (DM) contracts with an expert to provide information, the nature of the contract can, create incentives for the expert, and it is up to the DM to ensure that the contract provides incentives that align the expert’s and DM’s interests. In this paper, scoring rules (and related functions) are viewed as such contracts and are reinterpreted in terms of agency theory and the theory of revelation games from economics. Although scoring rules have typically been discussed in the literature as devices for eliciting and evaluating subjective probabilities, this study relies on the fact that strictly proper scoring rules reward greater expertise as well as honest revelation. We describe conditions under which a DM can use a strictly proper scoring rule as a contract to give an expert an incentive to gather an amount of information that is optimal from the DM’s perspective. The conditions we consider focus on the expert’s cost structure, and we find that the DM must have substantial knowledge of that cost structure in order to design a specific contract that provides the correct incentives. The model and analysis suggest arguments for hiring and maintaining experts in-house rather than using outside consultants. In this paper, we compare and analyze the results from automatic analysis and visual scoring of nocturnal sleep recordings. The validation is based on a sleep recording set of 60 subjects (33 males and 27 females), consisting of three groups : 20 normal control subjects, 20 depressed patients and 20 insomniac patients treated with a benzodiazepine. The inter-expert variability estimated from these 60 recordings (61,949 epochs) indicated an average agreement rate of 87.5 between two experts on the basis of 30-second epochs. The automatic scoring system, compared in the same way with one expert, achieved an average agreement rate of 82.3 , without expert supervision. By adding expert supervision for ambiguous and unknown epochs, detected by computation of an uncertainty index and unknown rejection, the automatic expert agreement grew from 82.3 to 90 , with supervision over only 20 of the night. Bearing in mind the composition and the size of the test sample, the automated sleep staging system achieved a satisfactory performance level and may be considered a useful alternative to visual sleep stage scoring for large-scale investigations of human sleep. When eliciting opinions from a group of experts, traditional devices used to promote honest reporting assume that there is an observable future outcome. In practice, however, this assumption is not always reasonable. In this paper, we propose a scoring method built on strictly proper scoring rules to induce honest reporting without assuming observable outcomes. Our method provides scores based on pairwise comparisons between the reports made by each pair of experts in the group. For ease of exposition, we introduce our scoring method by illustrating its application to the peer-review process. In order to do so, we start by modeling the peer-review process using a Bayesian model where the uncertainty regarding the quality of the manuscript is taken into account. Thereafter, we introduce our scoring method to evaluate the reported reviews. Under the assumptions that reviewers are Bayesian decision-makers and that they cannot influence the reviews of other reviewers, we show that risk-neutral reviewers strictly maximize their expected scores by honestly disclosing their reviews. We also show how the group's scores can be used to find a consensual review. Experimental results show that encouraging honest reporting through the proposed scoring method creates more accurate reviews than the traditional peer-review process. A large number of very popular team sports involve the act of one team trying to score a goal against the other. During this game play, defending players constantly try to predict the next move of the attackers to prevent them from scoring, whereas attackers constantly try to predict the next move of the defenders in order to defy them and score. Such behavior is a prime example of the general human faculty to make predictions about the future and is an important facet of human intelligence. An algorithmic solution to learning a model of the external world from sensory inputs in order to make forecasts is an important unsolved problem. In this work we develop a generic framework for forecasting future events in team sports videos directly from visual inputs. We introduce water polo and basketball datasets towards this end and compare the predictions of the proposed methods against expert and non-expert humans.